tag:blogger.com,1999:blog-64683183573289511732024-03-12T17:23:24.676-07:00insurance planetinsurance planetUnknownnoreply@blogger.comBlogger9125tag:blogger.com,1999:blog-6468318357328951173.post-59704470615378704462015-08-09T13:16:00.001-07:002015-08-09T15:42:32.750-07:00Catastrophe Derivatives and ILWs<div dir="ltr" style="text-align: left;" trbidi="on">
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<h2 style="text-align: left;">
INDEX-LINKED CONTRACTS</h2>
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Traditional insurance and reinsurance contracts are based purely on direct<br />
indemnification of the insured or reinsured for the losses suffered. Another<br />
way to transfer insurance risk, which is particularly important in its transfer<br />
to the capital markets, is to link the payments to a certain value of an index<br />
as opposed to basing it only on the reimbursement of the actual losses<br />
suffered by a specific entity.<br />
<br />
An example of such an index would be that of<br />
the level of losses suffered from a hurricane in a particular region by the<br />
whole insurance industry. Another example would be a purely parametric<br />
one based on the intensity of a specified catastrophic event without referencing<br />
actual insured losses.<br />
<div class="separator" style="clear: both; text-align: center;">
</div>
<br />
The two main types of insurance-linked securities whose payout depends<br />
on an index value are insurance derivatives and industry loss warranties.<br />
Industry loss warranties (ILWs) and catastrophe derivatives (a subset of<br />
insurance derivatives) were the first insurance-linked securities to appear.<br />
ILWs were first introduced in the 1980s and at the time they were often<br />
referred to as original loss warranties (OLWs) or original market loss<br />
warranties.<br />
<br />
The first catastrophe derivative contracts were developed in<br />
1992 by the Chicago Board of Trade (CBOT). Both types of contract have<br />
since evolved; their markets have evolved as well. ILWs in particular are<br />
now playing an important role in the transfer of catastrophe risk from insurance<br />
to capital markets.<br />
<br />
The use of an index as a reference offers the transparency and lack of<br />
moral hazard that are so important to investors. The ease of standardisation<br />
is also important. One of the key advantages, not yet fully realised, is the<br />
liquidity and price discovery that come with exchange-traded products such<br />
as catastrophe derivatives.<br />
<br />
This article provides an overview of ILWs and catastrophe derivatives<br />
and explains the considerations used in their analysis by investors and<br />
insurers. It then describes the standard indexes used in structuring these<br />
securities and gives some specific examples.<br />
<br />
The focus is on property insurance<br />
risk transfer; insurance derivatives linked to mortality and longevity<br />
are explained in the chapters dealing with mortality and longevity risk<br />
trading, while weather derivatives are discussed in Chapter 8. Finally, the<br />
present chapter examines the trends in the market for ILWs and catastrophe<br />
derivatives and the expectations for its growth and evolution.<br />
<br />
<h3 style="text-align: left;">
ROLE OF AN INDEX</h3>
Index-linked investments are common in the world of capital markets. The<br />
indexes used in insurance and reinsurance risk analysis are typically related<br />
to the level of insurance losses; these are not investable indexes and neither<br />
are their components. A derivative contract can still be structured based on<br />
such an index, but the underlying of the derivative contract is not a tradable<br />
asset.<br />
<br />
In the transfer of insurance risk, an index is chosen in such a way that<br />
there is a direct relationship between the value of the index and the insurance<br />
losses suffered. There is, however, a difference between the two: the<br />
basis risk. This risk is not present when a standard reinsurance mechanism<br />
is utilised.<br />
<br />
While index-linked products are used primarily for the transfer of true<br />
catastrophe risk, there is a growing trend of transferring higher-frequency<br />
(and lower-severity) risk to the capital markets. The indexes used do not<br />
necessarily have to track only catastrophic events.<br />
<br />
<h3 style="text-align: left;">
CATASTROPHE DERIVATIVES DEFINED</h3>
In financial markets, a derivative is a contract between two parties the value<br />
of which is dependent on the value of another financial instrument known<br />
as an underlying asset (usually referred to simply as an underlying). A<br />
derivative may have more than one underlying. In the broader sense, the<br />
underlying does not have to be an asset or a function of an asset.<br />
<br />
Catastrophe derivatives are such contracts, with an underlying being an<br />
index reflecting the severity of catastrophic events or their impact on insurance<br />
losses.<br />
<br />
Futures are an example of derivative instruments. Catastrophe futures are<br />
standardised exchange-traded contracts to pay or receive payments at a<br />
specified time, with the value of the payment being a function of the value of an index. Unlike the case of traditional financial futures, physical delivery<br />
of a commodity or other asset never takes place.<br />
<br />
Options are another example of financial derivatives; they involve the right to buy (call option)<br />
or sell (put option) an underlying asset at a predetermined price (strike). In<br />
the context of catastrophe derivatives, of particular importance are call<br />
spreads, which are the combination of buying a call at a certain strike price<br />
and selling a call on the same underlying at a higher strike, with the same<br />
expiration date.<br />
<br />
The calls can be on catastrophe futures. Using a call spread<br />
limits the amount of potential payout, making the contract somewhat<br />
similar to reinsurance, where each protection layer has its own coverage<br />
limit.<br />
<br />
Binary options provide for either a fixed payment at expiration or,<br />
depending on the value of the underlying, no payment at all. In other words,<br />
there are only two possible outcomes. They are also referred to as digital<br />
options.<br />
<br />
There are numerous ways that catastrophe derivatives can be structured.<br />
The payout may depend on a hurricane of specific magnitude making a<br />
landfall in a certain area; on the value of total cumulative losses from hurricanes<br />
to the insurance industry over a certain period of time for a specified<br />
geographical region; or on the value of an index tracking the severity of an<br />
earthquake at several locations.<br />
<br />
The flexibility in structuring an over-thecounter<br />
(OTC) derivative allows hedgers to minimise their basis risk. At the<br />
same time, there are significant advantages to using standard instruments<br />
that can be traded on an exchange.<br />
<br />
Exchange-traded derivatives are more<br />
liquid, allow for quicker and cheaper execution, provide an effective mechanism<br />
for managing credit risk and bring price transparency to the market,<br />
all of which are essential for market growth.<br />
<br />
<h3 style="text-align: left;">
Derivatives versus reinsurance</h3>
All insurance and reinsurance contracts may be seen as derivatives, albeit<br />
not recognised as such by accounting rules. Technically, they would be call<br />
spreads, which corresponds to policy limits in insurance. From the point of<br />
view of the party being paid for assuming the risk, an excess-of-loss reinsurance<br />
contract can be seen as being equivalent to selling a call with the<br />
strike at the attachment point and buying a call with the strike equal to the<br />
sum of the attachment point and the policy limit. The “underlying” in this<br />
case is the level of insurance losses.<br />
<br />
The true derivatives such as insurance catastrophe derivatives have a<br />
better defined and stable underlying and are accounted for as financial derivative products. Insurance accounting is not allowed for these products.<br />
This topic will be revisited later in the article.<br />
<br />
<h3 style="text-align: left;">
INDUSTRY LOSS WARRANTIES DEFINED</h3>
The term “industry loss warranty” (ILW) has been used to describe two<br />
types of contract, one of them a derivative and the other a reinsurance<br />
contract. In its most common form, an ILW is a double-trigger reinsurance<br />
contract. Both trigger levels have to be exceeded for the contract to pay. The<br />
first is the standard indemnity trigger of the reinsured suffering an insured<br />
loss at a certain level, that is, the ultimate net loss (UNL) trigger.<br />
<br />
The second is that of industry losses or some other index level being exceeded. The<br />
index of industry losses can be, for example, the one determined by the<br />
Property Claim Services (PCS) unit of Insurance Services Office, Inc. (ISO).<br />
An ILW in a pure derivative form is a derivative contract with the payout<br />
dependent only on the industry-based or some other trigger as opposed to<br />
the actual insurance losses of the hedger purchasing the protection. Even<br />
though labelled an ILW, it is really an OTC derivative such as the products<br />
described above.<br />
<br />
The choice between the ILW reinsurance and derivative forms of protection<br />
has significant accounting implications for the hedger. It is typically<br />
beneficial for the hedger to choose a contract that can be accounted for as<br />
reinsurance, with all the associated advantages. This is why the vast<br />
majority of ILW transactions are done in the form of reinsurance.<br />
<br />
The majority of ILWs have a binary payout, and the full amount is paid<br />
once the index-based trigger has been activated. (We assume that the UNL<br />
trigger condition, if present, has been met.) However, some ILW contracts<br />
have non-binary, linear payouts that depend on the level of the index above<br />
the triggering level. There seems to be general market growth in all of these<br />
categories.<br />
<br />
<h3 style="text-align: left;">
MARKET SIZE</h3>
While the size of the catastrophe bond market is known, it is difficult to estimate<br />
the volume of the industry loss warranty and catastrophe derivative<br />
market. The OTC transactions are rarely disclosed, leading to a wide range<br />
of estimates of market size. The only part of the market with readily available<br />
data is that of exchange-traded catastrophe derivatives. The exchanges<br />
report the open interest on each of their products.<br />
<br />
While its size is not very big (with no estimates exceeding US$10 billion<br />
in limits), this market is important as a barometer of reinsurance rates and their movements. Exchange-traded products bring price transparency to the<br />
traditionally secretive reinsurance market. The growing activity of ILW<br />
brokers is leading to increased transparency in the OTC markets as well.<br />
While not directly comparable to traditional reinsurance contracts, catastrophe<br />
derivatives and ILWs provide an important reference point in<br />
pricing reinsurance protection.<br />
<br />
It is likely that in terms of total limits, the ILW and catastrophe derivative<br />
market is between US$5 and US$10 billion. This number does not include<br />
catastrophe and other insurance derivatives linked to mortality and<br />
longevity; only property and casualty insurance risks are included.<br />
<br />
The market has been growing, but the growth has not been steady. Similar to the<br />
retro market (of which some consider this market a part), its size is particularly<br />
prone to fluctuations based on the rate levels in the traditional<br />
reinsurance market. The one part of the market that we can see growing is<br />
that of exchange-traded insurance derivatives. However, exchange-traded<br />
products are currently a relatively small part of the overall marketplace.<br />
<br />
<h3 style="text-align: left;">
KEY INDEXES</h3>
A number of indexes have been used in structuring insurance derivatives<br />
and ILW transactions. They include indexes tied directly to insurance losses<br />
and those tied to physical parameters of events that affect insurance losses.<br />
The overview below focuses on the indexes providing the most credible<br />
information on the level of insured industry-level property losses due to<br />
natural catastrophes.<br />
<br />
<h3 style="text-align: left;">
Property Claim Services</h3>
PCS, a unit of ISO, collects, estimates and reports data on insured losses<br />
from catastrophic events in the US, Puerto Rico and the US Virgin Islands.<br />
While every single provider of catastrophe-insured loss data in the world<br />
has at times been criticised for supposed inaccuracies or delays in reporting,<br />
PCS is generally believed to be the most reliable and accurate.<br />
<br />
In the half a century since it was established, the organisation has developed sound<br />
procedures for data collection and loss estimation. It has the ability to collect,<br />
on a confidential basis, data from a very large number of insurance carriers<br />
as well as from residual market vehicles such as joint underwriting associations.<br />
Other data sources are used as well. Insurance coverage limits, coinsurance, deductible amounts and other factors are taken into account by PCS in estimating insured losses. Estimates are provided for every catastrophe which is defined by PCS as an event that causes US$25 million or more in direct insured property losses and affects a significant number of<br />
policyholders and insurers. Data for both personal and commercial lines of<br />
business is included.<br />
<br />
Loss estimates are usually reported within two weeks of the occurrence of<br />
a PCS-designated catastrophe (and PCS provides the event with a serial<br />
number). For events with likely total insured property loss in excess of<br />
US$250 million, PCS conducts re-surveys and reports their results approximately<br />
every 60 days until it believes that the estimate reasonably reflects<br />
insured industry loss.<br />
<br />
These larger events are the ones of interest for catastrophe<br />
derivatives and ILWs. Figure 5.3 shows an example of PCS loss<br />
estimates for Hurricane Ike at various time points, in reference to the settlement<br />
prices for two of the exchange-traded catastrophe derivatives that use<br />
PCS-based triggers.<br />
<br />
While general catastrophe loss data is available dating back to the establishment<br />
of PCS in 1949, the more detailed data by geographic territory and<br />
insurance business line is available for only the more recent years.<br />
In Table 5.1, opposite, we can see the development of industry-insured<br />
loss estimates for the largest catastrophic events since 2001.<br />
<br />
The time between the occurrence of a catastrophic event and reporting of the final<br />
estimate could vary significantly depending on the event and complexity of<br />
the data collection and extrapolation. Of the events shown in Table 5.1,<br />
Hurricane Katrina had 10 re-survey estimates issued, with the last one<br />
almost two years after the event occurrence.<br />
<br />
However, the changes over the year preceding the reporting of the final estimate were minuscule. The 2008 Hurricane Gustav had the final estimate issued in less than five months,<br />
with that final number not changing from the first re-survey estimate.<br />
<br />
Insured loss estimates for catastrophes that happened before those shown<br />
in Table 5.1 often lacked precision, even though they did not take longer to<br />
obtain. For the 1994 Northridge earthquake in California, the preliminary<br />
estimate increased 80% in two months, and the final estimate was five times greater than the original number.<br />
<br />
However, we have to recognise the fact<br />
that the methodologies employed by PCS have been changing; current estimation<br />
techniques are more reliable given the possibly disproportionate<br />
focus on the actual reported numbers years ago.<br />
<br />
Catastrophe loss indexes based on PCS data are the basis for many ILW<br />
and catastrophe derivative transactions, as well as for catastrophe bonds<br />
and other insurance-linked securities. Both single-event and cumulative<br />
catastrophe loss triggers can be based on PCS indexes.<br />
<br />
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<br />
<h3 style="text-align: left;">
</h3>
<h3 style="text-align: left;">
Perils</h3>
Incorporated in 2009, PERILS AG was created to provide information on<br />
industry-insured losses for catastrophic events in Europe, similar to the way<br />
PCS provides information in the US. The plans call for ultimate expansion<br />
of catastrophe data reporting beyond Europe to other regions outside the<br />
US.<br />
<br />
The shareholders of the company are major insurance and reinsurance<br />
companies and a reinsurance intermediary, ensuring that a large segment of<br />
catastrophe loss data will be provided to PERILS. The information is<br />
provided anonymously by insurance companies and includes exposure data<br />
(expressed as sums insured) by CRESTA zone and by country, property<br />
premium data by country, and catastrophic event loss data by CRESTA zone<br />
and by country.<br />
<br />
The data is aggregated and extrapolated to the whole insurance<br />
industry based primarily on known premium volumes. Industry<br />
exposure and catastrophe loss data are examined for reasonableness and<br />
tested against information from other sources. The methodology is still<br />
evolving.<br />
<br />
In December 2009, PERILS launched an industry loss index service for<br />
European windstorm catastrophic events. The data can be used for industry<br />
loss warranties (ILW) and broader insurance-linked securities (ILS) transactions<br />
involving the use of industry losses as a trigger. Table 5.2 provides a<br />
description of the PERILS indexes for ILS transactions.<br />
<br />
ILW reinsurance transactions based on a PERILS catastrophe loss index<br />
have been done shortly after the introduction of the indexes. The scope and<br />
number of the indexes are expected to grow. The data collected by PERILS<br />
will allow the company to create customised indexes for bespoke transactions.<br />
The reporting is done in euros as opposed to US dollars.<br />
<div class="separator" style="clear: both; text-align: center;">
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<br />
<br />
<h3 style="text-align: left;">
Swiss Re and Munich Re indexes</h3>
The two largest reinsurance companies, Swiss Re and Munich Re, have been<br />
compiling industry loss estimates for catastrophic events for decades. Swiss<br />
Re’s sigma, in particular, has been compiling very reliable loss estimates for<br />
catastrophe events worldwide, including manmade catastrophes. Munich<br />
Re has assembled a very large inventory of catastrophic events in its<br />
NatCatSERVICE loss database. It is similar to Swiss Re’s sigma in its broad<br />
scope but does not include manmade catastrophes. Economic losses from<br />
catastrophic events are often estimated in addition to the insured losses.<br />
ILW transactions have been performed based on both Swiss Re’s sigma and<br />
Munich Re’s NatCatSERVICE.<br />
<br />
It is likely that for the windstorm peril Swiss Re’s and Munich Re’s estimates<br />
are not going to be used for ILS transactions, since PERILS provides a<br />
credible independent alternative. Other perils, and other regions around the<br />
world usually do not have such an alternative, and it is likely that Swiss Re<br />
and Munich Re indexes will continue to be used in structuring ILW and<br />
other transactions. This practice may change in the future if PERILS implements<br />
its ambitious expansion plans.<br />
<br />
<h3 style="text-align: left;">
CME hurricane index</h3>
This index has been developed specifically to facilitate catastrophe derivative<br />
trading. The index, based purely on the physical characteristics of a<br />
hurricane event, aims to provide a measure of insured losses without the use<br />
of any actual loss data such as reported industry losses. While the index has been developed<br />
for North Atlantic hurricanes, in theory the same or a similar approach can<br />
be used for cyclone events elsewhere.<br />
<br />
<h3 style="text-align: left;">
Mortality and longevity indexes</h3>
A number of indexes tracking population mortality or longevity have been<br />
developed for the express purpose of structuring derivative transactions.<br />
These indexes are usually based on general population mortality as opposed<br />
to that of the insured segment of the population. They can be used for<br />
managing the risk of catastrophic mortality jumps affecting insurance<br />
companies, or the longevity risk affecting pension funds, annuity product<br />
providers and governments.<br />
<br />
The CME hurricane index (CHI) was originally developed by reinsurance<br />
broker Carvill and is still usually referred to as the Carvill index. CME Group<br />
currently owns all rights to it.<br />
<br />
The standard Saffir–Simpson hurricane scale is discrete and provides<br />
only five values (from 1 to 5) based on hurricane sustained speed. Having<br />
only five values can be seen as lacking in precision required for more accurate<br />
estimation of potential losses. In addition, the Saffir–Simpson scale<br />
does not differentiate between hurricanes of different sizes as measured by<br />
the radius of the hurricane. Hurricane size can have a significant effect on<br />
the resultant insurance losses. CHI attempts to improve on the<br />
Saffir–Simpson scale by providing a continuous (as opposed to discrete)<br />
measure of sustained wind speeds and by incorporating the hurricane size<br />
in the calculation. The following formula is used for calculating CHI<br />
<br />
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V here is the maximum sustained wind speed, while R is the distance that<br />
hurricane-force winds extend from the centre of the hurricane. The denominators<br />
in the ratios are the reference values. V0 is equal to 74 m.p.h., which<br />
is the threshold between a tropical storm and a hurricane as defined by the<br />
Saffir–Simpson scale used by the National Oceanic and Atmospheric<br />
Administration (NOAA) of the US Department of Commerce. The index is<br />
used only for hurricane-force wind speeds, that is, for V equal to or greater<br />
than 74 m.p.h. R0 is equal to 60 miles, which is a somewhat arbitrarily<br />
chosen value intended to represent the radius of an average hurricane in the North Atlantic.<br />
<br />
EQECAT is the current official calculation agent of the CHI for CME<br />
Group. In calculating the value of the index used for contract settlement,<br />
EQECAT utilises official data from NOAA. If some of the data is missing,<br />
which would likely involve the radius of hurricane-force winds, EQECAT is<br />
to use its best efforts to estimate the missing values. There are additional<br />
rules governing the determination of which of the public advisories (from<br />
NOAA) is to be used, what constitutes a hurricane landfall, and how<br />
multiple landfalls of the same hurricane are treated.<br />
<br />
There is also an index tracking mortality of a specific group of individuals<br />
who have settled their life insurance policies, as opposed to the mortality of<br />
the general population. Life-settlement mortality tracked by such an index<br />
is very different from and not to be confused with mortality of the insured<br />
segment of the population.<br />
<br />
This article focuses on non-life insurance derivatives and ILWs.<br />
Mortality and longevity indexes and the insurance derivative products<br />
based on them are described in detail in the chapters dealing with securitised<br />
life insurance risk and the hedging of longevity risk.<br />
<br />
<br />
<h3 style="text-align: left;">
MODELLING INDUSTRY LOSSES</h3>
Modelling losses for the whole industry is performed using the tools that are<br />
used for modelling losses for a portfolio of risks. Industry loss estimates are<br />
significantly more stable than those of underwriting portfolios of individual<br />
insurance companies. Data such as premium volume provides additional<br />
information that assists in making better predictions.<br />
<br />
In addition, using probabilistic estimates of industry losses is a natural way of comparing<br />
different modelling tools. An outlier would be quickly noticed and need to<br />
be explained. Expected annual losses for peak hazards produced by<br />
different modelling tools do not significantly diverge. The overall probability<br />
distributions, however, can differ considerably.<br />
<br />
As an example, the following table shows estimated probabilities of insurance<br />
industry losses, as would be calculated by PCS, from a single catastrophic<br />
event exceeding a certain level that is used as trigger for catastrophe<br />
<br />
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<br />
derivatives and industry loss warranties. The probabilities do not correspond<br />
directly to the results of any of the standard catastrophe models. The<br />
assumption based on significantly heightened hurricane activity and warm<br />
sea surface temperature is used instead of utilising the entire historical event<br />
catalogue. This explains the higher than usually assumed probabilities of<br />
exceedance.<br />
<br />
<h3 style="text-align: left;">
THE ILW MARKET</h3>
The ILW market is very similar to the traditional reinsurance market in that<br />
it is facilitated, almost exclusively, by reinsurance brokers. The three largest<br />
reinsurance brokers, Aon Re, Guy Carpenter and Willis Re, account for<br />
almost all of the market volume. There are several small brokers that participate<br />
in the ILW market, but their share is small. Investment banks, despite<br />
their role in ILS markets in general, have limited involvement in ILWs.<br />
<br />
The vast majority of ILWs provide protection against standard risks of<br />
wind damage and earthquakes in the US, wind in Europe and earthquakes<br />
in Japan. All natural perils coverage for all of these territories is also<br />
common. The US territory can be split into several pieces, of which Florida<br />
has the most significant exposure to hurricane risk. In addition, second- and<br />
third-event contracts are often quoted.<br />
<br />
For these perils, in the US the standard<br />
index is PCS losses, with trigger points ranging from as low as US$5<br />
billion in industry losses to as high as US$120 billion or even greater to<br />
provide protection against truly catastrophic losses.<br />
<br />
Figure 5.1, opposite, illustrates indicative pricing for 12-month ILWs<br />
covering the wind and flood risk in all of the US. The prices, expressed as a<br />
percentage of the limit, are shown for first-event contracts at four trigger<br />
levels: US$20 billion, US$30 billion, US$40 billion and US$50 billion. The<br />
trigger levels are chosen to correspond to those used later in the chapter in<br />
the illustration of price levels for the IFEX contracts covering substantially<br />
the same catastrophe events.<br />
<br />
The prices can be seen to fluctuate dramatically depending on the market<br />
conditions. The highest levels were achieved following the Katrina–Rita–<br />
Wilma hurricane season of 2005. Another spike followed the 2008 hurricane<br />
losses combined with the capital depletion due to the financial crisis. The<br />
expectations of even higher rates immediately before the hurricane season<br />
of 2009, however, did not materialise.<br />
<br />
<h3 style="text-align: left;">
Structuring an ILW</h3>
Industry loss warranties have become largely standardised in terms of their<br />
typical provisions and legal documentation. A common ILW agreement will<br />
<br />
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<br />
be structured to provide protection in case of catastrophic losses due to a<br />
natural catastrophe such as a hurricane or an earthquake.<br />
The first step will be deciding on the appropriate index, which in the US<br />
can be a PCS index. Once the index is chosen, the attachment point has to be<br />
determined, as well as the protection limit.<br />
<br />
As the value of losses from a<br />
catastrophic event is not immediately known and an organisation such as<br />
PCS will need time to provide a reliable estimate, a reporting period needs<br />
to be specified to allow for loss development. This period can be, for<br />
example, 24 months from the date of the loss or 18 months from end of the<br />
risk term.<br />
<br />
The contract risk term is generally 12 months or shorter. Some<br />
ILWs provide protection only during the hurricane season. For earthquake<br />
protection, the 12-month term is standard. Multi-year contracts are rare.<br />
As an example of the legal language in a contract providing protection<br />
against catastrophic losses due to an earthquake, the contract might “indemnify<br />
the Reinsured for all losses, arising from earthquake and fire following<br />
such earthquake, in respect of all policies and/or contracts of insurance<br />
and/or reinsurance, including workers’ compensation business written or<br />
assumed by the Reinsured, occurring within the territorial scope hereon.<br />
<br />
This Reinsurance is to pay in the event of an Insured Market Loss for property<br />
business arising out of the same event being equal to or greater than US$20 billion (a ‘Qualifying Event’). For purposes of determining the<br />
Insured Market Loss, the parties hereto shall rely on the figures published<br />
by the Property Claim Services (PCS) unit of the Insurance Services Office.”<br />
The US$20 billion is specified as an example of the trigger level.<br />
<br />
The limits can be specified in the manner typical of an excess-of-loss reinsurance<br />
contract, with the possible contract language stipulating that the<br />
reinsured will be paid up to a certain US dollar amount for “ultimate net loss<br />
each and every loss and/or series thereof arising out of a Qualifying Event<br />
in excess of” an agreed-upon “ultimate net loss each and every loss and/or<br />
series thereof arising out of a Qualifying Event”.<br />
<br />
A reinstatement provision<br />
usually would not be included, but there are other ways to assure continuing<br />
protection after a loss event, including purchasing second- or<br />
multiple-event coverage, which can also be in the form of an ILW.<br />
While the reinsurance agreement requires that both conditions be satisfied<br />
– that is, only actual losses be reimbursed and only when the industry<br />
losses exceed a predetermined threshold – the agreements tend to be structured<br />
so that only the latter condition determines the payout.<br />
<br />
The attachment point for the UNL is generally chosen at a very low level,<br />
ensuring that exceeding the industry loss trigger level will happen only if<br />
the reinsured suffers significant losses. There is, however, a chance of the<br />
contract being triggered but the covered UNL being below the full reinsurance<br />
limit.<br />
<br />
Arguably the most important element of an ILW contract is the price paid<br />
for the protection provided. The price would typically be expressed as rate<br />
on line (RoL), that is, the ratio of the protection cost (premium) to the protection<br />
limit provided. The payment is often made upfront by the buyer of the<br />
protection.<br />
<br />
An important issue in structuring an ILW is management of credit risk.<br />
This topic is covered later in the chapter. Collateralisation, either full or<br />
partial, might be required to assure payment. The need for collateralisation<br />
is more important when the protection is provided by investors as opposed<br />
to a rated reinsurance company.<br />
<div style="text-align: left;">
<br /></div>
<h3 style="text-align: left;">
ISDA US WIND SWAP CONFIRMATION TEMPLATE</h3>
In 2009, the International Swaps and Derivatives Association (ISDA)<br />
published a swap confirmation template to facilitate and standardise the<br />
documentation of natural-catastrophe swaps referencing US wind events.<br />
Prior to that, several templates existed in the marketplace. The ISDA<br />
template is based on the one originally developed by Swiss Re. The template<br />
uses PCS estimates for insurance industry loss data for catastrophic wind<br />
events affecting the US.<br />
<br />
The covered territory is defined as all of the US,<br />
including the District of Columbia, Puerto Rico and US Virgin Islands. The<br />
option of choosing a subset of this territory also exists. It allows the choice<br />
of three types of covered event: USA Wind Event 1, USA Wind Event 2 and<br />
USA Wind Event 3. The first type is the broadest and includes all wind<br />
events that would be included in the PCS Loss Report.<br />
<br />
The second specifically<br />
excludes named tropical storms, typhoons and hurricanes, while the<br />
third includes only named tropical storms, typhoons and hurricanes. As in<br />
all of the swap confirmations used in the past for US wind, flood following<br />
covered perils is included in the damage calculation. The template clarifies<br />
the treatment of workers’ compensation losses, and whether loss-adjustment<br />
expenses related to such losses are included.<br />
<br />
It allows for both binary<br />
and non-binary (linear) payments in the event of a covered loss.<br />
The ISDA template specifically states that the transaction is not a contract<br />
of insurance and that there is no insurable loss requirement. The structure is<br />
that of a pure financial derivative without any insurance component.<br />
While the template brings legal documentation standardisation to these<br />
OTC transactions, it allows a significant degree of customisation to minimise<br />
the basis risk of the hedging party; this degree of customisation is not<br />
possible when using only exchange-traded instruments.<br />
<div style="text-align: left;">
<br /></div>
<h3 style="text-align: left;">
IFEX CATASTROPHE DERIVATIVES</h3>
Of the exchange-traded catastrophe derivatives, IFEX event-linked futures<br />
(ELF) are one of the two most common, the other being CME catastrophe<br />
derivatives. IFEX is the Insurance Futures Exchange, which developed<br />
(together with Deutsche Bank) event-linked futures. IFEX event-linked<br />
futures are traded on the Chicago Climate Futures Exchange (CCFE), a relatively<br />
new exchange focused on environmental financial instruments.<br />
<br />
CCFE is owned by Climate Exchange PLC, a UK publicly traded company. The<br />
founder of CCFE, Richard L. Sandor, played a key role in the introduction<br />
of the first catastrophe derivative products in the early 1990s. Even though<br />
the products were well designed, at the time the insurance industry was not<br />
ready for such a radical innovation as trading insurance risk.<br />
<br />
In addition to the need for education, the industry then did not have proper tools to quantify<br />
catastrophe risk or to estimate the level of basis risk created by the use<br />
of index-linked products as opposed to traditional reinsurance.<br />
The CCFE IFEX contracts have been designed to replicate, as far as<br />
possible, the better-known and accepted ILW contracts.<br />
<br />
The two primary differences between a traditional ILW and the corresponding IFEX contract<br />
are, first, that IFEX event-linked futures are financial derivatives and not<br />
reinsurance, and, second, that IFEX contracts provide an effective way to<br />
minimise if not eliminate the counterparty credit risk present in many ILW<br />
transactions. The terms “IFEX contract” and “ELF contract” are often used<br />
interchangeably.<br />
<br /></div>
Unknownnoreply@blogger.com1tag:blogger.com,1999:blog-6468318357328951173.post-90676643361101720792015-08-06T05:49:00.000-07:002015-08-09T12:00:15.681-07:00Catastrophe Model Structure<div dir="ltr" style="text-align: left;" trbidi="on">
<h3 style="text-align: left;">
CATASTROPHE MODEL STRUCTURE</h3>
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A catastrophe model that can be used in modelling insurance losses includes<br />
all the primary elements mentioned above. It starts with generating a natural catastrophe event such as a hurricane or an earthquake, then determines its physical characteristics at the locations where insured properties<br />
are situated, and finally determines the degree of damage caused to the properties and the total financial loss to the insurance companies.<br />
<br />
The model effectively simulates many (sometimes as high as a million or<br />
even more) hypothetical years and accumulates the loss statistics over these<br />
hypothetical years. The large number of simulations is essential when<br />
dealing with very rare events.<br />
<br />
The basic structure of the catastrophe models has been described in this<br />
and the previous chapter. Figure 4.16 shows a structure of a catastrophe<br />
model that is designed specifically for the hurricane hazard; it also shows<br />
some of the parameters that are generated by the model in intermediate<br />
steps in order to arrive at the final result, aggregate financial loss.<br />
<br />
Most (but not all) modules of the model are relatively independent of each<br />
other, with one feeding its output into the next one. Each module is critical<br />
in that it affects the end result to a significant degree. This structure explains<br />
the need for the wide-ranging multidisciplinary expertise required for<br />
developing such a model.<br />
<br />
The distribution of aggregate insurance losses is the primary piece of<br />
information used in the analysis of indemnity catastrophe bonds. A model<br />
like the one outlined in Figure 4.16 also allows us to produce the probability<br />
distributions of total industry losses or of catastrophic events without referencing<br />
insurance losses, which are needed in the analysis of catastrophe<br />
bonds with industry loss and parametric triggers respectively. Not all<br />
elements of the model might need to be utilised in these cases.<br />
<br />
<h3 style="text-align: left;">
MODELLING TERRORISM RISK</h3>
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Modelling the risk of terrorist attacks has unique challenges not present in<br />
modelling natural catastrophes. Similar to natural catastrophes, acts of<br />
terrorism are represented by a sample of historical observations. However,<br />
the applicability of such data to the present can be limited in that the political,<br />
societal and technological landscape has probably changed since the<br />
historical observations were made.<br />
<br />
Until September 11 of 2001, our assessment of potential terrorist attacks was certainly different. In addition to the changing sociopolitical and technological landscape, there is also the human<br />
factor of terrorists dynamically trying to choose the targets, weapons and<br />
operational means of implementing an attack.<br />
The article on securitising extreme mortality risk provides an overview<br />
of how the risk of terrorism was modelled in some of the extreme mortality<br />
<br />
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bonds. In summary, the model developed by Milliman for those transactions<br />
was based in part on a multi-level logic tree approach. At each level of<br />
the logic tree, three choices were possible: “success” of the terrorist attack,<br />
resulting in a random number of deaths in the predetermined range;<br />
<br />
“failure” of the terrorist attack; and escalation to the next level of severity<br />
(greater number of deaths). The third choice led to the next level of the decision<br />
tree, where the same choices were presented. At every level,<br />
probabilities of each outcome – “success”, “failure” and escalation – were<br />
determined by fitting a distribution to the actual observations over the<br />
previous six-year period (that included 2001).<br />
<br />
The model was simple and based on a very limited number of observations; however, it is not clear that more mathematically sophisticated models add value unless they are based<br />
on additional external information.<br />
The terrorism model described in the chapter on extreme mortality securitisation<br />
focuses entirely on the risk of mortality due to acts of terrorism.<br />
Property and other damage resulting from terrorism was not directly modelled.<br />
<br />
Risk Management Solutions (RMS) has developed its own proprietary<br />
terrorism risk model for the US, as well as a global model. The model is<br />
based in part on the game theory approach to reflect changes in the landscape.<br />
The situation is constantly evolving: as antiterrorism measures and<br />
higher security are implemented, terrorists change their tactics and potential<br />
targets. The moving target creates modelling difficulties that cannot be<br />
addressed in a mathematical model but require extensive expert input. In<br />
fact, this might be one of the cases where scenario analysis is preferable to a<br />
fully probabilistic framework.<br />
<br />
Using expert input is required to first build a database of potential targets.<br />
Prioritising the targets is the next step; it requires the analysis of both the<br />
target’s attractiveness to a terrorist and the degree of the target protection.<br />
As the latter factors change, the priorities are adjusted as well. The database<br />
of potential targets also contains data on potential damage to life and on<br />
economic loss from a terrorist attack.<br />
<br />
A terrorism model should also incorporate the fact of the existence of<br />
several attack modes based on various weapons that could be used. In addition<br />
to conventional weapons, chemical, biological, radiological or nuclear<br />
(CBRN) weapons can be utilised, each with its own probability of occurrence<br />
and potential damage.<br />
<br />
The choice of terrorism weapons can also be site-specific, as some weapons would be more natural choices for attacks on specific sites. Finally, the mode of attack might be unconventional but it might not fit in the CBRN category either. The attack on the World Trade<br />
Center in 2001 provides an example of such a type of weapon.<br />
<br />
The RMS probabilistic terrorism model is a bold attempt to combine<br />
rather sophisticated approaches taken from game theory, with extensive input on potential targets, threat levels and terrorist behaviour modes, in<br />
order to quantify the risk of losses from terrorism, with the focus on large<br />
losses that can be called catastrophic.<br />
<br />
The input is dynamic in that the new developments such as antiterrorism measures, information on potential types of weapons that might be in the hands of terrorists, and even the level<br />
of “chatter” detected by the intelligence community can in theory be<br />
reflected in the inputs into the model or in adjusting some of its parameters.<br />
The overall framework appears to allow a growing degree of sophistication<br />
and the incorporation of additional information on a dynamic basis.<br />
<br />
The practical implementation, however, presents numerous challenges.<br />
In assessing a difficult-to-quantify risk such as terrorism, it is particularly<br />
important to augment the probabilistic approach with scenario analysis.<br />
Along with allowing for reasonability testing, scenario analysis introduces<br />
one more way to use expert judgement in analysing exposure to the risk of<br />
terrorist attacks.<br />
<br />
<br />
<h3 style="text-align: left;">
MODELLING PANDEMIC FLU RISK</h3>
The risk of a global pandemic of an infectious disease is not insignificant.<br />
The chances of a pandemic of a serious disease with a high level of mortality<br />
might be small, but the consequences of such an event would be catastrophic.<br />
Focusing on insurance losses, there would be a spike in mortality rates resulting in life insurance losses of possibly a catastrophic nature, as well as an avalanche of medical claims resulting in huge health insurance losses.<br />
<br />
The latter might be the case even if the mortality rate is not high but<br />
the severity of the disease is. Finally, there would be property-casualty<br />
insurance losses. These would obviously include business-interruption<br />
insurance losses. However, it is possible that other lines of property-casualty<br />
insurance business might suffer even greater losses, even though such losses<br />
are usually not fully contemplated in catastrophe risk analysis.<br />
<br />
The chapter on extreme mortality bonds describes how pandemics have<br />
been modelled in the context of evaluating their potential impact on<br />
mortality rates resulting in a mortality spike. In analysing the risk of<br />
pandemics, the main focus is flu pandemics, since these are considered to<br />
represent the great majority of this type of risk in modern times.<br />
<br />
Milliman created a model for analysing the risk of mortality spikes due to flu pandemics in catastrophe mortality bonds. The model separated the frequency and severity components, parameters of which were estimated based on the available historical data. The data for<br />
frequency was considered over a long (multi-century) period of time, at least in some cases. Binomial distribution was used for annual frequency, which is a natural choice in modelling the frequency of such events.<br />
<br />
Severity data was based on five or six data points in the more recent history. In at least one<br />
of the securitisations, Milliman modelled severity as a percentage of excess<br />
mortality fitted to these historical data points, one of which was adjusted by<br />
placing a cap on broad mortality improvements in the general population.<br />
(See the fitted severity curve for excess mortality resulting from pandemics<br />
for the Tartan Capital securitisation, in the chapter on the securitisation of<br />
extreme mortality risk.)<br />
<br />
The Milliman model then simulates the pandemic<br />
results by sampling from the frequency and severity distributions. The<br />
current Milliman model’s results are sensitive to the distribution of age and<br />
gender. The binomial frequency distribution assumes that the probability of a<br />
pandemic is the same in any year. It is likely that the current risk of a flu<br />
pandemic is elevated above the average historical levels.<br />
<br />
This can be reflected by adjusting the mean of the binomial distribution; significant<br />
judgement and expert input are required to properly make this adjustment.<br />
The Milliman model is of the type that is sometimes called actuarial, in<br />
that frequency and severity are modelled separately based on available<br />
historical data. Another approach – the epidemiological one – is used in the<br />
model developed by RMS.<br />
<br />
It is based on a standard epidemiological approach known as SIR modelling (susceptible, infectious, recovered), which allows us to take into account additional variables such as vaccination, immunity, viral characteristics and lethality in a more direct way. The<br />
RMS model presents a more sophisticated approach from the mathematical<br />
point of view; but whether it is better than the simpler Milliman model is not<br />
fully clear, since it requires a number of inputs that introduce uncertainty<br />
and have the potential to skew the results. In the longer term, however, the<br />
RMS model is likely a better one to use for modelling pandemic risk.<br />
<br />
The Swiss Re internal model is reported to be a combination of the actuarial<br />
and epidemiological types. The excess mortality rates are estimated<br />
based on historical data as in the Milliman model, but are then adjusted to<br />
take into account the changes that have happened since those observed<br />
events. These changes include new virus threats, vaccinations, better standards<br />
of medical care, etc. A significant degree of judgement is used in<br />
making these adjustments.<br />
<br />
The article on securitisation of extreme mortality risk shows a fully<br />
stochastic model of the spread of a pandemic, implemented on the Los<br />
Alamos National Lab supercomputer. This approach is probably the one that will eventually become the standard. Right now it is not realistic. Of the<br />
models described above, the RMS model is the closest to this approach.<br />
<br />
<br />
<h3 style="text-align: left;">
PRACTICAL MODELLING OF CATASTROPHE RISK</h3>
<i>It is not certain that everything is uncertain.<br /><b>Blaise Pascal</b></i><br />
<br />
The time of occurrence of a natural catastrophe is unpredictable. Its magnitude<br />
is unpredictable too. So is the damage it causes in its wake. This is the<br />
inherent uncertainty associated with such events as hurricanes or earthquakes.<br />
When it comes to natural catastrophes, we are in the country where<br />
predictions do not work.<br />
<br />
Manmade catastrophes are in the same territory. The goal of modelling catastrophic events in the context of insurance securitisation as well as in general is to minimise the uncertainty<br />
surrounding the probability distribution of possible outcomes. The closest to<br />
certainty is the one who most precisely identifies and quantifies the uncertainty<br />
of these random variables.<br />
<br />
<h3 style="text-align: left;">
Available models</h3>
The previous chapter identified the three main providers of commercial<br />
catastrophe-modelling software used in the analysis of potential insurance<br />
losses. In addition to AIR Worldwide, EQECAT and Risk Management<br />
Solutions, there are additional providers of either software or consulting<br />
services based on proprietary software for modelling of catastrophic insurance<br />
losses.<br />
<br />
These tend to focus on one type of hazard in a specific<br />
geographic area. For example, Applied Research Associates’ hurricane<br />
model and URS’s earthquake models (combined and modified under the<br />
Baseline Management umbrella) are now covering all of the US. There are<br />
also some noncommercial models such as the Florida Public Hurricane Loss<br />
model (for Florida hurricane risk only) and FEMA’s HAZUS tool, which in<br />
its modified form can be used for modelling insurance losses.<br />
<br />
While a number of external models exist, in practice only the main three,<br />
AIR Worldwide, EQECAT and Risk Management Solutions, have been<br />
utilised in securitisation of insurance risk. This is reflective of the complete<br />
domination of these three companies in the insurance and reinsurance<br />
industry and the credibility they have earned over the years.<br />
<br />
Problems – realor perceived – with modelling software developed by these companies have<br />
been pointed out on a number of occasions. However, they do have the track<br />
record and credibility that no competitor possesses.<br />
Some companies in the industry, in particular reinsurance companies, have developed their own proprietary models of insurance catastrophe risk.<br />
<br />
However, these are generally not full catastrophe models but rather the software<br />
that sits on top of the three established models and uses their output<br />
to obtain its own estimate, which might be different from the results of each<br />
of the underlying models.<br />
<br />
While not every peril in every geographical area can be modelled, there<br />
now exist catastrophe models covering all the key areas of insurance exposure.<br />
Table 4.5 shows an incomplete list of the existing peril models and the<br />
countries for which they have been created. In almost all circumstances, all<br />
three major modelling companies would have these models.<br />
<br />
While many individual models – for specific perils and countries – are<br />
available, not all of them have the same degree of credibility. Models for<br />
some regions and perils are based on more extensive research and have<br />
existed for a longer period of time. The longer period of time has created<br />
more opportunities for model validation and refinement. Not surprisingly,<br />
the three most refined models cover:<br />
<br />
1. North Atlantic hurricanes (in particular Florida and the other Gulf<br />
states in the US);<br />
2. California earthquakes; and<br />
3. Japanese earthquakes.<br />
<br />
These three represent the biggest catastrophe risks for the insurance<br />
industry. They combine high concentration of insured exposure and high<br />
probability of catastrophic events. Even though the models produced by the<br />
three modelling firms have existed for a long time, their results differ, sometimes<br />
significantly, from one firm to another, and significant adjustments to<br />
each of them have been made even very recently.<br />
<br />
The net result is the uncertainty that still exists in quantifying catastrophe insurance exposure<br />
even in the areas where the research has been extensive and the investment<br />
in model development quite sizable.<br />
<br />
It is important to carefully analyse whether indirect effects of natural<br />
catastrophes have been modelled, and, if so, how. These indirect effects<br />
include, for example, flood following a hurricane and fire following an<br />
earthquake. These secondary effects might result in more damage than the<br />
primary ones, and their proper modelling is critical.<br />
<br />
<h3 style="text-align: left;">
Unmodelled losses</h3>
One of the most common examples of unmodelled losses are those that<br />
reflect improper data coding, resulting in wrong or incomplete entry of<br />
<br />
<table border="1">
<tbody>
<tr>
<td><h3>
Period</h3>
</td><td><h3>
Peril</h3>
</td><td><h3>
Country+</h3>
</td></tr>
<tr>
<td><b>Hurricanes,
cyclones and
storms</b></td><td>North America,
Mexico and
Caribbean</td><td>US (including Alaska), Mexico, Bahamas,
Barbados, Bermuda, Cayman Islands, Dominican
Republic, Jamaica, Puerto Rico, Trinidad and
Tobago</td>
</tr>
<tr>
<td></td><td>Europe</td><td>Austria, Belgium, Denmark, France, Germany,<br />
Ireland, Netherlands, Norway, Sweden,<br />
Switzerland, UK (including flood)</td>
</tr>
<tr>
<td></td><td>Asia-Pacific</td><td>Australia, China (including Hong Kong), Hawaii<br />
(US), Japan, Philippines, Taiwan</td>
</tr>
<tr>
<td><b>Earthquakes</b></td><td>North America,<br />
Mexico and<br />
Caribbean</td><td>US (including Alaska), Canada, Mexico, Bahamas,<br />
Barbados, Cayman Islands, Dominican Republic,<br />
Jamaica, Puerto Rico, Trinidad and Tobago</td>
</tr>
<tr>
<td></td><td>Central and<br />
South America</td><td>Belize, Chile, Costa Rica, Colombia, El Salvador,<br />
Guatemala, Honduras, Nicaragua, Panama, Peru,<br />
Venezuela</td>
</tr>
<tr>
<td></td><td>Europe and<br />
Middle East</td><td>Greece, Israel, Italy, Portugal, Switzerland, Turkey</td>
</tr>
<tr>
<td></td><td>Asia-Pacific</td><td>Australia, China, Hawaii (US), Indonesia, Japan,<br />
New Zealand, Philippines, Taiwan</td>
</tr>
<tr>
<td><b>Tornado and<br />related</b></td><td>North America</td><td>Canada, US</td>
</tr>
<tr>
<td><b>Terrorism</b></td><td>North America</td><td>US (worldwide terrorism models also exist but their<br />
credibility level is unclear)</td>
</tr>
<tr>
<td><b>Flu pandemic</b></td><td>Worldwide</td><td>Worldwide </td>
</tr>
</tbody></table>
<br />
exposure into the model. This is part of the pervasive issue of data quality<br />
described below.<br />
It is not unusual for some of the insured exposure not to be reflected in the<br />
models because they are not designed to handle specific types of coverage.<br />
Additional perils, related to the main one but in an indirect fashion, would<br />
probably not be taken into account by the model.<br />
<br />
Finally, there might be insurance losses due to catastrophic events that have never been contemplated in the original coverage but still have to be paid by insurance<br />
companies. Care should be taken to make sure that all losses that can be<br />
modelled by catastrophe software are input, and any other losses evaluated<br />
separately.<br />
<div style="text-align: left;">
<br /></div>
<div style="text-align: left;">
The issue of data quality is usually raised not in the context of the data<br />
used to formulate and parameterise the models, but in assessing the reliability<br />
and completeness of the data on the details of the exposure in applying<br />
a catastrophe model to a portfolio of insurance policies. </div>
<div style="text-align: left;">
<br /></div>
<div style="text-align: left;">
Quality of the insurance data serving as input into catastrophe models is an industry-wide issue<br />
introducing a significant degree of uncertainty to results of the modelling<br />
process. Best practices are still in the process of being developed, and the<br />
quality of data can vary widely from one insurance company to another.<br />
Improper data coding or not capturing all the relevant exposure data in<br />
sufficient detail is also an indication of deficiencies in the underwriting<br />
process.</div>
<div style="text-align: left;">
<br />
Implications for investors can be significant. Two insurance-linked securities,<br />
such as catastrophe bonds with indemnity trigger, might appear very<br />
similar but in reality have different risk profiles because of the different</div>
<h3 style="text-align: left;">
Modelling results presented to investors</h3>
As a reminder of the primary goal of the analysis, Panel 4.4 shows the<br />
summary output of the risk analysis performed for an indemnity catastrophe<br />
bond (see the chapter on property catastrophe bonds for additional<br />
information). It is no more than a summary, but it is often the main part of<br />
the information included in the offering circulars, no matter how long the<br />
risk analysis section appears to be.<br />
<br />
<h3 style="text-align: left;">
DATA QUALITY</h3>
The quality of data used in catastrophe models is as important as the quality<br />
of the models themselves. Data used to create and parameterise the models<br />
affects the precision and correctness of modelling results. Many elements of<br />
the existing models have been built so that they can take advantage of the<br />
most reliable data available. For example, certain hurricane data available<br />
from the National Oceanic and Atmospheric Administration databases<br />
include measurements at six-hour intervals. Models have been constructed<br />
specifically to take the six-hour intervals into account, as other data is either<br />
unavailable or not fully reliable. This is also the data used to validate the<br />
models.<br />
<br />
<br />
<h3 style="text-align: left;">
ILLUSTRATIVE SUMMARY OUTPUT OF RISK ANALYSIS OF A CATASTROPHE BOND</h3>
A simplified catastrophe bond description is presented below. The<br />
coverage attaches at US$5 billion of ultimate net loss resulting from a single<br />
occurrence of a hurricane.<br />
<br />
<u>Transaction parameters</u><br />
<br />
<b>Covered risk :</b> Hurricane affecting specific insurance portfolio<br />
<b>Trigger: </b>Indemnity per occurrence (UNL)<br />
<b>Attachment: </b>level US$5.0 billion<br />
<b>Exhaustion: </b>level US$5.5 billion<br />
<b>Insurance percentage: </b> 50%<br />
<b>Principal amount</b>: US$250 million<br />
<br />
Based on the per-occurrence exceedance probabilities resulting from catastrophe<br />
modelling of the subject insurance portfolio, key risk measures are<br />
calculated. The expected loss in this example is 1.48% per annum. The<br />
attachment probability is 1.70%.<br />
<br />
<table border="1">
<tbody>
<tr>
<td><b>Risk measures</b></td><td style="text-align: center;"><b>Base case</b><br />
<b> (standard catalogue)</b><br />
<b>(%) </b></td><td style="text-align: center;"><b>Warm Sea Surface</b><br />
<b>Temperature catalogue<br />(%)</b> </td>
</tr>
<tr>
<td><b>Attachment probability</b></td><td>1.70</td><td>2.54</td>
</tr>
<tr>
<td><b>Exhaustion probability</b></td><td>1.30</td><td>1.83</td>
</tr>
<tr>
<td><b>Expected loss</b></td><td>1.48</td><td>2.15</td>
</tr>
</tbody></table>
<br />
In this example, modelling was done twice: first with parameterisation<br />
based on the long-term historical averages of hurricane activity in the<br />
covered territory, and then based on the so-called Warm Sea Surface<br />
Temperature catalogue to take into account the greater chance of hurricane<br />
activity in the current period. The latter is of most interest since it is believed<br />
to present results that are more realistic.<br />
<br />
This summary does not include many of the other important elements of<br />
risk analysis. However, it does show the two figures of most interest to<br />
investors: expected loss and attachment probability. Expected loss provided<br />
in the offering circular serves as the starting point for analysis performed by<br />
investors.<br />
<br />
degrees of uncertainty related to data quality and underwriting standards in<br />
general. In evaluating such insurance-linked securities, the few investors<br />
familiar with underwriting processes of individual insurance companies can<br />
have an advantage over those not possessing this level of expertise.<br />
The seemingly inconsequential issue of data quality can play a much<br />
greater role in modelling catastrophe risk than we would expect. It presents<br />
a good illustration of the “garbage in, garbage out” principle, and could be<br />
an important element of the analysis performed by investors.<br />
<br />
<h3 style="text-align: left;">
INVESTOR AND CATASTROPHE MODELLING</h3>
Investors in catastrophe insurance-linked securities are presented with<br />
numerous choices and decisions in their analysis. Most of them have been<br />
mentioned or alluded to above.<br />
<br />
The questions to be answered are numerous. Which catastrophe model is<br />
most appropriate for a specific type of risk exposure? How different are the<br />
results of different models? Are there known biases in some models related<br />
to specific perils or geographical regions? Are models for one region more<br />
credible than for another?<br />
<br />
How can we quantify the additional uncertainty<br />
related to the lower credibility of some models? Are there ways to validate<br />
some modelling results? What are the primary sources of uncertainty in the<br />
modelling? How do we quantify the additional uncertainty of securities<br />
with indemnity as opposed to parametric trigger?<br />
<br />
The list of questions never ends, which once again underscores the advantages<br />
of having modelling expertise in the analysis of insurance-linked<br />
securities. It almost makes us wonder whether the informational disadvantage<br />
of the investor is too great to play the ILS game. The disadvantage is<br />
relative to both the sponsors of catastrophe bonds and to reinsurance<br />
companies that often invest in these securities. Both seem to have the level of expertise that an investor is usually unable to achieve.<br />
<br />
The answer to this question is more optimistic than it appears to be, however. Investors can and<br />
do participate in this market and generate attractive risk-adjusted returns.<br />
While reinsurance companies in their role as investors seem to have some<br />
expertise that few investors possess, it is not necessarily the type of expertise<br />
that is most important in ILS investing.<br />
<br />
Investors have the capital markets outlook that is usually lacking in insurance and reinsurance companies investing in insurance-linked securities. This capital markets view gives<br />
investors an advantage in some areas even when they are disadvantaged at<br />
others. Ultimately, the conclusion is simple: modelling is critical, and without modelling expertise it is impossible to generate high-risk adjusted returns<br />
on a consistent basis. The industry is slowly coming to this realisation.<br />
Managing catastrophe risk on a portfolio basis is one of the most critical<br />
elements of ILS investing. The choice of modelling tools is now available for<br />
this purpose; it is also discussed in the chapter on modelling portfolios of<br />
catastrophe insurance-linked securities.<br />
<br />
<h3 style="text-align: left;">
CATASTROPHE BOND REMODELLING</h3>
Almost every cat bond transaction has involved the analysis performed by<br />
one of the three main modelling agencies, AIR Worldwide, EQECAT and<br />
RMS. The summary of the analysis is included in the offering documents; a<br />
data file such as an Excel spreadsheet might also be provided as part of the<br />
offering circulars.<br />
<br />
This raises the question of the differences between<br />
models. The annual expected loss or probability of attachment calculated by<br />
AIR Worldwide might differ, perhaps significantly, from the annual<br />
expected loss or probability of attachment if they were calculated by one of<br />
the other models based on the same data.<br />
<br />
Leaving aside for a moment the question of which model is “better”, in<br />
the ideal world an investor would like to see the analysis performed by all<br />
three modelling firms and then make their own conclusions. “Remodelling”<br />
refers to analysing a catastrophe bond by a modelling firm that did not<br />
perform the initial analysis that was included in the offering documents and<br />
used in pricing of the bond.<br />
<br />
If the security has a parametric trigger, all the<br />
data is available and another modelling firm can easily perform its own<br />
analysis so that the results can be compared. Comparison is much more<br />
difficult for indemnity catastrophe bonds. For these bonds, it is necessary to<br />
have full exposure information in order to perform the analysis. Such information<br />
is never provided to investors; only summaries are included in the<br />
offering circulars.<br />
<br />
In order to perform the analysis, in this situation another modelling firm<br />
has to make a choice between two simplifying assumptions. One of them is<br />
to assume the correctness of the analysis, such as the values of expected loss,<br />
attachment probability and the exhaustion probability. Based on these<br />
figures and the exposure summary in the offering circular, the modeller then<br />
tries to work back to the inputs to arrive at exposure expressed at a greater<br />
level of detail than is provided in the documentation.<br />
<br />
The exposure information is important in portfolio management, where it allows us to monitor<br />
exposure accumulation over many securities and properly establish the<br />
risk–return tradeoffs on a portfolio basis.<br />
<br />
Another choice would be to start with the exposure summary in the<br />
investor documents, and try to estimate what the exposure is at a more<br />
detailed level. This could be done by supplementing the exposure data<br />
provided with publicly available data on the geographic and line-of-business<br />
distribution of exposure for the sponsor, as well as the possible<br />
knowledge by the modeller of the underwriting processes of the sponsor.<br />
<br />
The resultant expected loss and the exceedance probability would then<br />
differ from those in the offering circular. This type of analysis can now be performed very fast, even during the initial marketing stage before the bond pricing has been finalised. This topic<br />
is revisited later in greater detail.<br />
<br />
<h3 style="text-align: left;">
HURRICANE FORECASTING</h3>
“Hurricane forecasting” refers to probabilistic predictions of hurricane<br />
activity in the short term. These are not actual forecasts but probability<br />
distributions of potential outcomes based on the most current data. These<br />
forecasts refer to the upcoming hurricane season or a season already in<br />
progress.<br />
<br />
William Gray, for all intents and purposes, pioneered the field of hurricane<br />
forecasting. He developed a number of forecasting methodologies with<br />
a special focus on North Atlantic hurricanes. Phil Klotzbach, who has taken<br />
from him the leadership of the hurricane forecasting project, in 2009 started<br />
issuing 15-day forecasts in addition to the seasonal ones.<br />
<br />
This is a big change from issuing forecasts from the one to five times a year common for hurricane forecasters. The Klotzbach/Gray group has proven its skill over the<br />
years of issuing hurricane forecasts for the North Atlantic. Its methodology<br />
is continuing to evolve, but in most general terms it is based on identifying and monitoring several atmospheric and/or oceanic physical variables,<br />
either global or relatively localised, that are relatively independent of each<br />
other and have been shown, by utilising statistical analysis tools, to serve as<br />
good predictors of the following North Atlantic hurricane season.<br />
<br />
NOAA issues hurricane forecasts too, as do several research groups<br />
around the world. It appears that as of 2009 only the Klotzbach/Gray group<br />
has been able to clearly demonstrate its skill in forecasting probability of<br />
major hurricane landfalls in the US.<br />
<br />
Other groups either do not issue forecasts associated with landfalls or have not been recognised for their skill in successfully forecasting landfalls. In insurance catastrophe modelling, landfalls<br />
are of major importance, while hurricanes that bypass land are of<br />
interest only if they have the potential to damage oil platforms.<br />
<br />
The forecasts create additional opportunities for optimising risk-adjusted<br />
return on a portfolio basis. They also provide input into pricing of all<br />
affected insurance-linked securities, and in particular ILWs, securitised reinsurance<br />
and catastrophe bonds close to expiration.<br />
<br />
<h3 style="text-align: left;">
Live cats</h3>
The term “hurricane forecasting” is also used in reference to probabilistic<br />
assessment of development of the storms and hurricanes that have already<br />
formed and might make a landfall. The ability to trade the risk of natural<br />
catastrophic events that can occur in the very near future – from several days<br />
to several hours – creates opportunities for those who can obtain better<br />
information on the projected path and potential damage from the hurricane<br />
and to better take advantage of the situation. It also creates opportunities to<br />
offload excess risk if necessary.<br />
<br />
This “live cat” trading can be done on a more<br />
intelligent basis when short-term hurricane forecasts have a relative degree<br />
of credibility.<br />
<br />
The topic of hurricane forecasting is revisited in the chapters on ILWs and<br />
catastrophe derivatives and on managing investment portfolios of insurance<br />
catastrophe risk.<br />
<br />
<b>CLIMATE CHANGE</b><br />
<i>The trouble with our times is that the future is not what it used to be.<br /><b>Paul Valéry</b></i><br />
<br />
Climate change has been mentioned more than once in the context of modelling<br />
catastrophe risk. The expectations of the future climate state are<br />
different from its current one. The effects of climate change relevant to hurricane<br />
activity, in particular the increase in sea-surface temperature, can<br />
already be observed. These changes make it harder to rely on the old<br />
approach of forming conclusions about future natural catastrophe activity<br />
based entirely on prior historical observations.<br />
<br />
The future frequency and severity of hurricane events might be a function of atmospheric and oceanic processes that are different from the ones in the period of historical observations.<br />
The focus of an investor in the analysis of insurance-linked securities tied<br />
to the risk of natural catastrophes is on the relatively short time horizon.<br />
Changes expected to take place over a long period of time are of less significance<br />
due to their minimal impact on catastrophe-linked securities that<br />
tend to have short tenor.<br />
<br />
Unless there is a clearly observable trend, this view<br />
suggests disregarding recent changes and relying primarily on the longterm averages of hurricane frequency and severity. If the speed of the<br />
climate change is rapid, though, this view might be incorrect; there is a need<br />
also to reflect the developing new environment in evaluating the risk of<br />
future hurricanes. In addition, it is possible that the climate changes have<br />
already altered the atmospheric and oceanic processes, probably starting a<br />
number of years ago.<br />
<br />
This view would necessitate immediately taking<br />
climate change into account. In simple terms, we can then see the observed<br />
historical sample of hurricane activity as consisting of two parts: the first,<br />
longer, period when the conditions were relatively constant and the variability<br />
was due to natural statistical fluctuations; and the second period<br />
encompassing more recent years when a trend might be present in the<br />
changing atmospheric and oceanic conditions that influence hurricane<br />
activity. The trend might be accelerating, as suggested by all of the global<br />
warming theories.<br />
<br />
The decision regarding whether we are in the period of heightened hurricane<br />
activity and whether this activity is likely to accelerate in the very near<br />
future is an important one both for insurance companies with significant<br />
hurricane risk accumulation and for investors in catastrophe insurancelinked<br />
securities. The majority have decided that we are now in a period of<br />
climate change that has higher probability of hurricane activity than<br />
suggested by long-term historical averages.<br />
<br />
The modelling firms have incorporated this approach by creating an option in their software models to allow users to make their own choice about whether to base the analysis on<br />
long-term averages or assume higher levels of hurricane activity than<br />
suggested by the history. The latter option is referred to as using the Warm<br />
Sea Temperature Conditioned Catalogue of events when no additional<br />
trends are taken into account.<br />
<br />
The decision to use higher levels of potential hurricane activity as the<br />
primary modelling approach is not tied directly to the acceptance of the<br />
global warming theory; as mentioned earlier, the shorter-term climate<br />
processes of an oscillating nature can provide a sufficient reason for<br />
believing we are in an environment more conducive to hurricane development<br />
than in the past.<br />
<br />
<br />
<h3 style="text-align: left;">
SPONSOR PERSPECTIVE ON MODELLING</h3>
The importance of catastrophe modelling for insurance and reinsurance<br />
companies is apparent. Modelling catastrophe insurance risk is part of the<br />
enterprise risk management (ERM) process. Its results are used in making<br />
decisions on the best ways to employ company capital. They are an important input in decisions on whether to retain the risk, reinsure some of it or<br />
transfer it to the capital markets.<br />
<br />
The transfer to the capital markets can be<br />
in the form of sponsoring insurance-linked securities such as catastrophe<br />
bonds or in the form of hedging catastrophe exposure by purchasing ILWs<br />
or catastrophe derivatives. Another option available to insurance and reinsurance<br />
companies is to rebalance or reduce their underwriting to lower the<br />
overall exposure to catastrophe risk.<br />
<br />
For companies writing insurance that creates catastrophe exposure,<br />
modelling the risk of catastrophes is part of the standard business processes<br />
of underwriting and risk management; it is used also in capital allocation.<br />
Facilitating risk securitisation is not the primary goal of catastrophe modelling,<br />
even though the decision to transfer some of the risk to capital markets<br />
might be based on the modelling results. Instead, the emphasis is on total<br />
risk exposure.<br />
<br />
Modelling catastrophe risk is growing in importance at insurance<br />
and reinsurance companies, as management see the benefits it delivers.<br />
Quantification of catastrophe risk exposure is also driven by shareholders<br />
and rating agencies. Regulators are also paying more attention to catastrophe<br />
risk than ever in the past.<br />
<br />
It would appear that the insurance industry has greater expertise in<br />
modelling catastrophe risk than the investor community. While this is<br />
generally true, there are investors who are very sophisticated in catastrophe<br />
modelling, while the insurance industry expertise is generic and not focused<br />
on the specific issues relevant to securitising insurance risk.<br />
<br />
<h3 style="text-align: left;">
MODELLING AS A SOURCE OF COMPETITIVE ADVANTAGE TO INVESTORS</h3>
The primary risk of insurance-linked securities in almost all cases is, of<br />
course, the insurance risk. The risk of catastrophic events is the one most<br />
commonly transferred to investors; on the property insurance side the risk<br />
of catastrophic events fully dominates insurance securitisation. To make an<br />
informed decision, an ILS investor has to understand the risk profile of these<br />
securities.<br />
<br />
Without this understanding, it is impossible to make any intelligent<br />
decisions on individual insurance-linked securities or their portfolios.<br />
Catastrophe modelling and the risk analysis based on it are key to understanding<br />
the risk profile of these securities.<br />
<br />
(As pointed out earlier, there might be situations when an investor makes an informed decision to allocate a small portion of their assets to insurance-linked securities without developing<br />
expertise in this asset class. These situations are rare.)<br />
<br />
Since the ability to quantify risk and determine its proper price is based on catastrophe modelling and risk analysis, those investors better able to<br />
understand the risk analysis section of the offering circulars for catastrophe<br />
bonds have an immediate advantage over the rest of the investor community.<br />
Properly interpreting the risk analysis section requires knowledge of<br />
modelling techniques used, modelling software packages utilised, model<br />
credibility, the way exposure data is captured, and other modelling-related<br />
issues.<br />
<br />
Those who have better understanding of these issues have an advantage<br />
over those who do not. They are in a better position to quantify the<br />
uncertainty, make adjustments if necessary, and extract more useful information<br />
from the same risk analysis section of the offering circulars. This<br />
advantage is not limited to catastrophe bonds and is applicable to all types<br />
of catastrophe insurance-linked securities.<br />
<br />
Finally, those investors who use catastrophe modelling tools themselves<br />
have an extra advantage over those who do not. They tend to have a greater<br />
degree of understanding of the assumptions underlying the models and the<br />
types of uncertainty involved. The most sophisticated of them are able to<br />
perform additional sensitivity analysis and scenario testing, to come up with<br />
a better understanding of the risk profile of the security and the price to<br />
charge for assuming this risk.<br />
<br />
An example of the competitive advantage held by those with superior<br />
understanding of catastrophe modelling tools can be found in the analysis<br />
of California earthquake exposure. The difference in scientific views on<br />
which part of the San Andreas fault is most ripe for a major earthquake<br />
(referred to earlier in this chapter) is one of the reasons for the divergence in<br />
results among commercial catastrophe models in estimating expected losses<br />
at various exceedance levels from one part of California to another.<br />
<br />
(The divergence is true at the time of writing; models evolve, and updates and<br />
new releases are issued periodically.) Understanding the difference between<br />
models is by itself a source of competitive advantage; having an informed opinion on which model is likely to produce more precise results for a<br />
specific peril and geographical territory adds significantly to this competitive<br />
advantage.<br />
<br />
Even an informed view on the likely variability of results<br />
around the expected mean for a specific peril and geographical territory,<br />
and how it varies from model to model, is an informational advantage.<br />
The use of models by investors is of particular importance in portfolio<br />
management.<br />
<br />
Without using real catastrophe models, all an investor can do<br />
is to make very rough estimates of the risk accumulation by peril/geography<br />
bucket and try to put limits on individual risk buckets. There is no<br />
way to properly estimate risk-adjusted return for the portfolio, or how the<br />
addition of a position will affect the overall risk–return profile. The investors<br />
who are able to use modelling tools, both in the analysis of individual securities<br />
and in portfolio management, have an important competitive<br />
advantage, the value of which is magnified by the overall inefficiency of the<br />
insurance-linked securities market.<br />
<br />
<h3 style="text-align: left;">
MODELLING AS A SOURCE OF COMPETITIVE DISADVANTAGE TO INVESTORS</h3>
The appearance of models designed specifically for investors in insurancelinked<br />
securities such as catastrophe bonds is changing the way some<br />
investors are approaching ILS investing. Some of those who never utilised<br />
catastrophe modelling tools before have now tried to use the new software<br />
to model their ILS portfolios.<br />
<br />
The models designed specifically for investors<br />
are described elsewhere, including in the chapter on portfolio management.<br />
They are much simpler to use and understand than the full-blown catastrophe<br />
models used by insurance companies and, in most cases, by<br />
modellers providing the risk analysis in structuring catastrophe bonds. They<br />
do provide ways to analyse and visualise portfolio exposure, perform “what<br />
if” analysis, and more. They appear to be simple to use.<br />
<br />
The seeming simplicity of the tools is deceptive, however. By themselves<br />
they do not provide more than a software platform to combine individual<br />
cat bonds into one portfolio, with a semiautomatic way of calculating<br />
several risk measures.<br />
<br />
This platform is very useful to those who already<br />
understand the modelling approaches, the assumptions used in modelling,<br />
the differences between the models used for initial analysis, the degree of<br />
possible unmodelled risk, and many other factors required for using modelling<br />
tools and properly interpreting modelling results.<br />
<br />
For others, not possessing this expertise, the picture might be different. The availability of a<br />
tool that is a black box to a user can have mixed consequences. The tools<br />
themselves are not true black boxes: they are black boxes only to those who<br />
do not have the requisite expertise to use them effectively.<br />
<br />
While most ILS investors do not use these portfolio management tools,<br />
some of those who do may be worse off than if they did not. The ability to<br />
see all securities in one portfolio and have the software spit out risk<br />
measures and other statistics can create the illusion of understanding and<br />
properly managing portfolio risk when none is present.<br />
<br />
Modelling can be very dangerous to investorswho lack the understanding<br />
of howit is performed andwhat the resultsmean.Of course, the danger is not<br />
in modelling, but in not having the level of expertise needed to understand<br />
the modelling methods, output and implications. This problem has existed<br />
for a very long time and is unrelated to the appearance of software tools<br />
targeted specifically at the ILS investor.<br />
<br />
Improper interpretation of the risk analysis section of offering circulars by some investors has been going on for so long because of the seeming simplicity of the data presented. It creates the<br />
illusion of understanding, and that can be very dangerous. Some investors<br />
have become proficient in the lingo of catastrophe bonds and relatedmodelling<br />
but,without realising it, have not gained the level of expertise needed to<br />
turnmodelling into a useful tool. To think they understand the risk of securitieswhen<br />
they really do not creates a dangerous situation.<br />
<br />
The false sense of security when it comes to risk management, and the<br />
illusion of actively managing a portfolio to maximise its risk-adjusted<br />
return, can lead to catastrophic results for some investors in catastrophe risk.<br />
One more danger to point out is that the investors focused on modelling<br />
catastrophe risk are sometimes focused on it too much, to the degree that<br />
they do not pay the necessary attention to other types of risk associated with<br />
insurance-linked securities.<br />
<br />
These other risks are important in the analysis of<br />
individual securities; it is also important to take them into account when<br />
these securities become part of an investment portfolio.<br />
<br />
The problems mentioned above would become obvious and self-correct<br />
in investing in almost any other asset class. The level of historical returns<br />
and their volatility by itself would be a clear indicator of investor expertise,<br />
in most cases. Catastrophe ILS are tied to the risk of very rare events, and a<br />
track record of several years says little about the level of risk-adjusted<br />
returns generated.<br />
<br />
<h4 style="text-align: left;">
TRENDS AND EXPECTATIONS</h4>
The importance of modelling in the analysis of insurance-linked securities is<br />
impossible to overestimate. The specific type of modelling involved in the<br />
probabilistic analysis of catastrophe events and the resulting insurance<br />
losses is unusual in the investment world and requires specialised expertise.<br />
The times when most investors made their decisions based on the rudimentary<br />
analysis of the information in the offering documents have passed. A<br />
greater level of sophistication is now required.<br />
<br />
Insurance and reinsurance companies seeking to transfer some of their<br />
risk to the capital markets in the form of insurance-linked securities<br />
have dramatically improved and continue to improve their risk modelling<br />
and management. They are more and more finding themselves in the position of being able to make fully informed decisions on the ways<br />
to manage their catastrophe exposure and properly choose among such<br />
options as reinsurance, securitisation and retaining catastrophe risk.<br />
<ul style="text-align: left;">
<li>Superior modelling skills and the ability to better interpret results of modelling catastrophic events are a major source of competitive advantage to the investors who have this level of expertise. As the importance of modelling is becoming more widely recognised, those who lack the expertise will find it increasingly difficult to compete effectively.</li>
<li>The ability to model risk is particularly valuable in assembling and managing portfolios of insurance-linked securities. This skill is even more important at the portfolio management level than in determining the right price for a particular catastrophe bond or another security whose risk is linked to catastrophic events.</li>
<li>Without models, it is impossible to assess the risk-adjusted return in investing in catastrophe-linked securities. Without understanding the risk profile of a security, investors are in no position to evaluate whether they are being properly compensated for assuming the risk.</li>
<li>Track record of a fund investing in insurance-linked securities can often be meaningless and even misleading. Some of the investors who have been most successful on paper have achieved higher returns by taking on disproportionate amounts of risk, often unknowingly. Without properly utilised models, we cannot analyse this type of risk. When investing in the more traditional asset classes such as equities, track record of returns is usually very informative and revealing; but it is of less importance in investing in insurance-linked securities and can be considered only in the context of the risk that has been taken. Catastrophic events are, by their very definition, very rare, and it is possible for an investor to “be lucky” for quite a long period of time even when the investment portfolio is completely mismanaged. advantage for an investor in insurance-linked securities. It also enables better decision making for sponsors in dealing with the issues of basis risk.</li>
<li>Issues of data quality, understanding model limitations, credibility of models, and biases among existing models are key components of the type of expertise that can provide a competitive advantage.</li>
<li>Important as the use of modelling tools is, better understanding of the assumptions and superior interpretation of the results are of even greater significance. These two can be the most important sources of competitive advantage.</li>
</ul>
This article provided but an introduction to selected concepts in modelling<br />
catastrophic events in the context of analysing insurance risk securitisation.<br />
Some additional information on the topic can be found in other post in this blog.<br />
The issues touched on here should provide an understanding of why modelling<br />
catastrophe risk is important and why it is so difficult.<br />
<br />
<br /></div>
Unknownnoreply@blogger.com1tag:blogger.com,1999:blog-6468318357328951173.post-88902493761971337482015-08-04T14:54:00.001-07:002015-08-09T12:01:14.713-07:00Modelling Catastrophe Risk Part 2<div dir="ltr" style="text-align: left;" trbidi="on">
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<h3 style="text-align: left;">
SEASONALITY OF THE HURRICANE RISK IN INSURANCE-LINKED SECURITIES</h3>
The main hurricane risk of insurance-linked securities, that of North Atlantic hurricanes, is seasonal as opposed to following uniform distribution. The hurricane season officially starts on June 1 and ends November 30. Very few hurricanes occur outside the hurricane season. Approximately 97% of all tropical storm activity happens during these six months.<br />
<br />
As the the above diagram, there is a pronounced peak of activity within the<br />
hurricane season, which lasts from August through October. Over three quarters<br />
of storms occur during this period. The percentage of hurricanes, in<br />
particular major hurricanes, is even greater: more than 95% of major hurricane<br />
(Category 3 and greater) days fall from August through October.<br />
<br />
Definition of hurricane season is rarely used in the offering documents for<br />
insurance-linked securities. Instead, specific dates determine the coverage<br />
period. Knowing when the hurricane season officially starts and ends is not<br />
relevant. However, there are some insurance-linked securities for which the<br />
definition of the hurricane season is important. Exchange-traded IFEX catastrophe<br />
futures use a formal legal definition of North Atlantic hurricane season.<br />
<br />
This definition is used in establishing maintenance margin levels for<br />
IFEX contracts. Catastrophe futures and similar insurance-linked securities<br />
are described in detail in other chapters.<br />
Hurricanes threatening the Pacific coast of the US and Mexico have a<br />
longer period of heightened activity, which starts earlier than on the Atlantic<br />
coast but has the same activity peak as the North Atlantic hurricanes. West<br />
Pacific hurricanes are distributed even more evenly over the year; they are<br />
less important in securitisation of insurance risk.<br />
<br />
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<a href="http://3.bp.blogspot.com/-N7Ptoa8bJ2w/VcC1WGNw2CI/AAAAAAAAaq0/ceTAKWdQzqs/s1600/distribution%2Bof%2Bhurricanes%2Band%2Btropical%2Bstorm.jpg" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img alt="distribution of hurricanes and tropical storms by month north atlantic" border="0" src="http://3.bp.blogspot.com/-N7Ptoa8bJ2w/VcC1WGNw2CI/AAAAAAAAaq0/ceTAKWdQzqs/s1600/distribution%2Bof%2Bhurricanes%2Band%2Btropical%2Bstorm.jpg" title="distribution of hurricanes and tropical storms by month north atlantic" /></a></div>
<br />
<br />
Hurricanes in the Southern Hemisphere (called typhoons or cyclones<br />
there) tend to occur between October and May, but specific frequency distributions<br />
depend on ocean basin.<br />
<br />
<h3 style="text-align: left;">
LANDFALL FREQUENCY IN PEAK REGIONS</h3>
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Returning to the North Atlantic hurricanes, which present the greatest<br />
threat in the southeastern US, The following two figures illustrate hurricane<br />
landfall frequencies expressed as return periods. Unlike the figures<br />
above, only landfalls – which typically are the only hurricane risk in insurance-<br />
linked securities – are shown, with the two graphs corresponding to<br />
hurricane Categories 1 and 5 on the Saffir–Simpson hurricane scale.<br />
<br />
Return period is defined here as the long-term average of a recurrence<br />
interval of hurricane landfalls of specific or greater intensity (category) at the<br />
time of landfall. It can also be seen as the inverse of the annual exceedance<br />
probability. Return period is usually measured in years.<br />
Historical data is the best indicator of future hurricane frequencies. Of<br />
course, this does not mean that a simple sampling of the historical frequencies<br />
should be used in hurricane simulations. It means only that historical<br />
data is the starting point of any model, which is also where we return to validate<br />
the model once it has been built. A sound model is much more than just<br />
fitting of a distribution to the existing data points; some extremely sophisticated<br />
models have been created in recent years.<br />
<br />
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<br />
<h3 style="text-align: left;">
HURRICANE FREQUENCY AND SEVERITY EFFECTS OVER VARIOUS<br />TIME HORIZONS</h3>
Continuing to focus primarily on hurricanes affecting the US, three primary<br />
phenomena affect hurricane frequency and severity, each operating over its<br />
own time scale: short term, medium term and medium to long term.<br />
<br />
<h4 style="text-align: left;">
1 Short term</h4>
ENSO, which stands for El Niño Southern Oscillation, is the cycle of consistent<br />
and strong changes in sea surface temperature, air pressure and winds<br />
in the tropical Pacific Ocean. The two phases, El Niño and La Niña, typically<br />
take three to five years to complete the cycle.<br />
<br />
El Niño is the warm phase of the cycle, when the sea surface temperature in the tropical Pacific is above average. Its opposite, La Niña, is the phase when the temperatures are below<br />
average. The warming and cooling affect the level and patterns of tropical<br />
rainfall, which in turn has an effect on worldwide weather patterns and<br />
hurricane frequency and severity.<br />
<br />
<br />
El Niño is associated with lower-than-average tropical storm and hurricane<br />
activity in the Northern Atlantic due to higher-than-average vertical<br />
wind shear resulting fromthewind patterns during this phase of ENSO. The<br />
probability of hurricanes and hurricane landfalls in the Caribbean and other<br />
parts of the North Atlantic is significantly reduced during the regular hurricane<br />
season.<br />
<br />
At the same time, the weather patterns lead to an increase in<br />
tropical storms and hurricanes in the eastern tropical North Pacific.Results of<br />
the La Niña phenomenon are the opposite: storm formation and hurricane<br />
activity are increased in the North Atlantic during the hurricane season,while<br />
in the Pacific the probability of hurricanes is lower than average. These two phases of ENSO are not equal in time.<br />
<br />
El Niño rarely lasts longer than one year, while La Niña tends to take between one and three years. There is no strict cyclicality here, in the sense that each of the two phases can have shorter or much longer durations than expected. The general relationship, however,<br />
usually holds, with periods of increased hurricane activity in the Atlantic<br />
being longer than periods of decreased activity.<br />
<br />
Technically speaking, El Niño and La Niña are not truly two phases of the<br />
ENSO cycle. The end of El Niño leads to an ENSO-neutral period, which<br />
may not be followed by a pronounced La Niña phenomenon and can<br />
instead go back to the El Niño stage. Similarly, La Niña may not be followed<br />
by a pronounced El Niño stage.<br />
<br />
ENSO affects not only the frequency but also the severity of hurricanes.<br />
One reason for this is the vertical wind shear effect, where hurricane intensity in the Atlantic is dampened during El Niño and increased during La<br />
Niña. In addition, the tropical storm formation centres differ slightly and the<br />
hurricanes follow different tracks. La Niña results not only in a greater<br />
frequency of hurricanes in the Atlantic but also in a greater probability of<br />
hurricanes being formed off the west coast of Africa. These hurricanes have<br />
a higher chance of increasing in intensity and making a landfall in the US or<br />
Caribbean as major hurricanes.<br />
<br />
The following figure shows an anomalous increase in sea surface temperature<br />
indicative of the arrival of El Niño and the expectation of lower hurricane<br />
activity in the Atlantic.<br />
<br />
<h3 style="text-align: left;">
2 Medium term</h3>
AMO, which stands for Atlantic Multidecadal Oscillation, is a cycle of<br />
consistent and strong changes in sea surface temperature in the North<br />
Atlantic. The cycle is believed to be on the order of 70 years, with the up and<br />
down phases approximately equal in time. The amplitude of the temperature<br />
variations due to the AMO is much milder than that resulting from<br />
ENSO, and the changes much slower. It is believed that we are currently in of the warm phase. This phase is expected to end between 2015 and 2040.<br />
<br />
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AMO has some effect on the overall frequency of tropical storms and<br />
hurricanes, with warmer temperatures contributing to the tropical storm<br />
system development and colder temperatures leading to a reduction in tropical<br />
storms.<br />
<br />
This correlation is not strong and the effect is usually<br />
disregarded. However, during the warm phases of the cycle there is a<br />
greater chance of major hurricanes compared with the average; the chance<br />
is lower during the cold phases. This effect is unambiguous and the correlation<br />
is strong.<br />
<br />
<h3 style="text-align: left;">
3 Medium to long term</h3>
Climate change, in particular the increase in seawater temperature, has a<br />
strong potential to increase both the frequency and the severity of the hurricanes<br />
landfalling on the Atlantic coast of the US. Some of the change is the<br />
result of human activities.<br />
<br />
Global warming, recognised by the majority of<br />
the scientific community, is part of the overall climate change. There is no<br />
consensus on the exact manifestations of and the speed at which climate<br />
change is happening. Some would argue that categorising climate change as<br />
having medium- to long-term effect is wrong, and that substantial changes<br />
are already happening rapidly and will accelerate.<br />
<br />
The risk of abrupt climate change triggered by concurrent development of several factors has been repeatedly pointed out. Even those who subscribe to the global-warming<br />
view without any reservations are unclear on the long-term effects of this<br />
process. In fact, some research has suggested that the increase in the<br />
seawater temperature will lead to a significant increase in hurricane activity<br />
in the North Atlantic, but that at some point the process will reverse itself<br />
and the hurricane frequency will actually decrease even if the temperature<br />
continues to rise. This, however, is a minority opinion.<br />
<br />
While global warming remains a controversial topic, in particular because<br />
different people seem to attribute different meanings to the term, it is widely<br />
accepted that seawater temperature has been rising and that the probability<br />
of hurricanes in the North Atlantic is increasing as a consequence. This<br />
correlation has direct applications for hurricane modelling.<br />
<br />
<h3 style="text-align: left;">
INVESTOR VIEWS ON MACRO-SCALE FREQUENCY AND SEVERITY EFFECTS</h3>
In the analysis of catastrophe insurance-linked securities tied to the risk of<br />
hurricanes, investors have a short-term view due to the relatively short tenor<br />
of these securities. Whether the probability of hurricanes will be greater in<br />
15 years is not germane to the probabilistic analysis of cashflows from a catastrophe bond that matures in two years. To the degree that long-term phenomena such as climate change are already affecting the probability of hurricanes, they are relevant to and should be incorporated in the analysis.<br />
<br />
The difficulty is in having to work with very limited data samples, because,<br />
sometimes, these can provide only anecdotal evidence of the degree to<br />
which long-term processes are already affecting hurricane development and<br />
will continue to do so within the period an insurance-linked security is<br />
expected to remain outstanding.<br />
<br />
In practice, it is currently very difficult to<br />
separate and then separately model effects of the general climate change.<br />
Shorter-term effects such as ENSO, on the other hand, can be better<br />
modelled and incorporated in the analysis. To a lesser degree, the same is<br />
true in regard to AMO. Other processes, such as the overall warming related<br />
to climate change, are often incorporated indirectly through their influence<br />
on the observed parameters of the better-understood processes of storm<br />
formation and development.<br />
<br />
There is a broad issue of whether, and to what degree, catastrophe models<br />
should reflect the observed increase in hurricane activity in the North<br />
Atlantic. Following Katrina and the 2004–2005 hurricane seasons in general,<br />
there was an almost universal conviction that the frequency of hurricanes in<br />
the widely used commercial models was significantly understated.<br />
<br />
(There were also concerns about how other modules of the models performed, and<br />
whether the damage and loss severity were understated.) Since then, the<br />
models have been modified to produce loss results that are greater than<br />
would be expected based purely on long-term historical data, either as the<br />
main output or as an option available to the user.<br />
<br />
The change reflects the view that the long-term observations do not represent the current atmospheric conditions that affect formation, development and landfalling of<br />
tropical storms and hurricanes. This important practical issue is discussed<br />
further below and in other articles.<br />
<br />
Incorporating short-term effects such as ENSO in both the models and the<br />
general analytical approach can better capture the risk profile of insurancelinked<br />
securities and provide competitive advantage to investors able to do<br />
it. For example, if El Niño starts, which can happen fast and unexpectedly,<br />
short-term probabilities of North Atlantic hurricane losses will immediately<br />
be affected. This affects the risk profile of the insurance-linked securities<br />
exposed to this risk.<br />
<br />
The knowledge of lower expected hurricane activity has<br />
immediate application in pricing new insurance-linked securities and those<br />
that can be traded in the secondary markets. Another practical application is<br />
reassessing portfolio risk and return profile in light of the information on El Niño’s start. This reassessment might identify a change in the risk and<br />
return profile of the overall ILS portfolio. The practical result would be a<br />
conclusion regarding which risk buckets have to be filled and which<br />
reduced, and the right prices for doing so.<br />
<br />
Knowledge of expected changes in hurricane activity in the short term,<br />
along with the ability to quantify the degree of the change, can create a<br />
competitive advantage in the environment when many investors are not<br />
using proper models at all and few are able to incorporate new information<br />
in their modelling process.<br />
<br />
With some exceptions, quantifying the impact of<br />
new information such as the start of El Niño is not performed by the modelling<br />
firms. Users of the models might have a view on the adjustments to<br />
parameters that have to be made, but are unlikely to be able to properly<br />
incorporate these changes in the standard modelling tools. This area is ripe<br />
for improvement; new approaches are expected to be developed in the near<br />
future. For now, some use adjustments made primarily on judgement. These<br />
adjustments might or might not be implemented at the assumptions level,<br />
as opposed to modifying the results of modelling.<br />
<br />
The ability to reflect short-term frequency and severity effects of atmospheric<br />
processes to properly assess risk is an advantage in trading<br />
catastrophe bonds; it is an even greater advantage in investing in and<br />
trading shorter-term instruments such as ILWs and catastrophe derivatives.<br />
There is also a question of making better predictions of landfall probabilities<br />
and associated losses of tropical storms that have already formed, which is<br />
important in “live cat” trading; but these very short-term predictions have a<br />
low degree of dependence on the macro-scale hurricane frequency effects<br />
described here.<br />
<br />
The discussion about reflecting macro-scale frequency effects in quantifying<br />
the natural catastrophe risk in insurance-linked securities is irrelevant<br />
to most investors, since they do not attempt to make any adjustments. Their<br />
analysis might still capture some of these effects to the degree that the standard<br />
modelling software packages used in catastrophe modelling might<br />
give greater weight to recent years, as opposed to being calibrated based<br />
simply on the long-term historical record of observations.<br />
<br />
While this approach on the part of investors is inadequate and easy to criticise, it<br />
reflects the degree of difficulty of determining and quantifying the effects of<br />
macro-scale atmospheric processes on hurricane activity. A high level of<br />
expertise is required to do it properly, and there is a significant degree of<br />
uncertainty associated with these adjustments.<br />
<br />
<br />
<h3 style="text-align: left;">
EVOLUTION OF INVESTOR VIEWS ON CATASTROPHE MODELLING</h3>
Incorporating short-term effects in catastrophe modelling has grown in<br />
importance over time. Given that, for catastrophe bonds, buy-and-hold used<br />
to be the only investment strategy, modelling was often performed only<br />
once. Investors rarely tried to perform any real modelling and relied fully on<br />
the analytical data in the offering circulars.<br />
<br />
Many did not do even that and<br />
based their investment decisions on other considerations, of which bond<br />
ratings were the most important. Of course, even then there were investors<br />
with deep understanding of insurance-linked securities; however, they<br />
tended to be an exception rather than the rule. Even investors with a high<br />
level of expertise in catastrophe risk, such as reinsurance companies, often<br />
based the decisions on only a rudimentary overview of the summary<br />
analysis provided in the offering circulars.<br />
<br />
Some attempts to revisit the original<br />
analysis would sometimes take place in the context of portfolio<br />
construction, with a single focus on avoiding excessive risk accumulation in<br />
some combinations of geographies and perils. Again, this statement is not<br />
universally applicable, since from the very beginning some of the players in<br />
the ILS market have been very sophisticated.<br />
<br />
As the market has continued to develop, the level of sophistication of<br />
many investors has grown with it, even though a significant disparity<br />
remains. There are some ILS investors who lack any analytical expertise, and<br />
some who believe they understand the analytics while in reality they do not.<br />
In general, however, the current landscape is very different from what it<br />
was in the beginning of the cat bond market. There are more new issues and<br />
bonds outstanding. There is a sizable and growing secondary market for<br />
catastrophe bonds.<br />
<br />
This creates new opportunities for portfolio rebalancing<br />
and optimisation. In addition, the ILW market has grown significantly.<br />
Catastrophe derivative markets have reappeared and are growing as well.<br />
Investors able and willing to take part in these markets and not be confined<br />
to investing in catastrophe bonds have new options to generate higher riskadjusted<br />
return by investing in catastrophe risk insurance-linked securities.<br />
Direct hedging can be done in managing an ILS portfolio. The markets<br />
remain inefficient and liquidity insufficient, but the array of options available<br />
to investors has certainly expanded.<br />
<br />
The ability to better model the risk has always been important in the<br />
analysis of individual securities. The better tools now available for this<br />
modelling have given investors a greater degree of confidence in the<br />
analysis and opened new options not available several years ago.<br />
<br />
<br />
<h3 style="text-align: left;">
RELATIONSHIP BETWEEN ILS INVESTOR SOPHISTICATION AND THE LEVEL OF ILS ANALYTICAL EXPERTISE</h3>
There is an obvious connection between the level of investor sophistication<br />
and the ability to analyse the securities being invested. However, investing<br />
in insurance-linked securities without being able to fully analyse them does<br />
not necessarily put an investor in the “naïve” category.<br />
<br />
There could be very good reasons for arriving at a well-thought-out decision not to expand<br />
resources on developing internal expertise in insurance-linked securities,<br />
but instead to allocate a small percentage of the overall funds to this asset<br />
class without performing in-depth analysis.<br />
<br />
One of the reasons could be the diversifier role that insurance-linked securities can play in a portfolio. Given a very small percentage allocation to ILS, for some investors the<br />
cost–benefit analysis might not justify developing an expertise in this asset<br />
class, though they may still have sufficient reasons for investing in ILS.<br />
<br />
An even more important development stemming from the advances in modelling catastrophic events is the ability to better model and optimise portfolios of catastrophe insurance-linked securities. The new options available to investors – more new issuances; the development of secondary markets in catastrophe bonds, combined with a greater number of<br />
outstanding bonds; the availability of ILWs and catastrophe derivatives,<br />
both exchange-traded and over-the-counter – have also increased the need<br />
for models that can be used in portfolio and risk management.<br />
<br />
The shift from the buy-and-hold investment strategy as the only available option to<br />
the ability, no matter how limited, to optimise and actively manage a portfolio<br />
of insurance-linked securities is a sea change for a sophisticated<br />
investor. Modelling insurance-linked securities on a portfolio basis has<br />
increased the emphasis on modelling.<br />
<br />
Some of the new modelling tools developed specifically for investors are described later in this article. A sophisticated investor can also take advantage of the live cat trading<br />
opportunities arising when a hurricane has already formed and is threatening<br />
an area that has significant insurance exposure. Short-term forecasts<br />
can then be combined with broader portfolio modelling to take advantage<br />
of the opportunities to take on risk at attractive prices, or to offload excess<br />
risk in the portfolio.<br />
<br />
So far, very little live cat trading has been done, but at least some growth in this area is expected. Improvement in the ability to model catastrophe risk contributes to the<br />
development of the ILS markets. Enhanced tools give investors a higher<br />
degree of confidence and open up new options.<br />
<br />
At this point, however, most investors do not utilise the tools already available, and many make their investment decisions based primarily on judgement and a back-of-the-envelope<br />
type of analysis. While there are some extremely sophisticated players<br />
in this market, there is significant room for improvement in investor understanding<br />
and modelling of catastrophe insurance-linked securities.<br />
<br />
<br />
<h3 style="text-align: left;">
ELEMENTS OF HURRICANE MODELLING</h3>
<i>Doubt is not a pleasant condition, but certainty is absurd.<br /><b>Voltaire</b></i><br />
<br />
There is a very high degree of uncertainty associated with hurricane losses.<br />
It surrounds all elements of a hurricane model – from the frequency and<br />
location of storm formation to its tracks and intensity, and the possible landfall<br />
and resulting insured losses. The very high degree of uncertainty has<br />
been a continuing source of frustration for many investors who rely on the<br />
output of black-box-type modelling tools such as the analysis summarised<br />
in offering circulars for cat bonds.<br />
<br />
It is even more frustrating for those fewinvestors for whom the modelling tools are not black boxes and who understand the assumptions and the modelling of individual processes within the broader analytical framework. Their superior understanding does not eliminate<br />
the uncertainty and might even increase the perception of the degree<br />
of uncertainty in their minds.<br />
<br />
We need to keep in mind that the obvious uncertainty involved is not unique to insurance-linked securities tied to catastrophe risk: to some degree it is present in any security and financial instrument. Insurance-linked securities are unique in the type of risks they<br />
carry; they are not unique in the carrying of risk per se. Every security carries<br />
some degree of risk, uncertainty and unpredictability; assuming the risk is<br />
what investors are paid for. In the case of insurance-linked securities, one of<br />
the ways to reduce the uncertainty is to improve the modelling of hurricanes<br />
and the damage they cause.<br />
<br />
There exists a considerable body of research on modelling atmospheric<br />
phenomena such as storms and hurricanes. Catastrophe models used in the<br />
insurance industry and in the analysis of insurance-linked securities are<br />
based on some of this research, as described earlier.<br />
<br />
A comprehensive overview of the atmospheric science on which the commercial models are<br />
based would take up a thick volume and cannot be provided here. In most<br />
cases, understanding all of the science is completely unnecessary for an<br />
investor analysing insurance-linked securities. It is important, however, to<br />
have some basic understanding of the science and assumptions used in catastrophe<br />
software packages and avoid treating these tools as black boxes that spit out results based on user input.<br />
<br />
Among the many advantages of understanding<br />
the basics of the science and assumptions used by the models is the<br />
ability to better understand the sensitivity of results and the degree of uncertainty<br />
involved. Another important advantage is understanding some of the<br />
differences between the models.<br />
<br />
Some elements of the modelling of hurricane risk and related basic scientific<br />
concepts are discussed below. They are not intended to educate a reader<br />
on the hurricane science as such, or even its use in commercial catastrophe<br />
models: rather, the purpose is to provide an illustration of how the models<br />
work, by describing selected issues relevant to the topic.<br />
<br />
<h3 style="text-align: left;">
Modelling hurricane frequency</h3>
The number of storms in a hurricane season can be simulated by sampling<br />
from the hurricane frequency distribution. When the frequency of hurricanes<br />
or hurricane landfalls is modelled directly, there are three main<br />
choices for the probability distribution:<br />
<ul style="text-align: left;">
<li>Poisson;</li>
<li>negative binomial; and</li>
<li>binomial.</li>
</ul>
Poisson distribution is the natural first choice as it is for most frequency<br />
distributions. Binomial distribution might be appropriate where the sample<br />
variance is less than the sample mean. This is unlikely to be the case in<br />
events with such a high degree of uncertainty as hurricanes; the fact that<br />
there can be several hurricanes during the same time period further complicates<br />
the use of this distribution.<br />
<br />
In fact, the variance generally exceeds the mean, leading to the recent adoption by many of the negative binomial as the distribution of choice for hurricane frequency. Most of the standard catastrophe models utilise the negative binomial distribution for hurricane<br />
frequency in Florida; some allow users the choice between Poisson and<br />
negative binomial distributions.<br />
<br />
Despite the recent shift towards the use of the negative binomial distribution,<br />
Poisson distribution is still commonly used as well. When considering<br />
the choice of probability distribution for hurricane frequency, parameterisation<br />
might be a bigger issue than the analytical form of the distribution. This<br />
is particularly challenging because of the varying views on the changes in<br />
hurricane frequencies over time.<br />
<br />
In fact, the regime switch view of the hurricanefrequency affects both the choice of the parameters of the distribution and the choice of the distribution itself. It is possible that the statistically significant fact of the sample variance exceeding the sample mean is the<br />
result of inappropriately combining in the same sample unadjusted observations<br />
from time periods that have had different mean hurricane<br />
frequencies due to climate oscillations or other changes.<br />
<br />
If this is the case, the choice of Poisson distribution over the negative binomial might be preferable. In this context, the choice of the distribution is dependent on the choice<br />
of the distribution mean: if it is determined based on the full historical database<br />
of observations, with all observations given the same weight, negative<br />
binomial distribution seems to almost always outperform Poisson in backtesting<br />
regardless of the geographical region being considered.<br />
<br />
<h3 style="text-align: left;">
Hurricane frequency and intraseasonal correlation</h3>
There is an ongoing debate about whether the occurrence of a hurricane, in<br />
particular a major hurricane, during the hurricane season means that there<br />
is a greater probability of another hurricane occurring in the remainder of<br />
the season. In other words, there is a question of whether the frequency<br />
distribution changes if it is conditioned on an occurrence of a hurricane.<br />
<br />
The phenomenon in question is sometimes referred to as hurricane clustering.<br />
The rationale for the view that the probability of hurricanes increases<br />
under these circumstances is that a major hurricane is more likely to develop<br />
if the general atmospheric conditions are more conducive than average to<br />
hurricane formation. This in turn implies a greater-than-otherwise-expected<br />
chance of additional hurricanes during the season.<br />
<br />
In the analysis of insurance-linked securities, the issue of intra-seasonal<br />
correlation is of particular importance for second-event bonds and second event<br />
catastrophe derivatives. Of course, it is important in ILS analysis in<br />
general for valuation purposes as well as for evaluating opportunities in the<br />
catastrophe bond secondary markets. It could be of even greater consequence<br />
in the context of investment portfolio management.<br />
<br />
If the probability of hurricane losses on the US Atlantic coast has increased, it could affect<br />
several securities and have a magnified effect across the portfolio.<br />
In practice, we would be hard pressed to find investors who go through<br />
the process of calculating conditional probabilities of hurricane events. The<br />
standard commercial catastrophe models do not have an easy way to adjust<br />
the probabilities in the middle of a hurricane season based on the occurrence<br />
of an event such as Category 3 hurricane making a landfall in the US or the<br />
Caribbean. There have been attempts to take the intra-seasonal auto correlation<br />
into account in modelling second-event catastrophe bonds.<br />
<br />
A better approach than auto correlation models or making adjustments to the frequency distribution based largely on judgement would be to instead<br />
adjust the atmospheric parameters in the model. If the occurrence of a hurricane<br />
was indicative of changing atmospheric conditions, then the best way<br />
to reflect it in the model is by making changes to these assumptions. The<br />
approaches of using auto correlation methods or of making adjustments<br />
based primarily on judgement are also important.<br />
<br />
<h3 style="text-align: left;">
Wind field modelling</h3>
Storm track modelling and modelling of the characteristics of the storm are<br />
an essential part of the overall hurricane modelling. Characteristics of the<br />
storm at a particular location include central pressure, direction, forward<br />
velocity, maximum winds, air pressure profile and many others.<br />
Some elements of wind field modelling are shown in the following diagram. The<br />
approach shown is just one of many ways to build wind field models.<br />
<br />
The important output of wind field models that is used in insurance catastrophe-<br />
modelling software packages is the wind characteristics after<br />
hurricane landfall, at specific locations where insured exposure is located.<br />
Parameterisation of the models is a challenging task that has the potential<br />
to introduce uncertainty and, in some cases, lead to significant errors.<br />
<br />
While historical observations are used to calibrate and validate the models, the<br />
sample of observed events is not big enough to credibly estimate a large<br />
number of parameters. A very complex and scientifically sound theoretical<br />
wind field model might be completely useless in practice if it requires estimating<br />
a large number of parameters based on empirical data. This<br />
statement is not limited to wind field models and is applicable to most<br />
elements of hurricane modelling.<br />
<br />
<br />
<h3 style="text-align: left;">
Probability distributions of some wind field parameters</h3>
In the same way as there are several wind field models, there is more than<br />
one way to model individual parameters used in these wind field models.<br />
Most wind field models use the same general parameters.<br />
<br />
Below we look at the examples of probability distributions of some of the stochastic parameters, in particular the ones used in the standard commercial catastrophe<br />
models, as these are of most interest to the practitioner.<br />
<h4 style="text-align: left;">
Annual frequency</h4>
Generating storm formation frequency technically is not part of wind field<br />
modelling and comes before it, as does generating hurricane landfall<br />
frequency in most models. Hurricane frequency has been covered above,<br />
<br />
Wind field modelling is a critical part of simulating hurricanes and resulting<br />insurance losses. Various models have been developed; even for the same<br />model, parameterisation differs from one modeller to another. For illustrative<br />purposes, below we show selected elements of one of the wind field<br />models.<br />
<br />Pressure isobars of a cyclone can be modelled as concentric circles<br />around its centre. One of the standard models for the radial distribution of<br />surface pressure is<br />
<br />
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<a href="http://2.bp.blogspot.com/-oFaNDeowJ7U/VcExRBIWYvI/AAAAAAAAasE/2EtylYpsNG4/s1600/radial%2Bdistribution%2Bof.jpg" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" src="http://2.bp.blogspot.com/-oFaNDeowJ7U/VcExRBIWYvI/AAAAAAAAasE/2EtylYpsNG4/s1600/radial%2Bdistribution%2Bof.jpg" /> </a></div>
<div class="separator" style="clear: both; text-align: center;">
<br /></div>
<div class="separator" style="clear: both; text-align: left;">
where p(R) is the pressure at a distance R from the centre of the cyclone, p0<br />is central pressure, Rmax is radius to maximum winds, Dp is the central pressure<br />difference, and B is a scaling parameter reflective of pressure profile.<br />There are a number of models for the Holland parameter B, one of the<br />simplest being B = a + bDp + cRmax , where a, b and c are constant. </div>
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<br /></div>
<div class="separator" style="clear: both; text-align: left;">
In this formulation, dependence on latitude is taken into account indirectly<br />through other parameters. A popular wind field simulation model is based<br />on the gradient balance equation of the following form:</div>
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<br /></div>
<div class="separator" style="clear: both; text-align: center;">
<a href="http://2.bp.blogspot.com/-XOlCX05Lj5Y/VcEyGzwMICI/AAAAAAAAasM/o6hD-oZHHbA/s1600/pressure%2Bdistribution%2Bmodel.jpg" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" src="http://2.bp.blogspot.com/-XOlCX05Lj5Y/VcEyGzwMICI/AAAAAAAAasM/o6hD-oZHHbA/s1600/pressure%2Bdistribution%2Bmodel.jpg" /></a></div>
Vg is the gradient wind speed at distance R from the centre and angle a<br />from the cyclone translational direction to the site (clockwise considered<br />positive), r is the air density, f is the Coriolis parameter and VT is the<br />cyclone translational speed.<br />Using the pressure distribution model described above, we obtain the<br />following formula for gradient wind speed:<br />
<br />
<div class="separator" style="clear: both; text-align: center;">
<a href="http://2.bp.blogspot.com/-VyI-DiBkwcM/VcEyoNW5TLI/AAAAAAAAasU/XsRvn3e9j-k/s1600/gradient%2Bwind%2Bspeed%2Bcalculation.jpg" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" src="http://2.bp.blogspot.com/-VyI-DiBkwcM/VcEyoNW5TLI/AAAAAAAAasU/XsRvn3e9j-k/s1600/gradient%2Bwind%2Bspeed%2Bcalculation.jpg" /></a></div>
<br />
<div class="separator" style="clear: both; text-align: left;">
Gradient wind speed Vg can then be used to determine wind speed at<br />various heights. A number of decay models can be used to simulate the<br />evolution of wind parameters upon landfall. These will be utilised in calculating<br />wind gusts over land, taking into account surface roughness and<br />general topography.</div>
<div class="separator" style="clear: both; text-align: left;">
<br /></div>
<div class="separator" style="clear: both; text-align: left;">
where two functional distribution forms – Poisson and negative binomial –<br />have been described as the most appropriate, with a general shift to using<br />the negative binomial distribution because the variance of observed hurricane<br />frequencies typically exceeds its mean. Parameters of the distribution,<br />whether negative binomial or Poisson, are estimated based on a smoothing<br />technique to account for the low number or lack of observations in most<br />locations.</div>
<div class="separator" style="clear: both; text-align: left;">
<br /></div>
<h3 style="text-align: left;">
Landfall locations</h3>
<div class="separator" style="clear: both; text-align: left;">
If the landfall frequency is estimated directly by location based on one of the<br />methods described above, there is no need to use any distribution to estimate<br />landfall location probabilities. Otherwise, given the general hurricane<br />landfall frequency, the probability of landfall by specific location can be<br />distributed based on smoothing of empirical data or using a physical model.<br />Other approaches can be used as well.</div>
<div class="separator" style="clear: both; text-align: left;">
<br /></div>
<h3 style="text-align: left;">
Central pressure</h3>
<div class="separator" style="clear: both; text-align: left;">
Smoothed empirical distributions can be used for central pressure at and<br />following landfall. The same approach is possible but harder to implement<br />for modelling hurricane central pressure before landfall. While central pressure<br />does not easily lend itself to being described by any standard functional<br />probability distribution, the use of Weibull distribution has produced<br />acceptable fit. Strong hurricanes are much rarer than the weak ones, and the<br />Weibull distribution, with properly chosen parameters, captures this relatively<br />well. </div>
<div class="separator" style="clear: both; text-align: left;">
<br /></div>
<div class="separator" style="clear: both; text-align: left;">
<br /></div>
<h3 style="text-align: left;">
Forward speed</h3>
<div class="separator" style="clear: both; text-align: left;">
Smoothed empirical distribution specific to a landfall gate is one of the<br />choices for modelling hurricane forward speed. Similar to the central pressure<br />distribution, that of forward speed is skewed, with very fast forward<br />speeds being much less common than slower speeds. However, based on<br />historical observations, the degree of skewness is generally lower.<br />Lognormal distribution is a good choice for modelling storm forward speed<br />in most situations.</div>
<div class="separator" style="clear: both; text-align: left;">
<br /></div>
<h3 style="text-align: left;">
Radius to maximum winds</h3>
<div class="separator" style="clear: both; text-align: left;">
Lognormal distribution can be used for modelling Rmax, with its parameters<br />depending on central pressure and location latitude. The lognormal distribution<br />needs to be truncated to avoid generating unrealistic values of Rmax.</div>
<div class="separator" style="clear: both; text-align: left;">
<br /></div>
<div class="separator" style="clear: both; text-align: left;">
Gamma distribution has also been used for stochastically generating radius<br />to maximum winds, producing acceptable results when limited to modelling<br />the Rmax variable at landfall as opposed to including its modelling over<br />open water. Another way to generate Rmax values is by using one of the<br />models where logarithm of Rmax is a linear function of central pressure<br />(and/or its square) and location latitude. </div>
<div class="separator" style="clear: both; text-align: left;">
<br /></div>
<div class="separator" style="clear: both; text-align: left;">
Coefficients in the linear relationships are determined based on empirical data. Then Rmax is not simulated directly, but rather is calculated as a function of latitude and the simulated<br />value of central pressure. Other models can also be used.<br />These are just some of the random variables simulated in catastrophe<br />models. Many others need to be modelled, including such important ones as<br />wind dissipation overland, in order to ultimately derive hurricane physical<br />parameters after a landfall.</div>
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<br /></div>
<div class="separator" style="clear: both; text-align: left;">
DAMAGE MODELLING<br />In catastrophe models, the next step after simulating physical effects of a<br />hurricane (such as peak gusts and flood depth at specific locations) is determining<br />the damage they cause. Conceptually, this process is very<br />straightforward. It involves the following basic steps:<br />1. For each individual location in the insured exposure database,<br />consider</div>
<div class="separator" style="clear: both; text-align: left;">
<br /></div>
<ul style="text-align: left;">
<li>simulated physical characteristics of the storm that are relevant to estimating potential damage;</li>
<li>characteristics of the insured property at the location.</li>
</ul>
<div class="separator" style="clear: both; text-align: left;">
2. Identify the damage functions corresponding to the hurricane’s physical<br />parameters (peak gusts) and the vulnerability classes of insured<br />buildings and contents at the location.<br />3. Apply the damage functions to the replacement value of the insured<br />property to calculate the loss.<br />Detailed information on the insured property is essential for assessing its<br />vulnerability to hurricanes. The information should include the following,<br />in as great detail as possible:</div>
<ul style="text-align: left;">
<li> precise location of the insured property (street address, ZIP code, CRESTA, etc.);</li>
<li>vulnerability characteristics (construction type, height and footprint size, year of construction, occupancy type, mitigating factors, etc.); and</li>
<li> replacement property value.</li>
</ul>
Vulnerability functions are based on historical data and structural engineering<br />analysis. Their details represent a highly proprietary component of<br />commercial catastrophe models that can be a significant differentiator<br />among the models. The exact definition of a vulnerability function is the<br />relationship between the mean damage ratios and the peak gusts, where the<br />mean damage ratio relates the expense of repairing the damaged property<br />to the replacement cost of the property.<br />
<br />Modifications to vulnerability functions or subsets of vulnerability functions<br />can be based on secondary characteristics or mitigation measures such<br />as roof type, roof strength, roof-to-wall strength, wall-to-floor and wall-tofoundation<br />strength, opening protection and others. The variables are<br />largely the same for all models since they are a function of the type of exposure<br />information collected by insurance companies.<br />
<br />
The way vulnerability functions are determined and modified differs, sometimes significantly,<br />from one model to another. Some models use additional variables such as<br />wind duration to better estimate damage to insured property from hurricanes.<br />The fact that damage modelling follows very simple and logical steps<br />does not imply the ease of building a module for its calculation as part of a<br />catastrophe model.<br />
<br />
The effort going into determining and refining vulnerability<br />functions cannot be overestimated. Complex structural engineering<br />studies have been conducted for this purpose and a large amount of historical<br />hurricane damage data has been analysed. This is a continuing process<br />as more precise site information becomes available, building codes change<br />and other developments take place.<br />
<br />
<br />
<h3 style="text-align: left;">
FINANCIAL LOSS MODELLING</h3>
Once the damage for each insured location has been calculated, it can then<br />be translated into the amount of insured loss by applying to it policy terms<br />and conditions including its deductible and limit. Loss triggers, insurance<br />coverage sublimits and other factors are also taken into account in the calculations;<br />for reinsurance purposes, other factors such as attachment point are<br />also part of the loss calculations.<br />
<br />
This process too is very straightforward in<br />its implementation as long as all the necessary data inputs are reliable.<br />Adjustments to the process, when such are required, can introduce a<br />degree of complexity. Adjustments include taking into account demand<br />surge following a catastrophic event.<br />
<br />
<br />
<h3 style="text-align: left;">
WIND AND EARTHQUAKE STRUCTURAL ENGINEERING ANALYSIS</h3>
The ability to estimate potential damage to insured structures depending on<br />the physical characteristics of a hurricane or an earthquake is a challenging<br />structural engineering task. Two separate disciplines, hurricane engineering<br />and earthquake engineering, have developed to deal with engineering<br />aspects of hurricane and earthquake hazards.<br />
<br />
While the broader focus of the disciplines is on designing, constructing and maintaining buildings and infrastructure to withstand the effects of catastrophic events, in insurance<br />catastrophe modelling the emphasis is on quantifying the damage that<br />would result from hurricanes and earthquakes of various intensities. Similar<br />principles can also be applied to the risk of manmade catastrophic events<br />such as acts of terrorism.<br />
<br />Estimating the dependence of mean damage ratios on hurricane peak<br />gusts or earthquake physical characteristics for various types of structures is<br />the process of constructing vulnerability functions, which are an essential<br />part of the damage calculator in insurance catastrophe models.<br />Constructing sets of vulnerability functions for specific geographical areas<br />is necessary to take into account the overall topography, building codes<br />and the history of their change over time, and other factors.<br />
<br />
<br />
<h3 style="text-align: left;">
Demand surge</h3>
A catastrophic event such as a hurricane landfall or an earthquake can result<br />in the increase of costs of repairing the damage and other expenses covered<br />by insurance policies above the level of claim costs expected under normal<br />circumstances. This effect is referred to as demand surge, reflective of the<br />increase in costs being driven by a sharp increase in demand while the<br />supply lags behind.<br />
<br />
An example is the shortage of building materials following a major hurricane, when many properties are damaged and all of them require building materials for restoration, all at the same time immediately following the hurricane. The cost of building materials naturally<br />goes up to reflect the demand–supply imbalance created by catastrophic<br />events.<br />
<br />
The post-event shortage expands to the labour costs, which also<br />affect the cost of rebuilding the damaged property. Additional living<br />expenses can also grow after a large catastrophic event, further contributing<br />to losses suffered by insurance companies.<br />
<br />To account for demand surge, insurance catastrophe models can apply<br />special demand surge or loss amplification factors to insurance losses. The greater the magnitude of a catastrophic event, the greater the demand surge<br />effect. The effect applies to different parts of insurance coverage to different<br />degrees; consequently, demand surge factors differ as well. Sometimes the<br />factors are further refined to reflect the various degrees of the demand surge<br />effect, for example on the cost of rebuilding various types of property.<br />
<br />
<h3 style="text-align: left;">
Aggregate approach</h3>
An aggregate approach, as opposed to the more detailed location-by-location<br />modelling, starts before the financial loss module, in the analysis of<br />hurricane damage. The goal here is to arrive at aggregate insured losses for<br />an individual risk portfolio or even for the whole insurance industry. In this<br />approach, portfolio-level information is used in the calculations to arrive at<br />the loss distribution, as opposed to analysing each individual risk independently<br />and then aggregating the losses across the portfolio.<br />
<br />
Inventory databases of property exposure are utilised to help accomplish this goal,<br />with the data aggregated by location (such as ZIP or postal code) and<br />including information on the types of property, vulnerability degrees, type<br />of coverage, etc. The calculations consider aggregate exposure data by location,<br />estimate the average damage and then translate it into financial losses.<br /><br />
When this is done not for an individual portfolio of a specific insurance<br />company but for the whole insurance industry, the result is a figure for<br />industry-wide losses by geographic area (for example, all of Florida), the<br />probability distribution of which is important for larger primary insurance<br />writers, and even more important for reinsurance companies.<br />
<br />There are other ways to calculate aggregate losses, which are based on<br />more granular analysis and the use of databases of insurance policies from<br />several insurance companies, and then extrapolating the losses to the total<br />insurance industry based on insurance premiums or another measure of<br />exposure. Some modelling companies might have developed such databases<br />by combining data from the companies that provided them with this<br />information.<br />
<br />
In the context of insurance-linked securities, aggregate losses suffered<br />by the insurance industry are important in catastrophe bonds with an<br />industry loss trigger, in industry loss warranties (ILWs) and in catastrophe<br />derivatives.<br />
<br />
</div>
Unknownnoreply@blogger.com2tag:blogger.com,1999:blog-6468318357328951173.post-25968735814923311172015-08-03T11:12:00.000-07:002015-08-09T12:03:18.066-07:00Modelling Catastrophe Risk Part 1<div dir="ltr" style="text-align: left;" trbidi="on">
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<a href="http://4.bp.blogspot.com/-7hwJkK0IpCo/Vb-ukHmKcdI/AAAAAAAAaqI/IUlvRBeFcyE/s1600/modelling%2Bcatastrophe%2Brisk.jpg" imageanchor="1" style="clear: left; float: left; margin-bottom: 1em; margin-right: 1em;"><img alt="modelling catastrophe risk" border="0" src="http://4.bp.blogspot.com/-7hwJkK0IpCo/Vb-ukHmKcdI/AAAAAAAAaqI/IUlvRBeFcyE/s1600/modelling%2Bcatastrophe%2Brisk.jpg" title="modelling catastrophe risk" /></a></div>
<h3 style="text-align: left;">
THE CHALLENGE OF MODELLING CATASTROPHE EVENTS</h3>
The very last painting by Salvador Dali was titled The Swallow’s Tail – Series<br />
on Catastrophes. Dali was greatly interested in the catastrophe theory developed<br />
by the French mathematician René Thom, and referred to it as “the<br />
most beautiful aesthetic theory in the world”. Thom’s catastrophe theory<br />
describes how small changes in parameters of a stable nonlinear system can<br />
lead to a loss of equilibrium and dramatic, on the level of catastrophic,<br />
change in the state of the system.<br />
<br />
Thom described equilibrium topological<br />
surfaces and corresponding discontinuities that exist under certain conditions.<br />
An equilibrium state is associated with the minimum of its potential<br />
function; according to the catastrophe theory, a phase transition or a discontinuity<br />
can be associated with only a limited number of stable geometric<br />
structures categorising degenerate critical points of the potential function.<br />
<br />
The Swallow’s Tail includes two of the so-called elementary catastrophes<br />
taken directly from Thom’s graphs: the swallowtail and cusp geometries.<br />
Dali was captivated by the catastrophe theory, especially after he met Thom.<br />
Topological Abduction of Europe – Homage to René Thom, an earlier painting by<br />
Dali, even reproduces in its bottom left corner the formula describing the<br />
swallowtail elementary catastrophe geometry.<br />
<br />
There have been numerous attempts to apply the catastrophe theory to<br />
describing and predicting physical events. Returning from art to science, we<br />
are faced with the challenge of assessing the frequency and severity of<br />
natural and man made catastrophes that can lead to massive insurance<br />
losses. The challenge is daunting, and developing a model to accomplish<br />
this goal is a very practical task – with no surrealistic elements, even if the<br />
results of catastrophes can often appear surreal.<br />
<br />
This article introduces important concepts in modelling catastrophic events for the purpose of<br />
analysing insurance risk securitisation. Issues examined here provide an<br />
understanding of why modelling catastrophe risk is essential and why it is often so challenging.<br />
<br />
<h3 style="text-align: left;">
Predicting the unpredictable</h3>
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Catastrophic events are impossible to predict. The only way to analyse these<br />
events and their impact on insured losses is within a probabilistic framework.<br />
Catastrophe modelling has evolved in recent decades: its role in<br />
quantifying insurance risk is critical and credible. The credibility of the<br />
modelling tools continues to grow as they incorporate more and more of the<br />
latest scientific research on catastrophic events and the insurance-specific<br />
data that determines the impact of the catastrophes on insurance losses.<br />
<br />
<h3 style="text-align: left;">
IMPORTANCE OF CATASTROPHE MODELLING TO INVESTORS</h3>
Wherever the payout on insurance-linked securities is tied to the possible<br />
occurrence of insured catastrophe losses, catastrophe modelling is the most<br />
important tool for investors in analysing the risk of the securities and determining<br />
the price at which they would be willing to assume this risk.<br />
<br />
Superior ability to model insurance risk of catastrophic events is a source<br />
of competitive advantage to investors in securities linked to such risk. This<br />
ability can serve as an important differentiator and an indispensable tool in<br />
a market that remains inefficient and suffers from the problem of asymmetric<br />
information and general information deficiency.<br />
<br />
The chapter on catastrophe bonds provided a brief overview of the structure<br />
of the models used in analysing the insurance risk of property<br />
catastrophe securitisations; it also examined important outputs such as<br />
exceedance curves that specify probabilities of exceeding various loss levels.<br />
It is equally important to understand inputs to the models.<br />
<br />
The seemingly straightforward task of understanding the results, such as<br />
interpreting the risk analysis included in the offering documents for cat<br />
bonds, is actually the most important and the most challenging. If the<br />
modelling software is a complete black box to an investor, any analysis of its<br />
output is limited and deficient.<br />
<br />
Not understanding the modelling tools also detracts from the usefulness of the sensitivity analysis that might be included in the offering documents; it makes it difficult to make any adjustments to improve on what is included in the documents.<br />
<br />
It is unrealistic for most investors to become familiar with the inner<br />
workings of catastrophe modelling software to get a better insight into<br />
the risk involved in insurance-linked securities. The cost–benefit analysis<br />
does not justify developing such expertise in house. Only true specialists<br />
can afford this luxury. However, it is beneficial to any investor in catastrophe<br />
insurance-linked securities to be familiar with the basic methodology of modelling catastrophe risk. This, at the very least, will allow investors to interpret the data in the offering circulars on a more sophisticated<br />
level.<br />
<br />
<h3 style="text-align: left;">
MODELLING CATASTROPHE INSURANCE RISK OF INSURANCE-LINKED SECURITIES</h3>
The article on catastrophe bonds provided an overview of the modern<br />
catastrophe modelling technology and described the main modules of a<br />
catastrophe modelling software provided by the three recognised independent<br />
providers of insurance catastrophe modelling services, AIR<br />
Worldwide, EQECAT and Risk Management Solutions (RMS). The chapter<br />
also introduced concepts such as exceedance probability curve and return<br />
period, and included a summary of sensitivity analysis and stress testing<br />
that can be performed in evaluating insurance-linked securities.<br />
<br />
The output of a catastrophe model is based on thousands or even millions<br />
of years of simulated natural events and their financial impact on a given<br />
insurance portfolio. This output can then be used to determine the probability<br />
distribution of cashflows for a catastrophe bond or another security<br />
linked to the risk of catastrophic events.<br />
<br />
In fact, the modern models are not limited to natural catastrophes: models<br />
of manmade catastrophes have also been developed. For example, terrorism<br />
models have been developed to model the risk of catastrophe losses<br />
resulting from such acts.<br />
<br />
In this article, more information on the practical ways to model the<br />
cat risk of ILS is added, along with a description of the available modelling<br />
tools, their benefits and their limitations. First, however, the basics of<br />
the science of natural catastrophes are described, since they form the framework<br />
for the generation of catastrophe scenarios used by these software<br />
tools.<br />
<br />
<h3 style="text-align: left;">
THE SCIENCE OF CATASTROPHES</h3>
It is neither possible nor necessary for an investor to have in-house experts<br />
on the actual science underlying catastrophe models; basic understanding,<br />
however, at the very least allows us to ask the right questions and to bring<br />
a degree of transparency to the black-box view of the models.<br />
<br />
Seismology is the study of earthquakes and the physical processes that<br />
lead to and result from them. In the broader sense, it is the study of earth<br />
movement and the earth itself through the analysis of seismic waves.<br />
Earthquake prediction per se is not possible, but it is possible to identify<br />
probabilities of earthquakes of specific magnitude by geographic region; in<br />
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some cases, there are precursors that might be useful in short-term forecasting<br />
as well.<br />
<br />
Climatology and meteorology are the study of weather and atmospheric<br />
conditions, with the latter focused on the short-term analysis of weather<br />
systems and the former on the long-term analysis of weather patterns and<br />
atmospheric phenomena.<br />
<br />
The study of catastrophic weather events such as<br />
hurricanes is a specialised branch of this science. In recent years, significant<br />
progress has been made in understanding the dynamics of weather-related<br />
catastrophes, and in assessing both long-term and short-term probabilities<br />
of such events.<br />
<br />
Structural engineering and several related fields permit the analysis of<br />
damage to physical structures given the occurrence of a specific natural catastrophe.<br />
This analysis is important for assessing insurance losses that can<br />
result from a catastrophe such as hurricane or earthquake.<br />
Epidemiology and medicine offer yet another example of study of catastrophes,<br />
examining pandemic-type catastrophe events and their impact on<br />
the population.<br />
<br />
Manmade catastrophes are as difficult to predict as those caused by<br />
nature; disciplines ranging from structural engineering to political science<br />
can provide input into creating a probabilistic model of this type of catastrophic<br />
events.<br />
<br />
<h3 style="text-align: left;">
EARTHQUAKE FREQUENCY AND SEVERITY</h3>
A simple relationship between earthquake frequency and magnitude is<br />
described by the Gutenberg–Richter law. It states that, for a given long<br />
period of time in a certain region, the number N of earthquakes of magnitude<br />
M or greater follows the power law<br />
<br />
N(M) = 10a–bM,<br />
<br />
which can alternatively be written as log N(M) = a – bM, where a and b are<br />
constant. b usually, but not always, falls in the range between 0.8 and 1.2.<br />
This relationship, specifying that an earthquake magnitude has a left-truncated<br />
exponential distribution, holds surprisingly well for many territories<br />
and earthquake magnitudes.<br />
<br />
It can be used to obtain rough estimates of the<br />
probability of earthquakes, even of magnitudes not observed, based on the<br />
observations of earthquakes of other levels of magnitude.<br />
<br />
Another important relationship is the Omori–Utsu law,1 which describes<br />
the aftershock frequency of an earthquake. According to the Omori–Utsu<br />
law, the rate of aftershocks decays after the main shock as<br />
<br />
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where n(t) is the aftershock frequency at time t after the main shock, and K,<br />
c, and p are constant. The c constant is the time-offset parameter describing<br />
the deviation from the power law immediately after the main shock. The<br />
Gutenberg–Richter law can be used to describe the distribution of aftershocks<br />
by magnitude, which shows that the aftershock magnitude decay<br />
can also be described by a power law.<br />
<br />
The Reasenberg–Jones model combines the Guttenberg–Richter and Omori–Utsu laws to describe the intensity both of the main shock of an earthquake and its aftershocks.<br />
<br />
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<br />
According to Bath’s Law, in an earthquake, the difference in magnitude<br />
between the main shock and its strongest aftershock is constant and independent<br />
of the earthquake magnitude. All of these models should be<br />
considered in a probabilistic framework.<br />
<br />
It is important to note that the scientific definition of aftershocks,<br />
according to which they can happen years or decades after the main shock,<br />
differs from the insurance definition, which has a very narrow time range<br />
for what constitutes an earthquake event.<br />
<br />
Insurance-linked securities such as catastrophe bonds follow the same narrow definition of an earthquake, with aftershocks having to fall within a defined short period of time after the<br />
main shock; otherwise, an aftershock might be considered a separate earthquake<br />
event, and in that case it might have different coverage terms, it might<br />
not be covered at all, or it might trigger second-event coverage.<br />
The basic phenomenological laws such as the Gutenberg–Richter and<br />
<br />
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Omori–Utsu relationships are more accurate than their simple form would<br />
suggest. However, such simple laws are obviously insufficient for modelling<br />
earthquakes, and several more sophisticated models have evolved for<br />
this purpose.<br />
<br />
<h3 style="text-align: left;">
EARTHQUAKE LOCATION</h3>
The vast majority of earthquakes occur on tectonic plate boundaries; though<br />
some, typically smaller ones, do occur within the plates. Earthquakes within<br />
the tectonic plates usually happen in the zones of fault or weakness, and<br />
occur only in response to pressure on the plate originating from its<br />
boundary.<br />
<br />
The three categories of tectonic plate boundaries are spreading<br />
zones, transform faults and subduction zones, each of which can generate its<br />
own type of earthquake. Most spreading zones and subduction zones are in<br />
the ocean, while transform faults can occur anywhere and are among the<br />
best studied.<br />
<br />
A global map of tectonic plates is presented in Figure 4.3, overleaf; it<br />
shows the main tectonic plates and the boundary lines between them.<br />
The hypocentre, where a rupture happens, is typically not very deep<br />
under the earth’s surface for transform faults. In other words, the distance<br />
between the hypocentre and the epicentre is relatively small.<br />
<br />
Compressional and dilatational movements tend to follow straight patterns, at least for<br />
“simple” earthquakes such as those that involve limited changes to the original<br />
earthquake slip. The study of faults plays a major part in determining<br />
the probability distributions of earthquakes in different areas.<br />
<br />
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Seismic hazard maps illustrate the distribution of earthquake shaking<br />
levels that have a certain probability of occurring. Figure 4.4, opposite,<br />
shows the US national seismic hazard map that displays shaking levels,<br />
expressed as peak ground acceleration (PGA), at the probability level of 2%<br />
over the period of 50 years. Other maps developed by the US Geological<br />
Survey (USGS) correspond to the 5% and 10% probability of exceedance<br />
over the 50-year period. The map shown was developed in 2008; the USGS<br />
produces a fully revised version of the national seismic hazard maps<br />
approximately once every six years.<br />
<br />
The national seismic hazard maps are<br />
important in insurance catastrophe modelling even if the modellers disagree<br />
with the methodology used in developing the maps: the maps form the basis<br />
for many building codes, which in turn determine the level of property<br />
damage in case of an earthquake of a certain magnitude.<br />
<br />
The two main types of earthquake models are fault- and seismicity-based.<br />
The fault-based models rely on fault mapping; each known fault or fault<br />
segment has a statistical function associated with the recurrence time for<br />
earthquakes of specific magnitude.<br />
<br />
In the simplest case, it is assumed that following an earthquake at a fault, stress on the fault has to be “renewed” by the tectonic processes until the next earthquake occurs. This view, while<br />
fully stochastic, implies a certain degree of regularity of earthquakes that<br />
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leads to quasi-periodicity of earthquake occurrence. This is why fault-based<br />
models are also referred to as renewal models. Poisson, Weibull, gamma or<br />
lognormal distributions can be used in modelling time between earthquakes,<br />
even though other arrival process distributions are sometimes<br />
utilised as well.<br />
<br />
The Poisson renewal process, with an exponential distribution<br />
of recurrence times, is the simplest but probably least accurate. In its<br />
simplest form the Poisson fault-based model is time-independent. In<br />
contrast to the fault-based models, seismicity-based models assume that<br />
observed seismicity is the main indicator of the probability of future earthquakes.<br />
The use of the Gutenberg–Richter law or a similar relationship then<br />
allows the observed frequency of small earthquakes to be used for estimating<br />
earthquakes of greater magnitude.<br />
<br />
This approach does not require<br />
information on the faults or even knowledge of their existence; it overcomes<br />
a drawback of fault-based models, which can fail because many faults are<br />
not yet mapped correctly, and some are not mapped at all. Seismicity-based<br />
models are also called cluster models: the occurrence of several smaller<br />
earthquakes might signify the coming of a bigger one.<br />
<br />
Renewal processes can be used also for describing clustering events. Aftershock models allow<br />
us to project past seismicity forward to arrive at a time-dependent proba-bility distribution of earthquakes at a specific location. The fault- and seismicity-<br />
based models are not mutually exclusive: elements of both are<br />
employed in modelling, in particular for the better-researched faults for<br />
which there is also more extensive seismicity data available.<br />
Some parts of the world have high levels of earthquake-related insurance<br />
risk. They combine greater probability of earthquakes, due to being situated<br />
on or close to a fault line, and the concentration of insured risk exposure. All<br />
of Japan and part of California are examples of such high-risk areas.<br />
Japan is located in a very seismically active area and has very high density<br />
of population and insured property. Earthquakes in Japan have claimed<br />
many lives and caused significant property damage.<br />
<br />
The growth in population and property has led to the situation whereby a repeat of one of the<br />
historically recorded earthquakes would now result in enormous losses.<br />
Estimates of the overall (not only the insured) cost of a repeat of the great<br />
1855 Ansei-Edo earthquake today go as high as US$1.5 trillion.<br />
<br />
Tokyo sits at the junction of three tectonic plates: it is located on the Eurasian plate; while<br />
not far from the city the Pacific tectonic plate “subducts” from the east, and<br />
the Philippine Sea tectonic plate “subducts” from the south. Of particular<br />
concern is the plane fragment under the Kanto basin, detached from either<br />
the Pacific or the Philippine Sea tectonic plate, whose position could lead to<br />
a large-magnitude earthquake in the already seismically active region.<br />
Japanese earthquakes have been modelled very extensively, but there<br />
remains a significant level of uncertainty as to the probability distribution of<br />
their frequency and severity.<br />
<br />
This particularly high level of uncertainty has<br />
to be taken into account in any analysis of earthquake risk in Japan.<br />
It has been said that the occurrence of a large-magnitude earthquake in a<br />
densely populated area in California is a question of not if but when. The<br />
San Andreas Fault is situated where the North American tectonic plate and<br />
the Pacific tectonic plate meet, with the North American plate moving southward and the Pacific plate northward. The fault, shown on the next figure goes almost straight through San Francisco, with the city being on the North American plate, slightly to the east of the San Andreas Fault.<br />
<br />
Los Angeles is also situated dangerously close to the fault line, but is located<br />
to the west of it on the Pacific tectonic plate. San Andreas is a transform<br />
fault; transform faults tend to produce shallow earthquakes with the focus<br />
close to the surface.<br />
<br />
A number of studies have concluded that there is a high probability of a<br />
major earthquake at the San Andreas fault system, in particular in its<br />
southern part, where stress levels appear to be growing and where there has<br />
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not been a major earthquake in at least three centuries. The conclusion that<br />
the southern part of the fault has a higher probability of a major earthquake<br />
is not universally accepted. There is an agreement that all areas along the<br />
fault, including San Francisco, which experienced a major earthquake in<br />
1906, are at significant risk.<br />
<br />
<h3 style="text-align: left;">
MORE ON EARTHQUAKE MODELLING</h3>
A numerical simulation approach has been used for modelling earthquake<br />
parameters. The nature of the earthquake phenomenon and its inherent<br />
uncertainty invites the probabilistic approach, and simulation is the natural<br />
way to implement it. Models have been developed for describing ground<br />
motion, stresses at the faults, fault dimensions, rupture velocities and many<br />
other parameters.<br />
<br />
The sheer number of unknowns and random variables involved in simulating<br />
earthquakes leads to attempts to simplify the problem by focusing on<br />
only major factors affecting the development of earthquakes, and by using<br />
phenomenological laws in place of direct simulation for some variables. The<br />
results have been mixed.<br />
<br />
While every one of the existing models and<br />
approaches is incomplete, relies on many simplifying assumptions and<br />
could be easily criticised, there has not yet emerged a way to adequately<br />
simulate such complex natural phenomena as tectonic developments and<br />
earthquakes.<br />
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<br />
Even though the numerical simulation approach is generally the best to<br />
portray the behaviour of complex systems, incorrectly specifying some of<br />
the variables or the interdependences among the variables can lead to incorrect<br />
results. Even simpler approaches, by necessity neglecting interdependence<br />
of some of the variables involved, are very challenging to<br />
implement.<br />
<br />
Fitting distributions to variables such as the recurrence times of<br />
major earthquakes is a common approach. It still leaves a lot of room for<br />
uncertainty even as far as the choice of the probability distribution to be<br />
<div style="text-align: center;">
<b><br /></b></div>
<div style="text-align: center;">
<b>Simulating earthquakes: ground motion in Santa Clara Valley,<br />California, and vicinity from M6.7-scenario earthquakes and greater</b></div>
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fitted. As an example, Weibull distribution can be used to simulate earthquake<br />
occurrence times in the following way<br />
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expressing the cumulative probability of an earthquake happening at time t<br />
after the last earthquake, conditioned on there having not been an earthquake<br />
for a period of time t0 since the last earthquake.2 Parameters t and b<br />
are fitted to the distribution based on available data.<br />
<br />
Epidemic-type aftershock sequence (ETAS) models are the most common<br />
of the aftershock models mentioned above. They assume that each daughter<br />
earthquake resulting from a parent earthquake has its time of occurrence<br />
and magnitude distributed randomly but generally based on the<br />
Gutenberg–Richter and Omori–Utsu laws.<br />
<br />
Each daughter earthquake is a parent to the next generation of earthquakes. If the first-generation aftershock is greater in magnitude than the main shock, it becomes the main<br />
shock, and the shock previously considered to be the main shock becomes a<br />
foreshock. The branching aftershock sequence (BASS) model further<br />
imposes Bath’s Law in a modified form for the generation of earthquake<br />
sequences.<br />
<br />
Simulations based on the BASS model are often unstable; this<br />
practical difficulty can be overcome by imposing additional constraints on<br />
simulations. BASS models are seen as providing a better description of aftershock<br />
sequences than the standard ETAS models.<br />
<br />
A superior approach (though harder to implement) is not to impose a<br />
specific probability distribution on the recurrence time variable, but<br />
instead to simulate the physics of fault interaction, reflecting the correct<br />
topology and process dynamics. The earthquake recurrence times are<br />
then the output of that simulation process and do not follow any formulaic<br />
distribution.<br />
<br />
The models are evolving, and the ultimate goal is to create a complete<br />
model of earthquake generation based on the simulation approach.<br />
Advances in geophysics and computing make it possible to move closer to<br />
this goal. Creating a complete earthquake generation model requires simultaneous<br />
simulation of many interrelated processes involved in earthquake<br />
generation.3<br />
<br />
Large-scale supercomputer simulations are opening doors to<br />
creating models that incorporate the latest advances in earthquake physics<br />
and physical observations related to specific faults. Results of research<br />
coming from the Earth Simulator supercomputing project and other institutions<br />
have already been sufficiently valuable to be reflected in some modelling software used to analyse the risk embedded in insurance-linked<br />
securities.<br />
<div style="text-align: left;">
<br /></div>
<h3 style="text-align: left;">
TSUNAMIS</h3>
Tsunamis are caused by underwater seismic events such as regular earthquakes,<br />
volcanic explosions and landslides. They can also be caused by<br />
meteorites or underwater nuclear explosions. Since the causes of tsunamis<br />
are usually earthquakes, the study of tsunamis is closely related to earthquake<br />
science. Mapping potential earthquake locations and estimating<br />
probability of earthquakes of various magnitude at these locations is an<br />
important part of the tsunami threat analysis. Another part is estimating the<br />
impact of a tsunami caused by an earthquake with known location, magnitude<br />
and other characteristics.<br />
<br />
Tsunami modelling involves three parts corresponding to the three stages<br />
of a tsunami: wave generation, propagation and inundation. Propagation<br />
modelling attempts to produce stochastic scenarios of tsunami waves’<br />
speed, length, height and directionality. (Even though tsunami waves<br />
spread in all directions, there is often one direction that exhibits tsunami<br />
beaming, or the higher wave heights.)<br />
<br />
Modelling of run-up, which is a term used to describe the level of increase<br />
in sea level when the tsunami wave reaches shore, requires good knowledge<br />
of underwater topography close to shore. Far-field tsunami wave trains<br />
might result in greater inundation than waves of the same run-up heights<br />
<br />
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generated by an underwater earthquake or landslide located close to the<br />
area of inundation.<br />
A number of models for simulating tsunami events have been developed,<br />
and to a significant degree validated. Databases of pre-computed scenarios<br />
have been created for such tsunami-prone areas as Hawaii and Japan. Highresolution<br />
models are extremely useful in estimating an impact of a tsunami<br />
on insured properties.<br />
<br />
<h3 style="text-align: left;">
HURRICANES</h3>
Hurricanes represent the main natural catastrophe risk embedded in insurance-<br />
linked securities such as catastrophe bonds. A broader term, cyclone,<br />
includes both tropical cyclones (hurricanes, typhoons, tropical storms and<br />
tropical depressions) and extratropical cyclones, such as European wind-<br />
<br />
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storms and Northeasters. North Atlantic hurricanes are the main cyclone<br />
risk transferred to investors in insurance-linked securities, followed by<br />
European windstorms.<br />
The terminology is not consistent even within the same geographical<br />
region. Table 4.2, overleaf, displays the classification based on the criteria<br />
established by the US National Oceanic and Atmospheric Administration<br />
(NOAA). Hurricanes in the Northwest Pacific are usually called typhoons,<br />
while in the southern hemisphere all tropical storms and hurricanes are<br />
referred to as cyclones.<br />
<br />
A number of cyclone scales are in existence to classify cyclones by their<br />
strength. Wind speed is the most important parameter used in the classification<br />
systems, but other parameters are used as well. The scales vary by the<br />
way they measure storm strength and by which oceanic basin is being<br />
considered.<br />
<br />
The hurricane risk in insurance-linked securities is most often that of<br />
hurricanes striking the US, in particular the hurricanes originating in the<br />
Atlantic Ocean. Hence the description below is US-centric; and for this<br />
reason the terminology and analytical tools described here are primarily<br />
those developed by NOAA and in particular its National Hurricane Center.<br />
<br />
While the terminology and some of the characteristics of the hurricanes<br />
differ around the world, the example of the North Atlantic hurricanes<br />
provides a good general illustration, and most of its elements can be applied<br />
to cyclones in other parts of the world. In addition, North Atlantic hurricanes<br />
are arguably the best researched and documented, with numerous<br />
models having been developed for their analysis.<br />
<br />
Some of the scales used around the world include the Beaufort wind scale<br />
(initially developed for non-hurricane wind speeds but now extended to<br />
include five hurricane categories), Dvorak current intensity (based on satellite<br />
imagery to measure system intensity), the Fujita scale or F-scale (initially<br />
developed for tornadoes but now also used for cyclones), the Australian<br />
tropical cyclone intensity scale (similar to the expanded part of the Beaufort scale) and the Saffir–Simpson hurricane scale.<br />
<br />
The last of these is theprimary scale used by NOAA; it divides hurricanes into five distinct categories outlined in the next table. In the description of the effects of a<br />
hurricane, this scale uses the damage characteristics most appropriate for<br />
the US. When applied to categorising hurricanes in other parts of the world,<br />
only the level of sustained wind speeds would normally be used.<br />
One of the criticisms of the Saffir–Simpson Hurricane Scale has been the<br />
inclusion of specific references to storm-surge ranges and flooding refer <br />
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guish the two scales), which does not have specific references to the level of<br />
storm surge and includes an updated description of the damage effects.<br />
While currently considered experimental, it is likely that the new scale will<br />
become the main hurricane classification tool in the US. The next table provides<br />
the description of the categories in the 2009 Saffir–Simpson Hurricane Wind<br />
<br />
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Scale; minor changes to the description of wind-caused damages are<br />
expected as the new scale is being refined. The new scale represents a move<br />
away from describing the effects of the landfall of a hurricane of a certain<br />
category, towards relying on sustained wind speed as the primary determinant.<br />
Any effect of the expected minor adjustments to the description of<br />
wind-caused damages in the NOAA 2009 Saffir–Simpson Hurricane Wind<br />
Scale are likely to be negligible from the point of view of sponsors of and<br />
investors in insurance-linked securities.<br />
<br />
It is noteworthy that there is no Category 6 in the Saffir–Simpson scale<br />
since Category 5 is unbounded. A super-hurricane is not an impossibility,<br />
and wind speeds can exceed 200 mph. One of the main reasons the scale<br />
stops at Category 5 is that the damage at landfall is truly catastrophic, and<br />
there would be little difference between Category 5 and a hypothetical<br />
Category 6. The correctness of this logic is open to debate.<br />
<br />
<h3>
HISTORICAL FREQUENCY OF HURRICANES THREATENING THE US</h3>
<i>Lisa: Dad! I think a hurricane’s coming!<br />Homer: Oh, Lisa! There’s no record of a hurricane ever hitting Springfield.<br />Lisa: Yes, but the records only go back to 1978, when the Hall of<br />Records was mysteriously blown away!<br /><b>The Simpsons</b></i><br />
<br />
<i><b> </b></i><b>For rare events, samples of observed values tend to be very small, leading to<br />a considerable degree of uncertainty in estimating their probability of occurrence.<br />Major hurricanes certainly fall in the category of such events.</b><br />
<br />
NOAA 2009 Saffir–Simpson Hurricane Wind Scale (currently considered<br />
experimental)<br />
<br />
<br />
<table border="1">
<tbody>
<tr><td><b>Hurricane<br />category</b></td><td><b>Sustained<br />wind speed</b></td><td><b>Effects</b></td></tr>
<tr><td>1</td><td>74–95 mph<br />
(64–82 kt or<br />
119–153<br />
km/hr)</td><td>Damaging winds are expected. Some damage to building structures<br />
could occur, primarily to unanchored mobile homes (mainly pre-<br />
1994 construction). Some damage is likely to poorly constructed<br />
signs. Loose outdoor items will become projectiles, causing<br />
additional damage. Persons struck by windborne debris risk injury<br />
and possible death. Numerous large branches of healthy trees will<br />
snap. Some trees will be uprooted, especially where the ground is<br />
saturated. Many areas will experience power outages with some<br />
downed power poles.</td></tr>
<tr><td>2</td><td>96–110 mph<br />
(83–95 kt or<br />
154–177<br />
km/hr)</td><td>Very strong winds will produce widespread damage. Some roofing<br />
material, door and window damage of buildings will occur.<br />
Considerable damage to mobile homes (mainly pre-1994<br />
construction) and poorly constructed signs is likely. A number of<br />
glass windows in high-rise buildings will be dislodged and become<br />
airborne. Loose outdoor items will become projectiles, causing<br />
additional damage. Persons struck by windborne debris risk injury<br />
and possible death. Numerous large branches will break. Many<br />
trees will be uprooted or snapped. Extensive damage to power lines<br />
and poles will likely result in widespread power outages that could<br />
last a few to several days.</td></tr>
<tr><td>3</td><td>111–130<br />
mph<br />
(96–113 kt<br />
or 178–209<br />
km/hr)</td><td>Dangerous winds will cause extensive damage. Some structural<br />
damage to houses and buildings will occur with a minor amount of<br />
wall failures. Mobile homes (mainly pre-1994 construction) and<br />
poorly constructed signs are destroyed. Many windows in high-rise<br />
buildings will be dislodged and become airborne. Persons struck by<br />
windborne debris risk injury and possible death. Many trees will be<br />
snapped or uprooted and block numerous roads. Near-total power<br />
loss is expected with outages that could last from several days to<br />
weeks.</td></tr>
<tr><td>4</td><td>131–155<br />
mph<br />
(114–135 kt<br />
or 210–249<br />
km/hr)</td><td>Extremely dangerous winds causing devastating damage are<br />
expected. Some wall failures with some complete roof structure<br />
failures on houses will occur. All signs are blown down. Complete<br />
destruction of mobile homes (primarily pre-1994 construction).<br />
Extensive damage to doors and windows is likely. Numerous<br />
windows in high-rise buildings will be dislodged and become<br />
airborne. Windborne debris will cause extensive damage and<br />
persons struck by the wind-blown debris will be injured or killed.<br />
Most trees will be snapped or uprooted. Fallen trees could cut off<br />
residential areas for days to weeks. Electricity will be unavailable for<br />
weeks after the hurricane passes.</td></tr>
<tr><td>5</td><td>> 155 mph<br />
(135 kt or<br />
249 km/hr)</td><td>Catastrophic damage is expected. Complete roof failure on many<br />
residences and industrial buildings will occur. Some complete<br />
building failures with small buildings blown over or away are likely.<br />
All signs blown down. Complete destruction of mobile homes (built<br />
in any year). Severe and extensive window and door damage will<br />
occur. Nearly all windows in high-rise buildings will be dislodged<br />
and become airborne. Severe injury or death is likely for persons<br />
struck by wind-blown debris. Nearly all trees will be snapped or<br />
uprooted and power poles downed. Fallen trees and power poles<br />
will isolate residential areas. Power outages will last for weeks to<br />
possibly months.</td></tr>
</tbody></table>
<br />
<b> </b>4.10 illustrates historical frequency of the North Atlantic (NA) and Eastern<br />North Pacific (ENP) named storms, hurricanes and major hurricanes. The<br />data includes all such storm systems and not only those that resulted in<br />landfalls.<br />
<br />Climate changes affect the frequency and severity of hurricanes; the<br />majority of the scientific community holds the opinion that the current probability<br />of major hurricanes in this part of the world, in particular in the<br />North Atlantic, is greater than indicated by historical averages in the observation<br />period, and may be growing. This topic, tied to the subject of global<br />warming, is covered later in this and in other chapters.<br />
<br />
It is important to point out, however, that we do not need to believe in global warming to see<br />climate changes that can have an effect on hurricane activity. There is some<br />disagreement about whether the climate changes affect both the frequency<br />and the severity of hurricanes, and, if they do, whether they affect them to<br />the same degree.<br /><br />
It can be seen that few of the tropical storms become hurricanes, and even<br />fewer develop into major hurricanes. Landfalls are even rarer, but when<br />they happen the results can be devastating. From the point of view of insurance-<br />linked securities analysis, it is the probability of landfall and the<br />subsequent damage that characterise the risk.<br />
<br />
(In rare cases, insurance linked securities can be exposed to hurricane risk even if the hurricanes do not make a landfall. An example would be damage to offshore oil platforms.<br />Still, the risk-exposed areas are likely to be located very close to shoreline.)<br />The figure below shows tracks of observed North Atlantic and Eastern North<br />Pacific hurricanes. Only major hurricanes (Category 3 and greater on the<br />Saffir–Simpson hurricane scale) are shown; tracks and geographical distribution of formation differ by hurricane category. Florida and Texas are the<br />two states with the greatest historical number of hurricane landfalls and<br />damages. Hurricane risk in these two states is significantly higher than elsewhere<br />in the coastal US. <br />
<br />
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The following figure clearly shows the very high probability<br />of major hurricane landfalls in Mexico and the Caribbean. While the<br />majority of ILS hurricane risk in the Americas is in the US, some securities<br />have transferred to the capital markets hurricane risk of other countries in<br />the region, of which Mexico is the best example.<br />
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<br />
<br />
<br />
<br />It has been suggested that the tracks have been, on average, shifting over<br />the decades of observation. If true, this fact may be very important in probabilistic<br />assessment of future hurricanes and their landfall locations.<br />Unfortunately, the data is too limited to be statistically credible, and no solid<br />argument can be made based purely on the observations of historical hurricane<br />tracks.<br />
<br />
<br />
<b> NEXT : Modelling Catastrophe risk part 2</b></div>
Unknownnoreply@blogger.com1tag:blogger.com,1999:blog-6468318357328951173.post-43264099609123939452015-08-03T05:39:00.000-07:002015-08-09T12:04:09.513-07:00Property Catastrophe Bonds and Insurance Risk Part 2<div dir="ltr" style="text-align: left;" trbidi="on">
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<h3 style="text-align: left;">
Return period</h3>
Often, the data is presented in the form of return period instead of exceedance probability. These two terms are closely related. Return period is the average length of time between occurrences of events exceeding a specified threshold. If the annual exceedance probability is 1%, the return period is 100 years.<br />
<br />
As with the probability exceedance curve, we can draw a graph of return period as a function of loss level, and base decisions on the data presented in this format.<br />
<h3>
Stress testing and sensitivity analysis </h3>
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While the quantitative analysis is based almost entirely on the probability exceedance curves produced by catastrophe modelling software, scenario testing is often utilised too. It is used in part as a check on the “black box” software used to model catastrophe insurance losses, and in part as a stresstesting mechanism. For example, for an insurance risk concentrated in Northern California, one might want to estimate the losses that would be incurred if the 1906 San Francisco earthquake happened today.<br />
<br />
Stress testing, by necessity, has to be performed using the same modelling<br />
tools as those used to produce the probability distribution of catastrophe<br />
losses. Since no other tool is available, stress testing often involves moving<br />
along the probability curve and evaluating the results of a catastrophic event<br />
that the model considers less likely.<br />
<br />
Sensitivity analysis could be performed in the standard way of varying<br />
the input parameters of the model and observing the effect on the probability<br />
exceedance curve and losses affecting the cat bond. Ideally, more than<br />
one type of catastrophe modelling software would be used to produce probabilistic<br />
results that could then be compared. While it is sometimes done by<br />
the sponsor of a cat bond, this data rarely finds its way to investors. The socalled<br />
cat bond remodelling process introduced by the three major<br />
catastrophe-modelling firms attempts to alleviate this informational deficiency.<br />
<br />
<h3 style="text-align: left;">
INVESTMENT PERFORMANCE OF CAT BONDS</h3>
Ever since the first cat bonds, insurance-linked securities have been issued<br />
at widely fluctuating yields. Such market inefficiency is normal for any new<br />
type of security, in particular if the market is still developing and lacking<br />
real liquidity. As a group, catastrophe bonds have outperformed many<br />
other securities bearing the same degree of risk, when risk is defined only in<br />
terms of probability of default and loss in the case of default.<br />
(In the cat bond vernacular, these are called “attachment probability” and “conditional<br />
expected loss”.)<br />
<br />
More importantly, their volatility has been lower and correlation<br />
with the markets weaker than for most other fixed-income securities.<br />
This stellar performance, however, suffered in 2008, when there emerged<br />
credit-risk issues in cat bonds (though these were corrected in the newer<br />
structures described in Chapter 7, and when the forced selling by multistrategy<br />
hedge funds temporarily depressed cat bond prices in the<br />
secondary market.<br />
<div style="text-align: left;">
<br /></div>
<h3 style="text-align: left;">
Historical performance</h3>
Figure 3.8, overleaf, shows investment weekly performance of publicly dis -<br />
closed catastrophe bonds relative to the corporate debtwith the same ratings.<br />
<br />
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<h3 style="text-align: left;">
Excess spread</h3>
Spreads for catastrophe bonds have historically exceeded those for comparably<br />rated corporate securities. There are multiple reasons for the extra<br />spreads enjoyed by cat bond investors. The most important of these are the<br />following.<br />
<ul style="text-align: left;">
<li>NOVELTY PREMIUM. </li>
</ul>
This component of the spread accounts for investor<br />unfamiliarity with insurance-linked securities. The novelty premium<br />will eventually disappear as investors educate themselves about catastrophe<br />bonds and as transaction structures become more standard. To<br />some degree, this has already happened.<br />
<br />
<ul style="text-align: left;">
<li>LIQUIDITY PREMIUM. </li>
</ul>
Catastrophe bonds are relatively illiquid. The illiquidity<br />premiumplayed a very important rolewhen the very first cat bonds<br />were issued. At the time, there was virtually no liquidity in the marketplace,<br />and investorswere limited to the buy-and-hold strategy. Over time,<br />however, it has become easier to trade catastrophe bonds.<br />
<br />
Even immediately before hurricane landfall, when evacuation warnings are issued, it is<br />usually possible to buy and sell securities potentially affected by the hurricane.<br />Initially some structurers of insurance-linked securities havemade a<br />special effort to provide liquidity in order to help develop the overall cat<br />bond market.<br />
<br />
While liquidity is now improving, the bid–ask spreads are<br />still relativelywide and some bonds remain largely illiquid.As themarket<br />is growing quickly, both in terms of the number of securities issued and the<br />number of investors, liquidity should continue to improve, reducing the<br />liquidity premium now included in the excess spread.<br />
<br />
<ul style="text-align: left;">
<li>“SUDDEN-DEATH” PREMIUM. </li>
</ul>
A cat bond may have the same rating as corporate<br />debt, but there is a very important difference in the timing of default.<br />The default of a corporate bond is usually preceded by the deterioration<br />of the financial condition of the issuer and gradual downgrades by rating<br />agencies. Sudden defaults are rare. Cat bonds, on the other hand, could<br />default with no prior warning or rating agency downgrade.<br />
<br />
For example, an earthquake could cause an immediate default, resulting in total loss to<br />investors. For some investors, the possibility of a sudden default is unsettling.<br />Certain investors prefer never to see a default in the portfolios, and<br />would sell a security if it is downgraded and chances of default increase.<br />This behaviour is often based on purely psychological factors, with portfolio<br />managers not wanting to be blamed for defaults in their portfolios.<br />
<ul style="text-align: left;">
<li>ASYMMETRIC INFORMATION PREMIUM. </li>
</ul>
<br />
This component of the excess spread is present in cat bonds with indemnity-based triggers. Investors in indem-nity-based cat bonds are at an information disadvantage relative to the<br />insurance company sponsoring the bond. The company has better knowledge<br />than investors of the riskiness of its portfolio of insurance policies.<br />
<ul style="text-align: left;">
<li>RE-RATING PREMIUM (DISCOUNT). </li>
</ul>
Sophisticated investors often do not rely on the ratings assigned to cat bonds by rating agencies. Based on their own analysis, investors may choose to not believe ratings for any security<br />and effectively re-rate them by internally assigning their own ratings for<br />the purposes of pricing and risk analysis.<br />
<br />
This situation is much more common with cat bonds than with other rated securities. Some rating agencies even have caps on ratings assigned to cat bonds. In general,<br />investors tend to believe that cat bonds deserve higher ratings than those<br />assigned by rating agencies. The explanation is that rating agencies, like<br />some investors, might be averse to the situation of sudden default without<br />prior downgrade, and consequently assign ratings to cat bonds based on<br />criteria stricter than those applied to other securities.<br />
<br />
Another differentiator of cat bonds from other securities is the greater average loss given<br />default (LGD) than for most bonds. Many cat bonds, if defaulted, would<br />likely suffer full default with total loss to investors. Since some investors<br />tend to think that the “real” rating is higher than the one assigned by<br />rating agencies, the excess spread is reduced. In other words, this component<br />of the excess spread, if present, would usually be negative.<br />
<br />
It is important to note that, for some catastrophe peril types, the risk during<br />the term of the bond is not uniform. For example, hurricane season in the<br />Caribbean lasts from June till November; the rest of the year, the probability<br />of a hurricane is low. The dependence of risk level on the time period allows<br />us to construct a type of term structure for a catastrophe bond. The non uniformity<br />of the risk distribution over time has a significant effect on<br />pricing levels in the secondary market.<br />
<br />Because cat bond sponsors usually have the option of reinsuring their risk<br />instead of securitisation, the price levels in the reinsurance market have<br />some effect on the cat bond spreads, in particular the original spreads at<br />issue.<br />
<br />Spreads on cat bonds have been subject to significant volatility. Initially<br />very high, they trended downward until the 2005 hurricane season, when<br />demand level increased. The yields increased in 2005 also because questions<br />were raised about the quality of modelling and analysis provided to<br />investors. The reliability and accuracy of the cat modelling software were<br />questioned, resulting in improvements to the models and reassessment of<br />
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the catastrophe insurance risk in general. The previously mentioned difficulties<br />encountered by the cat bond market in the second half of 2008 led to<br />the greatest period of volatility and depressed values. This changed in the<br />first half of 2009, when the new collateral structures and the hardening of<br />catastrophe reinsurance markets led to the renewed growth of the market<br />and more stability in pricing.<br />
<h3 style="text-align: left;">
MARKET STABILITY AND GROWTH</h3>
The first loss in a publicly disclosed catastrophe bond was the Kamp Re<br />transaction, in which the risk of a hurricane was transferred to the capital<br />markets investors. Hurricane Katrina in 2005 caused insurance losses of a<br />level that led to the full loss of interest and principal for Kamp Re investors.<br /><br />
The loss tested the cat bond market, which prior to Hurricane Katrina had<br />not been known to result in losses to investors. In fact, overall, investors<br />have profited handsomely from catastrophe bonds, with spreads usually<br />being significanty over comparably rated corporate bonds. The default of<br />the cat bonds affected by the bankruptcy of Lehman Brothers as the total<br />return swap counter-party was another difficult test for investors.<br />
<br />
The market addressed the issues of credit risk by introducing new cat bond<br />structures The 2004 and 2005 hurricane seasons in the US generated a renewed focus<br />on catastrophe risk management in the insurance and reinsurance industry.<br />The analysis, along with recalibration of catastrophe models, led to the realisation<br />that the risk exposure is far greater than previously believed.<br />
<br />
This created a strong demand for cat bonds and other capital markets solutions<br />on the part of insurers. The demand was boosted by the limited reinsurance<br />capacity for catastrophe risks.<br /><br />
Hurricane Katrina had an additional impact: the payout of the Kamp Re<br />bond to its insurance sponsor clearly demonstrated that cat bonds could<br />provide reliable protection to insurance companies.<br />Fixed-income investors are also increasingly interested in catastrophe<br />bonds and other insurance-linked securities. With investors searching for<br />new types of securities to provide diversification and yield, the growing<br />insurance-linked securities marketplace appears more and more attractive.<br />
<h3 style="text-align: left;">
MORE ON THE SPONSOR AND INVESTOR PERSPECTIVES</h3>
The structure and pricing of a cat bond are an outcome of the process of<br />trying to find a balance between the interests of the sponsor and the<br />investors.<br />
<br />
<h3 style="text-align: left;">
Diversification</h3>
A key reason for investors to buy cat bonds is to diversify their investment<br />portfolios. This is true even for the specialised hedge funds that invest only<br />in insurance-linked securities, since other investors obtain diversification by<br />investing in these funds either directly or through the fund-of-funds mechanism.<br />Cat bonds provide investors with a financial instrument weakly<br />correlated with the equity and fixed-income markets, which has led to cat<br />bonds being called zero-beta securities.<br />
<br />The view that there is no correlation between the performance of cat<br />bonds and that of other securities was initially questioned in the aftermath<br />of Hurricane Katrina. While a typical hurricane would not affect financial<br />markets, a very large catastrophic event such as an earthquake in California<br />could have a shock effect on the economy. In these extreme cases, many<br />types of risk suddenly become highly correlated, even if the degree of<br />dependence is very low under normal circumstances.<br />
<br />
The “zero-beta” view was clearly shown to be invalid by the events of 2008, which uncovered<br />sources of correlation with the markets that had never been appreciated<br />before that period. While the zero-beta view is incorrect in its application to cat bonds, the<br />
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<br />
relatively weak correlation of cat bonds with traditional financial assets is a<br />major source of potential diversification and a strong reason for investors to<br />gain exposure to this asset class. Cat bonds undeniably provide a diversification<br />benefit in addition to affording exposure to a new type of investment.<br /><br />
Within a portfolio of catastrophe bonds and related securities, investors<br />can achieve diversification in a variety of ways. One of them involves<br />building the portfolio with an eye on geographic and peril diversification.<br />Managing portfolios of cat bonds is described in Chapter 16, in the broader<br />context of active management of portfolios comprising various types of catastrophe<br />insurance-linked securities.<br />
<h3 style="text-align: left;">
Slicing and packaging of risk</h3>
A cat bond designed to securitise the risk to an insurance portfolio resulting<br />from a specific natural catastrophe would generally consist of tranches with<br />various degrees of risk. In the example shown in Figure 3.9, if the total loss<br />level exceeds US$750 million, tranche A is activated. As long as the aggregate<br />loss level remains below US$850 million, investors in tranche B and<br />tranche C receive interest and principal in full. Since the loss level is above<br />US$750 million, investors in tranche A suffer the loss of part or entire<br />interest and principal.<br />
<br />To avoid moral hazard, there is usually participation by the sponsor in the<br />excess losses. In structuring terms, this means that not all of the excess risk<br />is reinsured to the SPV, and the sponsor retains a share of potential excess<br />losses.<br />
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Since the tranches have different degrees of risk, they would generally be<br />assigned different ratings, with tranche C as the safest, receiving the highest<br />rating and the lowest spread. It is possible for some tranches to be unrated<br />and others to be rated, in the same transaction.<br />
<br />Another way to slice and package risk is to issue several tranches, with<br />each individual tranche associated with the risk of a specific natural catastrophe<br />in a certain geographic region. Each tranche would have its own<br />trigger; trigger type may even differ from tranche to tranche. A “combo”<br />tranche could also be issued, based on the combination of risks contained in<br />individual tranches. This combination tranche provides diversification to<br />investors unwilling or unable to achieve it on their own. Figure 3.10<br />provides an example of such a structure.<br />
<br />The Successor cat bond issued by Swiss Re in 2006 is a good example of<br />this structure. The Successor programme placed US$950 million of principal-<br />at-risk variable-rate notes, transferring to investors the risks of North<br />Atlantic hurricane, European windstorm, California earthquake and<br />Japanese earthquake in individual and multi-peril tranches.<br />
<br />Another pioneering transaction brought to the market by ABN Amro in<br />2006 was structured as a collateralised debt obligation (CDO) from the very<br />beginning. In fact, it was the first publicly rated CDO of natural catastrophe<br />risk. The CDO offered to investors was based on the cat bonds with industry<br />loss triggers sponsored by the Catlin Group.<br />
<br />
The least risky tranche of the CDO was then rated AA by Standard & Poor’s. Higher ratings open up a new universe of investors who otherwise would have no interest in catastrophe<br />insurance-linked securities. The negative connotation of the term CDO has led to renaming this type of security collateralised risk obligation<br />(CRO). A managed CRO structure was introduced by Nephila Capital in the<br />Gamut transaction developed by Goldman Sachs in 2007. At this point, it is<br />unclear whether CRO structures will be actively used in the future.<br />
<h3 style="text-align: left;">
Types of sponsor</h3>
Catastrophe bonds are generally sponsored by insurance or reinsurance<br />companies. However, corporations can also get protection against natural<br />catastrophe losses by going directly to the capital markets. Tokyo<br />Disneyland’s securitisation of earthquake risk in Japan provides an example<br />of a non-insurance company bypassing the insurance marketplace and<br />going directly to the capital markets to obtain cat protection.<br />
<br />
<br />Many believed that in the future cat bonds would be issued only on behalf<br />of reinsurance companies. This view was based on its being seemingly more<br />efficient for primary insurance companies to reinsure their risk as opposed<br />to sponsoring cat bonds. Reinsurance companies would then accumulate all<br />the risk, and transfer a part of it to the capital markets. This has not<br />happened and we do see cat bonds issued directly by insurance companies.<br />One of the reasons is the credit risk involved in catastrophe reinsurance.<br />
<br />Reinsurance companies are particularly exposed to the risk of natural catastrophes,<br />and might default on their obligations should such an event<br />happen. Cat bonds, on the other hand, provide a fully collateralised protection<br />with little exposure to credit risk.<br />
<br />From the point of view of an investor, the identity of the sponsor of a nonindemnity<br />cat bond is largely irrelevant, with the analysis focused on<br />natural catastrophe modelling performed by the same cat modelling firms<br />as would be modelling insurance company books of business.<br />
<br />
<h3 style="text-align: left;">
Investor types</h3>
While the first major investors in cat bonds were reinsurance companies,<br />now they represent only a small percentage of the overall investor base. A<br />number of specialised hedge funds have been formed for the sole purpose<br />of investing in insurance-linked securities.<br />
<br />
These funds often possess superior expertise and drive the pricing of cat bonds both at issue and in the secondary markets. In addition, many other investors such as pension funds<br />have invested in cat bonds. The number of investors in insurance-linked<br />securities and the total capital committed to this asset class continue to<br />grow.<br />
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<h3 style="text-align: left;">
MODELLING PROPERTY CATASTROPHE INSURANCE RISK</h3>
The reason for including risk analysis in cat bond offering documents is that<br />investors do not have the means to assess default probabilities on their own.<br />This is the case in part because most of them do not possess expertise in<br />determining the likelihood of natural catastrophes and resultant insurance<br />losses. The other reason, applicable to indemnity-type transactions, is that<br />detailed information on the exposure by geographic location is not<br />provided, making it impossible for investors to determine exact default<br />probabilities even if they had superior expertise in analysing insurance risk<br />of hurricanes and earthquakes.<br />
<br />Specialised catastrophe modelling firms play a critical role in the securitisation<br />of insurance catastrophe risk. The modelling software provides the<br />only objective way to analyse the probability of default. It is also the only<br />way for rating agencies to assess the risk and be able to assign a rating to<br />these securities.<br />
<br />The modelling generally includes two components. First, a probabilistic<br />analysis of specific types of natural catastrophes is performed for a certain<br />geographic area. For example, the model could simulate hurricanes in<br />Florida or typhoons affecting Japan. The second step involves assessing the<br />financial impact that these natural catastrophes would have on the portfolio<br />of insurance policies held by the sponsoring insurance company. This<br />assessment is also probabilistic.<br />
<br />
The final output of the model is the probability distribution of the insured losses, which takes into account not only policy conditions and limits, but also the reinsurance structure in place.<br />The damage module is based on structuring engineering input. Its function<br />is to take a specific catastrophe scenario and superimpose it onto a portfolio of insurance policies being analysed.<br />
<br />
The damage calculator takes individual exposures such as insured properties and probabilistically estimates the damage caused by the catastrophe under the scenario, taking into account such parameters as policy limits and deductibles. The output is the<br />insured loss that the company would have to pay out under the scenario.<br />The model runs a large number of scenarios and generates a set of catastrophic<br />losses and probabilities associated with them. <br />
<br />There are only three major recognised independent providers of modelling<br />services for insurance catastrophe exposure. The three companies, AIR<br />Worldwide, EQECAT and Risk Management Solutions (RMS), are primarily<br />software developers for the property-casualty insurance industry. While<br />the RMS model is the most widely used in the industry, AIR is currently<br />leading in providing consulting analytical services for structuring cat bond<br />transactions.<br />
<br />The output of catastrophe modelling software includes the data necessary<br />to construct an exceedance probability curve. The exceedance probability<br />curve could be used for structuring and pricing a cat bond. In structuring, it<br />would help determine the trigger level to provide the needed protection to<br />the insurance company. In pricing, the exceedance probability curve is used<br />to provide a probabilistic look at exceeding the trigger level (that is, bond<br />default) that determines the bond price.<br />
<h3 style="text-align: left;">
TRENDS AND EXPECTATIONS</h3>
The catastrophe bond marketplace is growing and will continue to do so,<br />along with other capital markets mechanisms for transferring catastrophe<br />insurance risk. We are witnessing both an increase in cat bond issuance and<br />growth in the total capital committed to this asset class. Some of the reasons<br />for the growth and its drivers are as follows.<br />
<br />
<ul style="text-align: left;">
<li>The insurance-linked securities market has finally reached the criticalmass needed to make cat bonds a solution always to be considered in evaluating available options in the transfer of insurance catastrophe risk.</li>
</ul>
<ul style="text-align: left;">
<li>The 2004 and 2005 hurricane seasons have led to an increased emphasis on catastrophe risk management. This emphasis has been both internal and external, stimulated by increased scrutiny by the rating agencies and regulators. It has resulted in a demand for additional catastrophe-riskbearing capacity that is not met by traditional reinsurance mechanisms.</li>
</ul>
<ul style="text-align: left;">
<li>The recalibration of catastrophe models post-Katrina has led to the realisation that the insurance industry is exposed to much greater risk of natural catastrophes than previously thought.</li>
<li>The second half of 2008 was the greatest test of the viability and future<br />prospects of the market. The bankruptcy of Lehman Brothers led to the<br />default of cat bonds for which Lehman served as the total-return-swap<br />counterparty. Besides the counterparty risk, these events revealed structural<br />weaknesses in the way collateral arrangements had been made in<br />the standard cat bond structures. Ultimately, however, the market has<br />emerged from this debacle stronger, as the weaknesses were addressed in<br />new structures and all other potential weak points carefully examined.</li>
<li>The depressed values of cat bonds in 2008 caused by the forced selling of<br />cat bonds by multi-strategy hedge funds made the low-correlation (lowbeta)<br />argument slightly weaker, to some degree reducing the<br />diversification value of cat bonds. However, it also highlighted the advantages<br />of this asset class: the multi-strategy funds faced with redemptions<br />were selling cat bonds because they held value better than the great<br />majority of other asset classes.</li>
<li>The educational process in the insurance industry has led to better understanding<br />of cat bonds and other risk-linked securities, allowing insurance<br />and reinsurance companies to see the advantages of the securitisation<br />approach.</li>
<li>Investors, too, have become better educated about catastrophe bonds and<br />the benefits of diversification provided by these securities. The number of<br />investors in risk-linked securities is growing, including the hedge funds<br />focused exclusively on insurance risk.</li>
<li>Structuring of catastrophe bonds has become more standardised, making<br />the process easier for the sponsors and the analysis more straightforward<br />for investors.</li>
<li>The cost of issuance of catastrophe bonds has gone down, due to the<br />standardisation of cat bond structures, the use of multi-year bond terms<br />to spread the cost of issuance over a longer period of time, and shelf<br />registration.</li>
<li>With the growth in the number of cat bonds issued and in the total<br />investor capital, the secondary market for cat bonds and similar securities<br />is growing, too, resulting in greater liquidity. This, in turn, creates greater<br />opportunities for active management of investment portfolios including<br />cat bonds.<br />Other important developments that will affect the future of the market are<br />the following.<br /><h3>
PROPERTY CATASTROPHE BONDS</h3>
</li>
<li>Innovation is continuing, resulting in new products or modifications of<br />the old products to better suit the needs of both issuers (sponsors) and<br />investors.</li>
<li>There has been some movement away from indemnity-based towards<br />parametric index triggers, with bond default not depending on the actual<br />losses of a specific insurance company. Many investors are no longer<br />willing to be at an informational disadvantage and demand that default<br />triggers and payout be based on a more objective index.</li>
<li>With the movement away from indemnity-based triggers, basis risk is<br />becoming a growing concern for the sponsors of catastrophe bonds. The<br />risk that cat bonds would turn out to be an ineffective hedge and will not<br />provide protection when expected is necessitating better modelling and<br />trigger choices.</li>
<li>The development of new parametric triggers that have the ability to<br />further reduce basis risk of the sponsors is an ongoing process and will<br />likely lead to the greater use of these new triggers at the expense of the<br />indemnity and standard industry loss triggers. The ability to address the<br />issue of basis risk can expand the universe of sponsors and lead to market<br />growth.</li>
<li>Securitisation of new types of insurance risk, including liability insurance,<br />will probably grow and has a potential to become a viable alternative to<br />reinsurance for some extreme catastrophic events. It is also expected that<br />insurance securitisation will move beyond very low-frequency/extremeseverity<br />events and will involve higher-frequency insurance risk.</li>
</ul>
</div>
Unknownnoreply@blogger.com1tag:blogger.com,1999:blog-6468318357328951173.post-62854419563141878472015-08-02T22:46:00.001-07:002015-08-09T12:04:48.110-07:00Property Catastrophe Bonds and Insurance Risk Part 1<div dir="ltr" style="text-align: left;" trbidi="on">
<h2 style="text-align: left;">
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Property Catastrophe Bonds</h2>
<div style="text-align: left;">
This article describes property catastrophe bonds, which are probably the<br />
best known <a href="http://insuranceplanet.blogspot.com/2015/07/insurance-linked-securities.html">type of insurance-linked securities</a>. Standard structural features<br />
of catastrophe bonds are explained and the main analytical approaches<br />
introduced. The chapter explains advantages and disadvantages of these<br />
securities from both an insurance company and an investor perspective.</div>
<div style="text-align: left;">
<br /></div>
<h3 style="text-align: left;">
SECURITISATION OF PROPERTY INSURANCE RISK</h3>
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<div style="text-align: left;">
The insurance industry is one of the largest warehouses of risk, incorporating<br />
the roles of both risk underwriter and risk bearer in the way that the<br />
banking industry did three decades ago. Since then, the banking industry<br />
has undergone dramatic changes and now passes much of the risk on to<br />
investors in the form of mortgage-backed and other securities. </div>
<div style="text-align: left;">
<br /></div>
<div style="text-align: left;">
A strong argument could be made that the insurance industry should move in the<br />
same direction by underwriting insurance risk and then passing a sizable<br />
part of it on to investors in the form of standard securities. Many believe that<br />
this is eventually going to happen, in particular for the products that are<br />
more homogeneous and relatively commoditised, such as some types of life<br />
and automobile insurance. </div>
<div style="text-align: left;">
<br /></div>
<div style="text-align: left;">
At this point, however, capital markets’ involvement<br />
in the insurance industry is starting not from the standard<br />
homogeneous risk but rather from the most unusual and severe type of risk<br />
– that is, the risk of natural catastrophes.</div>
<div style="text-align: left;">
<br /></div>
<div style="text-align: left;">
Insurance and reinsurance industries, while considered to be well capitalised,<br />
do not have the capacity to withstand the financial impact of a<br />
large-scale natural disaster. Individual insurance companies, especially<br />
those with significant exposure in certain geographic locations, face the risk<br />
of large losses or financial ruin even from smaller-scale catastrophic events.<br />
The sheer size of capital markets makes them the natural candidate for<br />
providing the backstop protection to the insurance industry should a<br />
Category 5 hurricane make a landfall in Miami, Florida, or should an earthquake<br />
Category 8 on the Richter scale hit San Francisco, California. Capital markets, whose size exceeds that of the insurance industry by orders of<br />
magnitude, may more easily weather such catastrophic losses.</div>
<h3 style="text-align: left;">
MOTIVATION FOR TRANSFERRING NATURAL CATASTROPHE RISK TO THE CAPITAL MARKETS</h3>
<div style="text-align: left;">
The idea behind catastrophe (cat) bonds is to transfer to the capital markets<br />
the risk that extreme catastrophic events would inflict sizable losses on portfolios<br />
of insurance policies held by insurance companies. Cat bonds offer a<br />
new way for insurance companies to manage their risk exposure, a way that<br />
provides benefits to insurance company shareholders by controlling the risk<br />
and, if used appropriately, deploying their capital more effectively. From<br />
the point of view of policyholders and regulators, the advantage is the<br />
decreased likelihood of the company’s inability to pay its claims in the event<br />
of a natural catastrophe.</div>
<div style="text-align: left;">
</div>
<h3 style="text-align: left;">
Insurance company motivation</h3>
<div style="text-align: left;">
The primary motivation of an insurance company in securitising its property<br />
catastrophe exposure by entering into a cat bond transaction is risk<br />
transfer. In contrast, in triple-X and most other life insurance securitisations,<br />
the primary motivation is not risk transfer but relieving the capital strain<br />
created by regulatory requirements. As part of the overall capital-management<br />
programme, the transfer of catastrophe risk to the capital markets is<br />
another tool that insurance companies have in their overall arsenal of ways<br />
to find the right balance between risk and return, and to manage capital<br />
more efficiently.</div>
<div style="text-align: left;">
<br />
Cat bonds are used as an alternative to traditional reinsurance for lowprobability<br />
events. In some cases, protection obtained this way is cheaper<br />
than the cost of reinsurance. An additional advantage is the fully collateralised<br />
nature of the cat bond protection. It reduces the credit risk that is<br />
always present in traditional reinsurance. </div>
<div style="text-align: left;">
<br /></div>
<div style="text-align: left;">
This risk could be significant since, when a sizable natural disaster strikes, reinsurance companies are<br />
exposed to large losses and some might not be able to make good on their<br />
obligations. Cat bond transactions also allow insurance and reinsurance<br />
companies to lock in the cost of protection for a period longer than the one<br />
year that is standard for reinsurance contracts.</div>
<h3 style="text-align: left;">
Investor motivation</h3>
<div style="text-align: left;">
The motivation of the insurer in hedging risk exposure is clear. What are the<br />
advantages of the transaction to the investor in these securities? In other<br />
words, why would capital markets players be interested in investing in cat<br />
bonds? The first reason is the excess return that has been available on cat bond<br />
transactions. The excess (relative to similarly rated corporate debt) return<br />
has existed from the very first days of insurance risk securitisation and has<br />
been attributed primarily to market inefficiency. </div>
<div style="text-align: left;">
<br /></div>
<div style="text-align: left;">
It has always been expected that with the growth of the cat bond issuance and the increase in the number and sophistication level of the market participants, the excess return would<br />
become very small. However, this has not happened in the decade since the<br />
first cat bond was issued even as we witnessed wide fluctuations in pricing.<br />
On the contrary, in the aftermath of the huge insurance losses in the 2004<br />
and 2005 hurricane seasons, the excess return widened. This “Katrina effect”<br />
has led to investors’ being able to obtain high yields on securities that have<br />
relatively high credit ratings. The ubiquitous search for alpha has led some<br />
investors to this asset class.</div>
<div style="text-align: left;">
<br /></div>
<div style="text-align: left;">
The second and probably more important reason is the fact that cat bonds<br />
are often seen as almost “zero-beta” securities that provide a diversification<br />
benefit. The rationale behind this view is that cat bonds are weakly correlated<br />
with the other securities, leading to the comparison with Kipling’s “Cat That<br />
Walked by Himself”. For cat bonds that are properly structured, where all<br />
risks besides that of natural catastrophes are minimised,default rates are only<br />
slightly affected by movements in the financial markets. </div>
<div style="text-align: left;">
<br /></div>
<div style="text-align: left;">
If the stock market crashes or the economy enters a recession, the effect on such cat bonds should<br />
be minimal. (In the past,most cat bonds included significantly greater credit<br />
risk than was intended by the structurers or appreciated by the investors. The<br />
bankruptcy of Lehman Brothers revealed this weakness in a painful way for<br />
some investors. The “new” cat bonds, issued since the beginning of 2009,<br />
have structural features than minimise the credit risk.)</div>
<div style="text-align: left;">
<br /></div>
<h3 style="text-align: left;">
HISTORICAL PERSPECTIVE</h3>
<div style="text-align: left;">
The idea of securitising insurance risk had been floating around for a long<br />
time before the first insurance-linked securities saw the light of day. Some of<br />
the first securities intended to transfer the insurance risk of natural catastrophes<br />
directly to investors were catastrophe options. Traded on the Chicago<br />
Board of Trade (CBOT) in the 1990s, they were met with lukewarm reception<br />
by both insurers and investors and were ultimately withdrawn.</div>
<div style="text-align: left;">
Exchange-traded catastrophe derivatives have recently reappeared and are<br />
now traded, in slightly different forms, on exchanges that include the<br />
Chicago Climate Futures Exchange, CME and Eurex. We will discuss this further by providing<br />
more in-depth treatment of these securities.</div>
<div style="text-align: left;">
<br /></div>
<div style="text-align: left;">
Cat bonds have enjoyed greater success. One of the first cat bonds was issued on behalf of USAA, a large insurance company, in 1997. It transferred<br />
to investors the risk that a hurricane in the Eastern US and the Gulf Coast<br />
would result in catastrophic insured losses to the company. The size of the<br />
bond was US$395 million, which was the maximum protection size<br />
provided to USAA by the transaction.</div>
<div style="text-align: left;">
<br />
Since that pioneering transaction, the volume of property catastrophe<br />
insurance securitisations has steadily grown, primarily in the form of catastrophe<br />
bonds. Insurance and reinsurance companies as well as corporate<br />
entities have turned to the capital markets for protection against catastrophe<br />
risk. The type of risk transferred to investors has ranged from hurricanes to<br />
earthquakes to typhoons, in geographic areas spanning the globe from the<br />
US to Europe to Japan.</div>
<div style="text-align: left;">
<br /></div>
<div style="text-align: left;">
Until recently, the growth was not as fast as observers had anticipated.<br />
However, in the aftermath of Hurricane Katrina in 2005, the interest in<br />
securitising property catastrophe insurance risk has exploded, and there<br />
has been dramatic growth in the total amount of capital invested in securitised<br />
risk in the forms of cat bonds, industry loss warranties and reinsurance<br />
sidecars. The last two are described in greater detail in Chapter 6. The<br />
temporary pause in issuance in the second half of 2008 had to do with<br />
the above-mentioned credit risk issues, which have now been largely<br />
resolved.<br />
<h3 style="text-align: left;">
RISK TRANSFER IN INSURANCE</h3>
Insurance loss distributions tend to differ significantly from the normal<br />
distribution (the bell curve). They are referred to as fat-tailed distributions<br />
because of the high probability of extreme diversion from the mean. (More<br />
precisely, these are leptokurtic distributions.<br />
<br />
Their excess kurtosis leads to the higher probability of outliers in a sample relative to samples drawn from a Gaussian distribution.) Insurance losses resulting from natural catastrophe<br />
events lie at the far-right tail of the aggregate loss distribution, the “cat’s<br />
tail”. These events and their financial impact are difficult to model but are<br />
important for insurance companies to protect against.<br />
<br />
Insurance companies often find themselves unable or unwilling to retain<br />
all of the risk inherent in their portfolios of insurance policies. In dealing<br />
with catastrophe risk, the two main mechanisms for risk transfer are reinsurance<br />
and, more recently, cat bonds or similar capital markets solutions.<br />
<br />
Reinsurance plays a very important role by providing a somewhat efficient<br />
risk exchange mechanism for the insurance industry. In dealing with large scale<br />
catastrophic events, however, even reinsurance fails to provide adequate protection due to the limited capital in the reinsurance and insurance industry relative to the magnitude of potential losses.<br />
<h3 style="text-align: left;">
Reinsurance risk transfer</h3>
Discussion of risk transfer and catastrophe bonds is impossible without<br />
describing reinsurance, the main mechanism for risk transfer in the insurance<br />
industry. Simply put, reinsurance is insurance for insurance<br />
companies. In the case of catastrophe risk transfer, an insurance company<br />
can buy reinsurance protection against losses exceeding a certain level.<br />
<br />
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<br />
<br />
<span id="goog_1968296512"></span><span id="goog_1968296513"></span>The insurance company, or “cedent” of risk in the reinsurance parlance,<br />
pays premiums to a reinsurer for the protection, and is reimbursed for<br />
claims in the scope of the reinsurance contract.<br />
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<br />
Depending on jurisdiction, reinsurer’s rating and other considerations,<br />
the reinsurance company might be required to post collateral. Reinsurance<br />
companies could in turn reinsure some of their risk. This type of reinsurance<br />
is called retrocession.<br />
<br />
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In the above presented in diagram, if the company decides that it is<br />
too risky to retain the exposure between US$500 million and US$630 million,<br />
it has two main options. One of them is to reinsure this exposure. Another<br />
is to go to the capital markets and obtain protection in the form of a catastrophe<br />
bond or a similar instrument. In addition, there is always an option<br />
to reduce the insurance risk exposure by either writing less insurance business<br />
or by changing the concentrations, policy limits or policy conditions of<br />
the insurance portfolio.<br />
<br />
There are also options of raising additional capital in<br />
the form of equity, debt or hybrid securities, as well as obtaining contingent<br />
capital. From the point of view of the efficient use of capital, these options<br />
are usually less effective than reinsurance or catastrophe bonds. Advantages<br />
and disadvantages of using cat options and futures to protect against catastrophic<br />
events are discussed in earlier articles.<br />
<br />
<h3 style="text-align: left;">
CATASTROPHE bONd STRUCTURE</h3>
The structure of a cat bond is different from that of asset-backed securities.<br />
Effectively, securitising insurance risk amounts to securitising a liability<br />
rather than an asset.<br />
Unlike the case of corporate bonds, the insurance or reinsurance company<br />
transferring catastrophe risk to the capital markets is not issuing the bond<br />
directly. Instead, the bond is issued by a special purpose reinsurance<br />
company, which is generally located offshore. Thus, the entity that transfers<br />
the risk to the capital markets is referred to as the sponsor rather than the<br />
issuer of the catastrophe bond.<br />
<br />
An entity that wants to transfer catastrophe risk to the capital markets<br />
would enter into a catastrophe reinsurance contract with a special purpose<br />
vehicle (SPV), a reinsurance company. The SPV will issue a bond with the<br />
payment of principal and interest contingent on there not occurring a catastrophe<br />
causing specified damage. The term of the reinsurance contract is<br />
the same as the term of the bond.<br />
<br />
If during this term no such catastrophe has<br />
happened, investors get back the principal and interest in full. Should there<br />
be a natural catastrophe triggering the reinsurance contract, the SPV will<br />
pay the claims. The remainder of the funds, if any, will go towards the<br />
payment of principal and interest to investors.<br />
The simplified structure of a catastrophe bond is shown in the diagram below<br />
If no covered catastrophe has occurred during the term of the bond,<br />
investors receive back their principal at the end of the term.<br />
<br />
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by a certain natural catastrophe. The second transaction is the issuance of a<br />
fixed-income security, a cat bond, to investors. The cat bond provides for<br />
payment of interest and repayment of principal unless a default is triggered<br />
by a natural catastrophe leading to a high level of insured losses.<br />
Proceeds from the sale of the cat bond are deposited into a trust account<br />
that serves as collateral.<br />
<br />
The trust account would contain very secure, highly<br />
rated short-term instruments. While in many cases a cat bond sponsor could<br />
legally own the SPV without affecting its bankruptcy-remote status, in practice<br />
the SPV would usually be established by a third party such as an<br />
investment bank structuring the transaction.<br />
<br />
Returns from the collateral account are swapped for a Libor-based rate<br />
with a highly rated counterparty. The total-return swap feature has become<br />
common in cat bond structures. Thus, interest rate-risk is minimised and the<br />
cat bonds become floating-rate instruments.<br />
<br />
The interest payments received by investors are composed of the Liborbased<br />
returns on the funds in the collateral account; they can also include all<br />
or part of the reinsurance premiums received by the SPV from the sponsor.<br />
Several ways to minimise credit risk related to the swap counterparty and<br />
to the assets in the collateral account have emerged post-2008.<br />
<br />
No credit enhancement or credit wrapping has been used in property<br />
catastrophe bond securities. This has to do, in part, with the relatively low<br />
ratings of most catastrophe bonds, which makes them too risky for monoline<br />
financial guarantee companies to add a credit wrap. (Credit<br />
enhancement used to be a common feature of extreme-mortality catastrophe<br />
bonds and other life-insurance-linked securities.<br />
Credit enhancement of this type is no longer available from financial guarantors.<br />
<h3 style="text-align: left;">
DEFAULT TRIGGERS</h3>
A number of payout triggers – triggers of the cat bond default – have been<br />
proposed and used in cat bond transactions. In general, the triggers fall into<br />
one of two categories: indemnity and index. Indemnity triggers<br />
Indemnity triggers provide for cat bond payout based on the actual insurance<br />
losses suffered by the bond sponsor. This makes the cat bond a very<br />
effective hedge against the risk of losses from the natural catastrophe since<br />
the basis risk is minimised.<br />
<br />
It largely avoids the unfortunate situation of a<br />
natural catastrophe occurring, an insurance company suffering significant<br />
losses, but finding itself unable to collect from the cat bond it sponsored.<br />
<br />
The negative side of indemnity triggers, from the point of view of the<br />
sponsor, is the need for information disclosure about its book of insurance<br />
business and underwriting practices. Many insurance companies prefer to<br />
keep this data confidential. Some of them are also hesitant to undergo the<br />
data quality review needed to present the information to investors in an<br />
offering circular.<br />
<br />
Many investors see only negatives in the use of indemnity triggers. By its<br />
very nature, an indemnity trigger is less objective since it is based on actual<br />
insured losses rather than on parameters of a physical event. Investors are<br />
justifiably wary of the asymmetric information, with the insurance company<br />
sponsoring the bond having a significant information advantage in better<br />
knowing the types of risks it underwrites, risk aggregation, its underwriting<br />
standards and claim-settlement practices.<br />
<br />
The investors also assume the risk that, as the company implements its strategy or responds to market conditions during the term of the bond, its insurance portfolio might change and increase the risk of bond default. The very fact that the bond sponsor has<br />
obtained protection via a cat bond could lead to a morale hazard, demonstrating<br />
itself in less care being taken in insurance underwriting and claim<br />
settlement.<br />
<br />
In addition to the morale hazard, there is always a potential for<br />
moral hazard, with the insurer intentionally (but without violating the bond<br />
covenants) making changes to its portfolio to the detriment of the cat bond<br />
investors. While indemnity-based bonds historically were the first issued and are<br />
still common, the general trend has been away from the indemnity-type triggers<br />
and towards index triggers.<br />
<h3 style="text-align: left;">
Index triggers</h3>
Index triggers do not directly depend on the bond sponsor’s actual insurance<br />
losses. Rather, they depend on parameters that are outside of the<br />
control of the sponsor, thus providing more comfort to investors by eliminating<br />
the information asymmetry inherent in indemnity-based triggers.<br />
Index triggers usually fall into one of the following four categories: simple<br />
index, parametric, model portfolio loss and industry loss.<br />
<h3 style="text-align: left;">
</h3>
<h3 style="text-align: left;">
Basic index trigger</h3>
Basic index trigger provides for cat bond payout in case a predetermined<br />
physical event happens. A simple example would be a Category 5 hurricane<br />
making a landfall in Florida. If such an event happens, a cat bond with this<br />
<br />
trigger will suffer a default and make a payment to the benefit of the<br />
sponsor. It could, but does not have to, be structured like a binary option,<br />
providing either no payment to the sponsor if not triggered or the full<br />
payment (full default) if triggered.<br />
<br />
From the point of view of an investor, this structure is very attractive.<br />
Investors have access to full information, and the dependence on sponsor’s<br />
underwriting and other practices is eliminated.<br />
On the other hand, the insurance company sponsoring the bond faces<br />
significant basis risk.<br />
<br />
A trigger so crudely defined could have poor correlation<br />
with actual insurance losses, reducing the effectiveness of the cat bond<br />
as a hedge. In other words, there is a significant chance that the cat bond<br />
would provide little or no protection against actual insurance losses suffered<br />
by the sponsor. There is also a chance that the bond will be triggered when<br />
the sponsor has not suffered sizable losses. In this case, the sponsor has paid<br />
for unneeded protection.<br />
<br />
The basis risk is present in all non-indemnity trigger types, but is greatest<br />
when the trigger is based on a basic index.<br />
<br />
<h3 style="text-align: left;">
Parametric trigger</h3>
Parametric trigger is based on the occurrence of catastrophic events with a<br />
combination of defined physical parameters. More than one type of catastrophic<br />
event (hazard) could be involved, and the amount of the payout is<br />
a function of the physical parameters of the cat events. A predefined formula<br />
is used to determine whether the bond is triggered and what the payout<br />
amount is.<br />
<br />
The formula could be quite complex. It is structured in a way that<br />
reduces the basis risk by identifying physical parameters of cat events (such<br />
as wind speeds at several locations) that would lead to insurance losses of<br />
the magnitude that the sponsor wants to transfer to capital markets.<br />
Identification of such parameters and the construction of the formula (the<br />
overall index), if done properly, involve a significant modelling exercise on<br />
the part of the sponsor.<br />
<br />
The investor, on the other hand, is not concerned<br />
with the sponsor’s insurance losses and hedge effectiveness. Since the probability<br />
of default and the loss given default are independent of actual<br />
insured losses, investor analysis is focused on the probabilities of the physical<br />
events and their severities included in the parametric trigger formula.<br />
<h3 style="text-align: left;">
Model portfolio loss</h3>
In this case, a sponsor creates a model portfolio that closely mirrors its actual<br />
portfolio of insurance policies or the portfolio that the sponsor expects to<br />
hold during the term of the bond. The portfolio is held “in escrow” together<br />
with the modelling software used to calculate losses to the portfolio. If a<br />
natural catastrophe happens, its actual physical parameters are input into<br />
the modelling software and losses to the model portfolio are generated.<br />
<br />
The bond payout depends on whether and by how much the modelled losses<br />
exceed a predetermined level. To further reduce basis risk, the sponsor could use its actual current insurance portfolio instead of the representative model portfolio.<br />
<br />
The negatives of this approach have to do with the unwillingness of insurance companies to<br />
reveal detailed information about their insurance portfolios and the fact that<br />
such detailed policy-level disclosure could sometimes be unlawful.<br />
Investors not possessing specialised expertise and knowledge of cat<br />
modelling software sometimes feel uncomfortable with the use of this<br />
trigger type.<br />
<h3 style="text-align: left;">
Industry loss trigger</h3>
This trigger is tied to an index of losses suffered by the insurance industry<br />
as a whole as a result of a natural catastrophe. While not based directly on physical parameters of a catastrophic event, this index could be modelled<br />
much better than indemnity losses. Given that a specific catastrophic event<br />
has occurred, insurance losses for the whole industry are more predictable<br />
than losses for an individual insurance company.<br />
<br />
They also are not subject to manipulation by the sponsor through claim settlement or another mechanism. Insurance loss-reporting organisations provide information to<br />
determine the overall loss level for the industry from a specific catastrophic<br />
event. The sponsor bears the basis risk, which depends on how its actual loss<br />
distribution differs from the rest of the insurance industry.<br />
<h3 style="text-align: left;">
Trigger choice</h3>
In choosing a trigger, there is always a balance to be struck between transparency<br />
and simplicity on the one side, and the minimisation of basis risk on<br />
the other. It is also worth noting that trigger choice to some degree affects structuring<br />
costs, with indemnity-based transactions being the most expensive to<br />
structure.<br />
<br />
Indemnity-based cat bonds also take longer to generate a payout<br />
since the sponsor might have to settle its claims first to determine the loss<br />
size. Basic index, parametric and model portfolio triggers provide for fast<br />
payout, while cat bonds based on industry-loss triggers have a payment<br />
delay due to the need to calculate the estimates of industry losses.<br />
<br />
While in general all default triggers fall into one of the described categories,<br />
modifications of these triggers could be and have been used too.<br />
Some investors, especially in the aftermath of 2005 Hurricane Katrina, have expressed a strong aversion to indemnity-based transactions, and<br />
prefer bonds with parametric and similar triggers.<br />
<br />
<h3 style="text-align: left;">
Second- or third-event trigger</h3>
Structuring a cat bond provides a lot of room for creativity in trying to<br />
achieve the best protection for the insurance company while satisfying<br />
investor concerns. Sometimes an insurance company is not afraid of<br />
suffering one catastrophic loss, but wants to get protection in case one catastrophe<br />
is followed by another in the same or the following year.<br />
<br />
A second-event trigger could provide the required protection to the company,<br />
with the bond providing no payout (but being “activated”) after the first<br />
catastrophic event and paying only if the second event occurs as well.<br />
<h3 style="text-align: left;">
NUMBER ANd TYPES OF PERILS</h3>
A catastrophe bond trigger could be based on one specific type of peril such<br />
as a hurricane, typhoon or earthquake. It could also be based on a number of perils, with losses from any one of them or a combination of perils triggering<br />
the payout.<br />
<br />
While the first cat bonds were generally designed to provide protection<br />
against one type of peril, we have now seen a strong trend towards incorporating<br />
multiple perils in a bond. The same bond could have a number of<br />
peril/geographic location combinations.<br />
<br />
Larger insurance or reinsurance companies with portfolios of insurance policies on more than one continent are interested in this aggregate protection. When it comes to multiple perils,<br />
investors fall into two categories. Some are happy to see various types of<br />
uncorrelated risk in the same security. Effectively, diversification is<br />
provided for them in such a bond. Others prefer to buy cat bonds tied to a<br />
single peril and to achieve diversification on their own.<br />
<br />
The latter category tends to include investors with better understanding of the insurance-linked<br />
securities, including the funds focused exclusively on these financial instruments.<br />
The table, overleaf, shows a sample of catastrophe bonds issued<br />
based on various default triggers and types of catastrophe peril. Some cat<br />
bonds have included a number of tranches, each of which corresponds to a<br />
specific type of insurance risk and has its own trigger.<br />
<br />
The securitisation of insurance risk has moved beyond property catastrophe<br />
and has included some liability insurance cat bonds, as well as<br />
securitisation of property-casualty insurance risk that is not truly catastrophic<br />
in nature.<br />
<br />
<h3 style="text-align: left;">
TERM</h3>
The cat bond tenor varied widely in the early days of insurance securitisations,<br />
but has now stabilised with the average being three years. This term<br />
is long enough for the sponsor to lock into a multi-year protection at a<br />
predetermined price and to avoid paying the fixed cost of issuing a cat bond<br />
every year. At the same time, it is short enough for the sponsor to predict the<br />
composition of its future insurance portfolio with a reasonable degree of<br />
confidence.<br />
<h3 style="text-align: left;">
QUANTITATIVE ANALYSIS</h3>
Both investors and the sponsor require a good understanding of potential<br />
losses, that is, the probability distribution of cat bond payouts. This<br />
probability distribution is in turn based on probabilities of the cat bond<br />
being triggered, and the payout amounts given that the bond has been<br />
triggered. <br />
<br />
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<h3 style="text-align: left;">
Exceedance curve</h3>
Insurance-linked securities might be the only asset type for which probabilistic<br />
risk analysis is included in the investor prospectus. For catastrophe<br />
bonds the analysis, performed by one of the firms specialising in modelling<br />
catastrophe events and their financial impact on portfolios of insurance policies,<br />
is usually presented in the form of a probability exceedance curve (EC).<br />
The exceedance curve shows probabilities of insurance losses of various<br />
magnitudes.<br />
<br />
If the annual exceedance probability is 1%, then the probability of<br />
exceedance during a three-year period is 3%. (More precisely, the probability<br />
of exceedance over a three-year period is equal to 1–(1–0.01)3 = 2.97%.<br />
<br />
The approximation works well for only very small annual exceedance probabilities<br />
and short time periods. For example, if the annual exceedance probability is 2% and the term is eight years, we might think that the probability of exceedance over the term equals 16%. In reality, it is 14.92%, which is calculated as 1–0.988.) The following figure shows an example of an exceedance<br />
probability curve for a portfolio of insurance risk.<br />
<br />
In this example, losses above US$500 million might have a catastrophic<br />
effect on the insurance company’s financial position. The company has<br />
several options to protect itself against this possibility. Some of them have to<br />
do with raising additional capital or reducing or rearranging the company’s<br />
portfolio of insurance policies. The most common solution is purchasing<br />
reinsurance – that is, insurance protection for this insurance risk portfolio.<br />
<br />
For example, the reinsurance coverage could take the form of the reinsurance<br />
company reimbursing the insurance company for all losses above the<br />
level of US$500 million, limited to the total payout of US$250 million. In this<br />
<br />
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<br />
case, the insurance company would still be unprotected if the total losses<br />
exceed US$750 million, but would probably be willing to take this risk if<br />
losses above US$750 million were considered to be exceptionally unlikely.<br />
The company might wish to protect itself from losses in excess of US$500<br />
million even if the effect of such losses would not have a truly catastrophic<br />
effect on its financial position.<br />
<br />
The reasons for it could be the desire to<br />
decrease earnings volatility or to reduce capital requirements.<br />
In property insurance, unique terminology has been developed. Probable<br />
maximumloss, or PML, is the loss level thatwould be reached only extremely<br />
rarely. There are many opinions of how rare is “rare”, leading to multiple<br />
definitions of PML. If a company wants to define PML as the aggregate loss<br />
level thatwould be reached only once in 100 years, then in the example above<br />
the PML will be US$500 million.<br />
<br />
With the increased emphasis on risk management and themore stringent capital-adequacy requirements promulgated by the rating agencies, there is growing shift of focus to property<br />
catastrophe events that happen on average less often than once in 250 years,<br />
leadingmany to define PML as the 1-in-250-year event.<br />
<br />
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<br />
<br />
While the concept of PML is often used in relation to losses from individual policies, here we<br />
discuss the aggregate PML of an insurance portfolio. We also avoid nonquantitative<br />
definitions of PML still common in the insurance industry. In insurance, probability of exceedance is usually expressed on an annual basis, that is, as a probability that insured losses will exceed a certain level over a period of 12 months. In the context of cat bonds, exceedance probability<br />
could also be expressed as the probability of losses exceeding a certain<br />
level, such as the bond trigger level, over the term of the bond.<br />
<br />
Modelling catastrophe risk presents numerous challenges, but, even when it is accomplished,<br />
the results by themselves do not tell the investors what price is<br />
appropriate or fair for the cat bond being modelled. Several pricing models<br />
have been proposed. Often, they use as an input the observed prices for<br />
other cat bonds. <br />
<br />
As “neat” mathematically as the Wang transform is, its practical application<br />
is very difficult. It has also been pointed out (Pelsser 2008) that its use<br />
in pricing financial and insurance risks is consistent with arbitrage-free<br />
pricing, only under rather restrictive assumptions (this statement, however,<br />
has been disputed).<br />
<br />
Other pricing approaches have been proposed, such as the application of<br />
extreme-value theory to cat bond pricing. This approach requires making<br />
assumptions not fully appropriate for cat bond analysis, and it does not<br />
produce results resembling observed cat bond prices. A simple rule-ofthumb<br />
approach to pricing includes the use of “multiples” of expected<br />
annual loss (average annual loss, or AAE) to determine the required spread over Libor or risk-free rate. Different multiples correspond to different levels<br />
of expected loss.<br />
<br />
While this approach has a questionable mathematical foundation,<br />
it is easy to use and there are some investors that utilise it. Another<br />
simple approach that has been proposed calculates prices based on the<br />
expected frequency and severity of the losses. The parameters are estimated<br />
based on the observed cat bond prices. This approach has the appeal of<br />
simplicity, but it lacks any theoretical foundation.<br />
<br />
Finally, some still use approaches that calculate prices based on the mean plus a multiple of standard<br />
deviation. Many of these relatively simple approaches are borrowed<br />
from reinsurance pricing, where they have been used for many years, but<br />
even there they are being replaced by the more sophisticated methods.<br />
<br />
In addition to the shaky theoretical foundations of some of the pricing<br />
approaches, their common weakness is the dependence – either for parameter<br />
fitting or for results validation – on the actual observed cat bond<br />
prices. The cat bond market and the ILS markets in general are far from <br />
<br />
<h3 style="text-align: left;">
WANG TRANSFORM AND PRICING OF CAT BONDS</h3>
The Wang transform was developed by Shaun Wang (Wang 2000; Wang<br />
2004; Pelsser 2008) with the goal of linking actuarial pricing and modern<br />
finance theories. It has been used for both pricing cat bonds and excess-ofloss<br />
reinsurance. While the full explanation of this method is outside the<br />
scope of this chapter, the basics of the approach are explained below.<br />
<br />
Based on the underlying loss variable X, the loss to the excess-of-loss<br />
layer attaching at a with the limit of h, which is equivalent to the loss to a<br />
cat bond, is defined as<br />
<br />
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being efficient, and the observed prices, even relative to each other, do not<br />necessarily follow the logic evident in more efficient markets.<br />The supply–demand dynamics play a very important role in pricing cat<br />bonds and catastrophe risk in general. When reinsurance markets “harden”,<br />the spread over Libor is likely to increase. This effect does not necessarily<br />correlate with the behaviour of the financial markets.<br />
<br />
Even more importantly, the “peak peril” effect results in prices that are difficult to predict<br />based on the assumption that markets are efficient. Two cat bonds, one<br />linked to hurricane losses in Florida and the other to typhoon losses in<br />Australia, might have exactly the same exceedance probability distributions,<br />but the yield on the Florida hurricane bond is likely to be dramatically<br />greater than on the Australia typhoon one.<br />
<br />While the proposed pricing approaches often fail in the analysis of individual<br />bonds, relative-value analysis is still possible and helpful. More<br />importantly, the existing modelling tools allow us to manage cat risks on a<br />portfolio basis, and – instead of trying to come up with a theoretically<br />correct price for an individual bond – to see what incremental impact its<br />addition to the portfolio is going to have relative to the available alternatives.<br />This topic is further discussed in Property Catastrophy Bonds Part 2.</div>
</div>
Unknownnoreply@blogger.com0tag:blogger.com,1999:blog-6468318357328951173.post-8293091669093732462015-07-31T02:22:00.001-07:002015-08-09T12:05:20.204-07:00 Insurance Linked Securities<div dir="ltr" style="text-align: left;" trbidi="on">
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This article provides a brief introduction to insurance-linked securities (ILS) as an asset class in order to lay the foundation for the more thorough treatment of individual types of ILS in the rest of the blog. It explains the reasons for transferring insurance risks to the capital markets and the benefits this transfer provides both to the insurance industry and to the investors.<br />The types of insurance risk transferred to the capital markets are also briefly discussed.<br />
<h3 style="text-align: left;">
INSURANCE-LINKED SECURITIES DEFINED</h3>
In the article <a href="http://insuranceplanet.blogspot.com/2015/07/introduction-to-investing-in-insurance.html">Investing in insurance risk, we defined insurance-linked securities as financial instruments</a>, other than traditional equity and debt securities issued by insurance companies,<br />that carry insurance risk or a type of risk that is closely related to it.<br />Examples of the risks included in ILS are those associated with property<br />catastrophe, mortality, longevity and insurance loss reserve adequacy. ILS<br />can also include many of the traditional risks such as market, credit and<br />interest-rate risks, but it is the inclusion of a significant degree of insurance<br />risk that defines them.<br />
<br />The term “risk-linked securities” is occasionally used instead of ILS,<br />sometimes to highlight a broader spectrum of insurance-linked securities –<br />for example, weather derivatives, which do not have a direct relationship to<br />any actual insurance losses, but serve the purpose of transferring to the<br />capital markets risks very similar to those taken on by insurance companies.<br />
<br />
In some cases, the distinction between insurance-linked and other securities<br />becomes blurred; but generally a security is labelled an ILS if it resembles<br />one of the standard types of insurance-linked securities.<br />
<br />Insurance risks involved in insurance-linked securities cover the whole<br />range of insurance-related risks, from property-casualty insurance to life<br />insurance. The wide variety of insurance risks embedded in ILS is reflected<br />in the multitude of types of insurance-linked securities.<br />
<br />
<h3 style="text-align: left;">
TYPES OF INSURANCE-LINKED SECURITIES</h3>
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While catastrophe bonds are the best known insurance-linked securities, the<br />ILS universe is much broader than that. Products range from alternatives to<br />reinsurance coverage, to securities that can be constructed only with the use<br />of capital markets. The following infographic, presents ILS characterised by the<br />degree of catastrophe risk being transferred to the capital markets and by the<br />type of insurance risk. The list is far from complete: only the main types of<br />insurance-linked securities are shown.<br />
<br />
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<br />Categorisation of insurance-linked securities is partly dependent on the<br />reasons the insurance risk is being transferred to the capital markets by<br />insurance companies or other entities.<br />
<br />
<h3 style="text-align: left;">
Reasons for transferring insurance risk to the capital markets</h3>
Insurance risk can be transferred to investors for a number of different<br />reasons. Some of these reasons are described below. There is significant<br />overlap since a transaction can accomplish more than one objective.<br />
<br />
<h4 style="text-align: left;">
TRANSFER OF CATASTROPHE RISK. </h4>
Insurance and reinsurance companies are limited in the amount of true catastrophe risk they can assume. A largescale catastrophe, either natural or manmade, has the potential of<br />wiping out the surplus (shareholder equity) of many companies at the<br />same time. It can even start a spiral of insolvencies or downgrades if<br />several reinsurance companies fail, and the reinsurance recoverables<br />remain uncollectable.<br />
<br />
Prudently managed insurance and reinsurance<br />companies are aware of this risk and either partially transfer it to other<br />parties or choose not to assume it at all, leaving some exposures uninsured.<br />Since the total shareholder funds of the insurance industry are<br />dwarfed by the size of the capital markets, it makes perfect sense to<br />transfer the true catastrophe risk to investors.<br />
<br />
This can be done in the form of cat bonds, industry loss warranties, reinsurance sidecars, catastrophe<br />derivatives, collateralised reinsurance of catastrophe risk, or<br />contingent capital securities. Catastrophe risk also exists in life insurance<br />– for example, in the case of a jump in mortality due to a pandemic<br />event. Such risk can be transferred to the capital markets primarily in<br />the form of cat mortality bonds and cat mortality derivatives.<br />
<br />
<h4 style="text-align: left;">
SUBSTITUTE FOR TRADITIONAL REINSURANCE. </h4>
Limited risk capacity leads to higher reinsurance rates, which in some cases results in capital markets<br />solutions being more efficient in terms of cost. Given the additional<br />advantages provided by some insurance-linked securities (for example, the ability to lock in the cost of protection for more than one year, and limited credit risk), capital markets solutions can be an important part of the overall risk management programme, acting as both a substitute for<br />and a complement to traditional reinsurance.<br />
<br />
Avoiding overexposure to a few reinsurers and thus lowering credit risk is of particular importance.<br />Investor-provided collateralised reinsurance, insurance derivatives, and<br />industry loss warranties are all examples of insurance-linked securities<br />that fall in this category.<br />
<br />
<h4 style="text-align: left;">
RELIEVING CAPITAL STRAIN. </h4>
In the absence of distressed conditions, insurance<br />companies can still experience capital strain when they grow too fast<br />or when regulations require them to hold capital significantly in excess of<br />the levels necessary from the economic point of view.<br />
<br />
An example of a capital markets solution driven by this rationale is XXX and AXXX securitisation.<br />In this case, US regulations require that reserves for some life insurance products be maintained at levels significantly in excess of what most consider economically reasonable. This requirement results in considerable strain on insurance companies’ capital; XXX and AXXX securitisation<br />or private investment solutions help alleviate this strain.Value-in-force securitisation or monetisation can also provide additional capital, either to eliminate a shortfall or to be used for other purposes such<br />as mergers and acquisitions.<br />
<br />
<h4 style="text-align: left;">
TURNING LIFE INSURANCE INTO TRADABLE INSTRUMENT. </h4>
Life settlements developed as a way for policyholders to monetise the value of their existing life<br />insurance policies when they are no longer needed, when they cannot be<br />afforded or when the benefit of immediate monetisation outweighs the<br />advantages of keeping the policies.<br />
<br />
From the economic point of view, a life insurance policy is a security and thus can be traded. Once a life insurance policy is bought by investors, it then can be resold more than once.<br />Portfolios of life settlements can be separately managed or securitised.<br />Managing portfolios of life settlements can benefit from the use of another<br />type of ILS, longevity derivative instruments, that could hedge the<br />longevity risk of such portfolios.<br />
<br />
<h4 style="text-align: left;">
LONG-TERM LONGEVITY RISK TRANSFER. </h4>
Capital markets solutions can be utilised to address the risk of greater-than-anticipated longevity. Pension funds and some annuity providers are among the entities exposed to this<br />risk. In the case of pension funds, longevity improvements in excess of<br />expectations can lead to significant shortfalls. Longevity derivatives and<br />longevity bonds are examples of instruments that can transfer this risk to<br />the capital markets.<br />
<br />
The list illustrates some of the reasons why insurance risks would be transferred<br />to the capital markets, along with a few types of insurance-linked<br />securities used for this purpose. There are a number of additional reasons,<br />including more efficient capital management, reducing earnings volatility of<br />insurance companies, addressing rating agency concerns, managing credit<br />risk and many others; again, these often overlap.<br />
<br />
<h3 style="text-align: left;">
Reasons for investing in insurance-linked securities</h3>
While there is a multitude of reasons why insurance companies and other<br />entities might want to transfer insurance risk, conceptually the reasons why<br />investors might want to accept it are much simpler.<br />
<br />Adding an ILS to an investment portfolio may be beneficial if it improves<br />the risk–return profile of the portfolio. Consequently, the analysis of<br />whether an ILS investment makes sense is quite similar to the analysis of<br />investing in any other security. If the marginal impact of adding an insurance-<br />linked security to the portfolio improves its risk–return profile more<br />than available alternatives, the investment probably makes sense.<br />
<br />In even simpler terms, investors find insurance-linked securities attractive<br />because they provide yield, diversification or both. Given the constant<br />search for extra yield and diversification opportunities, it is natural for<br />investors to consider this asset class, with all of its unique characteristics.<br />
<br />
Structurers of insurance-linked securities are mindful of the fact that<br />investor needs should be satisfied, and they take this into account when<br />deciding on the best ILS structure to transfer an insurance risk to the capital<br />markets.<br />
<br />
<h3 style="text-align: left;">
YIELD AND DIVERSIFICATION OFFERED BY INSURANCE-LINKED SECURITIES</h3>
Investors look to insurance-linked securities primarily for yield or diversification.<br />Diversification in particular has been publicised as a unique advantage of ILS. Insurance-linked securities do offer a type of diversification not available through exposure to other assets. For many types of ILS, especially cat bonds and similar instruments, this is a critical advantage that<br />makes this asset class so important.<br />
<br />
The experience of 2008 shows that, when almost all asset classes are down, even those that historically have had low correlation, the importance of the low correlation that stays low even in the<br />tail of the probability distribution becomes clearly evident.<br />
<br />
<h3 style="text-align: left;">
The “zero-beta” assets</h3>
Many insurance-linked securities provide a unique type of diversification<br />through exposure to “pure” insurance risk. While this is often their main<br />attraction to investors, it does not mean they are completely uncorrelated<br />with the rest of the financial markets.<br />
<br />Statements have been made repeatedly that ILS, in particular life settlements<br />and cat bonds, are zero-beta assets and have no correlation with the<br />markets at all. While the correlation between some types of ILS and the<br />financial markets might be weak, it does exist, and the zero-beta claims are<br />not valid. They are particularly unfounded where they are repeated most<br />often – in the case of life settlements, which are clearly exposed to the<br />interest rate and a host of other risks.<br />
<br />
<h3 style="text-align: left;">
Yield generation</h3>
Insurance-linked securities often provide yield opportunities in excess of<br />those implied by their risk level. The yield can be a very important benefit<br />of these securities and can become an alpha generator for an investment<br />portfolio.<br />
<br />Part of the reason for the extra yield is the market inefficiency and the<br />unfamiliarity of investors with these securities. The market is still small, and<br />expertise in ILS analysis is hard to find in the investment community. Over<br />time, the markets will surely become more efficient, and excess returns will<br />diminish or disappear.<br />
<br />
This, however, is likely to be a very long process.<br />Some ILS offering what appears to be high return on a risk-adjusted basis<br />might in reality be much riskier than expected by investors lacking sufficient<br />expertise in this space. Some of the ILS appear deceptively simple, and an<br />investor without deep expertise in this asset class can be lured into making<br />poor investment decisions.<br />
<br />
<h3 style="text-align: left;">
Efficient frontier</h3>
The ability to invest in insurance-linked securities can have the effect of<br />shifting the efficient frontier for an investor. The limited correlation of ILS<br />returns with other assets enhances diversification options, and the new efficient<br />frontier may then have lower risk for the same level of return, or higher<br />return for the same level of risk.<br />
<br />
This is the exotic beta appeal of this asset class as it provides exposure to a risk factor with low correlation with the rest of the financial markets. It is important that the efficient frontier mentioned above does not have to be defined within the mean-variance optimisation framework. In fact, the in the more sophisticated framework that takes into account events in the<br />tail of the probability distribution. value of adding ILS to an investment portfolio can be even more apparent in the more sophisticated framework that takes into account events in the<br />tail of the probability distribution.<br />
<br />
<h3 style="text-align: left;">
MARKET DYNAMICS</h3>
Despite its relatively small size to date, the ILS market is very dynamic and<br />constantly changing. New instruments appear, or the existing ones<br />suddenly grow in prominence, while others fade into obscurity, more or less<br />in direct response to changing market conditions. Meanwhile there is a<br />gradual, ongoing process of education and acceptance of this new asset<br />class.<br />
<br />Not all of the developments have been smooth and the growth has been<br />uneven. An example of such a change in the ILS market is the redesign of<br />the cat bond structure to minimise the credit risk of this security. This was<br />done in response to the realisation, driven by the events surrounding the<br />bankruptcy of Lehman Brothers in 2008, that credit risk is present and can<br />play a significant role in these securities.<br />
<br />
Another example is the uneven development of the life settlements market, which has been affected by problems specific to this asset class as well as by the general availability of<br />risk capital in the changing investment environment.<br />
<br />
A further example is the painfully slow development of the longevity transfer market, despite the<br />seemingly obvious need for it. Finally, exchange-traded catastrophe derivatives<br />first appeared in the early 1990s but were unable to gain traction; now<br />they have been reintroduced to address the growing needs of both hedgers<br />and sellers of protection.<br /><br />
Demand for and supply of insurance-linked securities differ by the type<br />of ILS and change over time, even for the same type of ILS. For example,<br />reinsurance sidecars made a sudden appearance in the aftermath of the 2005<br />Katrina–Rita–Wilma hurricane season; they addressed an urgent need and<br />then quietly decreased in importance.<br />
<br />
The existence of dedicated ILS funds brings another interesting element into the dynamics of this market, since they are effectively the source of captive capital that provides a guaranteed<br />level of demand for some insurance-linked securities.<br />The financial crisis of 2007–2009 was a good test of the ILS market, as it<br />allowed market participants to identify weaknesses of some of the ILS structures.<br />More importantly, it underscored the general benefits of investing in<br />most types of insurance-linked securities that provide both yield and diversification<br />opportunities. It also drew attention to the need for proper expertise in the analysis of these financial instruments.<br />
<br />
The convergence between the insurance and capital markets is occurring slowly but steadily. Securitisation of insurance risk is an important part of<br />this process. It addresses the needs of both the holders of insurance risk and<br />the investors, and there is every expectation that the insurance-linked securities<br />market will continue to grow and develop.<br />
<br />
<br /></div>
Unknownnoreply@blogger.com0tag:blogger.com,1999:blog-6468318357328951173.post-91242320815410779332015-07-31T01:42:00.001-07:002015-08-09T12:07:13.605-07:00Introduction to Investing in Insurance Risk<div dir="ltr" style="text-align: left;" trbidi="on">
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This article provides a brief overview of concepts that are fairly obvious to most professionals in the investment and insurance field but may be unfamiliar to other readers. In addition, insurance risk is<br />
presented through an uncommon perspective that sheds light on its unique characteristics and corresponding investment considerations.<br />
<br />
<h3 style="text-align: left;">
Investing in Risk</h3>
There are no truly riskless assets. We always invest in risk. We might do it<br />
in the form of stocks, corporate bonds, real estate or treasuries, but ultimately<br />
the investment performance of these securities is predicated on their<br />
risks. We invest because we expect to earn a return commensurate with the<br />
risk we take in investing. In fact, we want the return to be higher than what<br />
the risk profile of an investment would imply.<br />
<br />
<h3 style="text-align: left;">
Insurance Risk is good</h3>
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It is too simplistic to say that “risk is bad”, and to think that it is something<br />
we want to avoid or minimise. Investing is always about risk. In fact,<br />
investors actively search for risk to invest in. As long as the compensation<br />
for taking on the risk is appropriate, the investment usually makes sense.<br />
<br />
A good investor is not the one who avoids risk; with excessive focus on<br />
avoiding risk such an investor will also strip out his return. A good investor<br />
is the one who invests in securities that together generate high risk-adjusted<br />
return appropriate to the investor’s goals.<br />
<br />
A good investor is certainly riskaverse, but only in the sense of not being willing to accept risk without proper compensation. As obvious as such statements may seem, the idea of<br />
seeking risk makes some uncomfortable. An investor must recognise that<br />
risk is good as long as it is the right kind of risk, the returns are commensurate<br />
with it and the overall investment objectives are satisfied. The portfolio approach to investing is important to every investor.<br />
<br />
A pension fund might have allocations to individual asset classes and benefit from the diversification it provides. The benefits of diversification explain why investments with low correlation to others are at a premium and being sought after. Certain types of insurance risk possess this desired quality of<br />
having low correlation with other asset classes.<br />
<br />
<h3 style="text-align: left;">
WHAT IS INSURANCE RISK</h3>
Insurance risk lacks a clear, unambiguous definition. It is generally defined<br />
as the risk being taken on by insurance companies in selling insurance<br />
protection. This could be interpreted in a very broad sense to include all<br />
risks faced by an insurance company in the course of its operations.<br />
<br />
So it could be said that investing in insurance risk is the same as investing in an<br />
insurance company. Considered in this broad sense, insurance risk includes<br />
all traditional investment risks – market, credit, operational and others – as<br />
well as the insurance risk defined in a more narrow way – that of insurance<br />
claims (obligations under insurance policies) being greater than expected, or<br />
greater than a certain level that the insurance company wants or is<br />
permitted to take.<br />
<br />
Even this definition is imprecise, since all the risks are intermingled and cannot be fully decomposed into individual elements. The more narrowly defined type of insurance risk would apply in cases of<br />
higher-than-anticipated losses due to factors such as random statistical fluctuations<br />
in the number of insurance claims or their severity, natural<br />
catastrophes or man-made disasters, spikes in mortality or fundamental<br />
shifts in longevity, and many others.<br />
<br />
Often such types of insurance risk either cannot be transferred to investors<br />
purely through the traditional equity or debt instruments issued by insurance<br />
companies, or are best transferred to capital markets in a different fashion.<br />
Insurance-linked securities (ILS) are structured to transfer to investors this<br />
type of risk, and are specifically designed to address unique issues of insurance<br />
companies. Most have to do with the transfer of “pure” insurance risk<br />
where other risks are excluded orminimised.<br />
<br />
They afford investors exposure to risks that are different from those embedded in the traditional securities and that are often only weakly uncorrelated to the behaviour of the financial<br />
markets.<br />
<br />
<h3 style="text-align: left;">
INSURANCE MARKETS</h3>
Before considering securities that are in some way linked or related to insurance,<br />
it is instructive to take a look at the insurance markets in general.<br />
Insurance markets have many unique features not found in other industries.<br />
Insurance companies are highly leveraged enterprises in the sense that their assets-to-shareholder equity ratio tends to be very high – particularly for life insurance companies and property-casualty companies in long-tail lines of business.<br />
<br />
While the degree of leverage across the capital markets has been going down (in some cases considerably), the insurance industry remains an exception. The diverse offerings of insurance companies comprise two main categories: life and health insurance, and property-casualty insurance (referred to as general insurance in many parts of the world). Though these two categories<br />
of insurance are quite distinct, some companies handle both life and<br />
property-casualty insurance.<br />
<br />
In describing insurance markets, it is also important to note that insurance<br />
is one of the most heavily regulated industries, a fact that, by itself, introduces<br />
a broad set of constraints and risks not found in other sectors.<br />
Moreover, the regulation to which insurance companies are subject is not uniform among jurisdictions, contributing to the fragmented nature of the insurance marketplace.<br />
<br />
Some jurisdictions impose price regulation and so insurance companies are not free to raise insurance rates on some of their products. In extreme cases, companies unable to raise rates for this reason<br />
have decided to exit certain products lines, but have encountered additional<br />
regulatory constraints, making this exit difficult. Few industries have to deal<br />
with such issues.<br />
<br />
Another phenomenon specific to the insurance industry is the underwriting<br />
cycle; it pertains primarily to property-casualty insurance, and, to a<br />
lesser degree, to health insurance. Insurance companies as a group go<br />
through periods of charging customers rates that are too low, leading to<br />
rates of return dropping below the required level (referred to as “soft”<br />
markets); followed by periods when the companies are able to raise their<br />
rates to the level where they generate rates of return in excess of the<br />
minimum required (“hard” markets).<br />
<br />
This cycle does not have a simple logical explanation and is seen by many as evidence of how inefficient the insurance markets are. Arguably, no other sector has such a clearly<br />
pronounced profitability cycle, with the possible sad example of the airline<br />
industry.<br />
<br />
While many factors drive the underwriting cycle – changes in<br />
macroeconomic conditions, shock events resulting from investment losses<br />
or losses due to natural catastrophes, the fear of losing customer relationships,<br />
and many others – it is also recognised that some of the factors are<br />
purely psychological (such as the herd mentality).<br />
<br />
Predicting the next turn in the insurance underwriting cycle is a favourite pastime of the sell-side<br />
equity analysts who cover the insurance sector. The underwriting cycle To sum up, insurance markets are unique because of a variety of factors, including fragmentation, particularly strict regulatory requirements, unusual risk and a significant degree of inefficiency. Deep understanding of<br />
such industry dynamics is a prerequisite to analysing many securities issued<br />
by this industry.<br />
<h3 style="text-align: left;">
SECURITIES ISSUED BY INSURANCE COMPANIES</h3>
Insurance companies issue some of the same types of securities as do most<br />
companies in other industries. We can invest in insurance through common<br />
stock, debt or preferred stock.<br />
<br />
The analysis of the common stock of insurance<br />
companies is based on the general principles of equity analysis, while<br />
taking into account also the specific features of the insurance industry. Other<br />
types of securities issued by insurance companies are not found in most<br />
other sectors.<br />
<br />
An example would be surplus notes, which are securities<br />
similar to the trust-preferreds issued by banks. The securities issued by<br />
insurance companies are a relatively small part of the global capital markets,<br />
reaching at most 3% of their total size. In the US, insurance companies provide two types of financial statements: traditional statements based on the Generally Accepted Accounting Principles (GAAP), and statutory statements mandated by insurance regulators.<br />
<br />
The volume of information contained in these statements is greater<br />
than what would typically be available for a company in another industry.<br />
Detailed exhibits provide a wealth of additional information. Both the<br />
GAAP and the statutory statements, along with other data released by<br />
insurance companies, help investors analyse the companies and value the<br />
securities they issue. The availability of the additional information,<br />
however, does not make the analysis easier and the uncertainty lower.<br />
<br />
There are too many industry-specific issues that make the analysis different from<br />
that of other companies, and these issues present unique challenges. In the<br />
simplified analytical framework, we may often wonder why price-to-book<br />
ratios of insurance companies exhibit idiosyncratic behaviour, and what<br />
drives the difference in the price-to-book and other ratios between companies<br />
that appear to be rather similar based on their balance sheets, income<br />
statements and the business they conduct. Only a deeper level of analysis<br />
can answer such questions.<br />
<br />
We may think that diversification can be achieved simply by investing in<br />
stocks or bonds issued by insurance companies, since they contain the<br />
“pure” insurance risk such as that of losses related to natural catastrophes or<br />
changes in mortality rates. However, these risks are rarely the main drivers<br />
of insurance stock performance. For most insurance companies, the main<br />
component of their profits stems not from underwriting income but from the investment returns on their asset portfolios.<br />
<br />
This explains why insurance companies, with their huge balance sheets and assets invested mostly in<br />
bonds and stocks, are heavily exposed to market risk. Life insurance stocks<br />
are seen by many as a beta play, as opposed to an uncorrelated asset.<br />
Figure 1.1 overleaf illustrates the performance of the Dow Jones US<br />
Insurance Index relative to the S&P 500 Index and the Dow Jones Industrial<br />
Average.<br />
<br />
Correlation of the insurance index returns with the markets for the<br />
time period illustrated in Figure 1.1 was 80%, showing that investing in<br />
insurance stocks in and of itself does not necessarily provide diversification,<br />
because insurance companies are, to a significant degree, leveraged investment<br />
vehicles.<br />
<br />
Warren Buffett puts it in slightly different terms by using the concept of<br />
float: “Float is money we hold but don’t own. In an insurance operation, float<br />
arises because premiums are received before losses are paid, an interval that<br />
sometimes extends over many years.During that time, the insurer invests the<br />
money.”<br />
<br />
This statement, repeated with minor variations in numerous annual<br />
letters by Buffett to the shareholders of Berkshire Hathaway, explains both<br />
the concept of leverage in insurance and why many insurance stocks have a<br />
high degree of correlation with the financial markets.<br />
<br />
<h3 style="text-align: left;">
INSURANCE-LINKED SECURITIES</h3>
ILS are defined as financial instruments, other than traditional equity and<br />
debt securities issued by insurance companies, which carry insurance risk or<br />
a type of risk that is closely related to it. Examples of the risks included in<br />
insurance-linked securities are property-catastrophe risk, mortality,<br />
<a href="http://2.bp.blogspot.com/-quUlX_ejm_U/VbsxjeeDUtI/AAAAAAAAajU/TRMzQWk3jMI/s1600/performance%2Bof%2Binsurance%2Bequities%2Brelative%2Bto%2Bstock%2Bmarkets.jpg" imageanchor="1" style="clear: left; float: left; margin-bottom: 1em; margin-right: 1em;"><img border="0" height="286" src="http://2.bp.blogspot.com/-quUlX_ejm_U/VbsxjeeDUtI/AAAAAAAAajU/TRMzQWk3jMI/s400/performance%2Bof%2Binsurance%2Bequities%2Brelative%2Bto%2Bstock%2Bmarkets.jpg" width="400" /></a>longevity and insurance loss reserve adequacy. ILS can also include many of the traditional risks such as market, credit and interest rate risks, but it is the inclusion of the significant degree of insurance risk that defines them.<br /><br />
The seemingly irrelevant question of what asset category ILS belong to is<br />important. ILSs are normally classified as alternatives, but they come in many shapes and forms even for the same type of risk. These securities can be structured as fixed income instruments or as equities. Some ILS come in the form of derivatives while others most closely resemble private equity<br />investments. A dedicated ILS fund can be limited to investing in only catastrophe<br />bonds or have a broader mandate of investing in various types of insurance-linked securities and types of insurance risks they contain.<br />
<br />
The fund mandate determines how an investment in the fund itself is classified – whether it necessarily falls in the category of alternatives and, if the answer<br />is positive, where it is placed within that category. The uncertainty as to the<br />appropriate allocation bucket exists even in the cases of direct investment<br />rather than that through a fund.<br /><br />
The classification may affect the flow of funds to ILS and insurance linked<br />strategies since they are relatively new and have not earned standard<br />allocations afforded to the more traditional asset classes and investment<br />strategies. The size of the insurance-linked securities markets is very small relative to that of the global financial markets, and even relative to the total value of<br />securities issued by insurance-related entities.<br />
<br />
While exact figures are not available, the total size of the traded insurance risk (ILS), even when broadly defined and including both property-casualty- and mortality/longevity linked<br />securities, does not exceed US$70 billion. The following figure shows estimates<br />of the insurance-linked securities markets in relation to the broader financial<br />markets.<br />
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<a href="http://2.bp.blogspot.com/-HON4XDam2dw/VbsyxoCw1_I/AAAAAAAAajg/WvpIKcd2ROM/s1600/relative%2Bsize%2Bof%2Bthe%2Bmarket.jpg" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" height="360" src="http://2.bp.blogspot.com/-HON4XDam2dw/VbsyxoCw1_I/AAAAAAAAajg/WvpIKcd2ROM/s640/relative%2Bsize%2Bof%2Bthe%2Bmarket.jpg" width="640" /></a></div>
<br />
<span style="color: magenta;"><b>Notes</b>: Derivatives, whose total notional amount is a multiple of the stock and bond markets<br />combined, are not included. Estimates are as of 2009 and are based on data from the Bank for<br />International Settlements, SIFMA, World Federation of Exchanges, World Bank, Milken<br />Institute, World Economic Forum, LISA, Conning, and McKinsey. Only publicly traded<br />securities are considered in estimating the size of the global financial markets. </span><br />
<br />
<span style="color: magenta;">There are no adjustments for the cases of one public company owning stock of another publicly traded company. Securities that have connection to the insurance industry are broadly defined and<br />include those issued by companies involved in other businesses in addition to insurance. A<br />broad definition of insurance-linked securities is used to include such types of ILS as life<br />settlements and industry loss warranties. Most private deals that can be reasonably character -<br />ised as ILS-type transactions are also included.</span><br />
<br />
<span style="color: magenta;"><span style="color: black;">standard investment analysis used for most other industry sectors. The<br />specialised expertise is a significant source of competitive advantage in the<br />investment analysis of insurance equities and debt.<br /> </span></span><br />
<span style="color: magenta;"><span style="color: black;">Investing in securities issued by insurance companies does not provide<br />the diversification that might be expected from exposure to the risks of<br />insurance losses being greater than expected due to the fluctuations in the<br />frequency or severity of insurance claims, changes in mortality rates, or<br />other risks unique to the insurance industry. </span></span><br />
<br />
<span style="color: magenta;"><span style="color: black;">In investing in corporate securities issued by insurance companies, most of the risks are not “pure”<br />insurance risks but risks common to the financial markets. This explains the<br />high degree of correlation between the investment performance of the insurance<br />sector and the markets as a whole.</span></span><br />
<br />
<h3 style="text-align: left;">
<span style="color: magenta;"><span style="color: black;">Search for uncorrelated return</span></span></h3>
<h3>
<span style="color: magenta;"></span></h3>
<span style="color: magenta;"><span style="color: black;">Investors never stop their search for assets that improve the performance of<br />their investment portfolios, either through extra yield or through exposure<br />to uncorrelated assets. The value of truly low correlation with the markets<br />became painfully obvious during the financial crisis that started in 2007. By the end of 2008 all correlation assumptions broke down, and assets with historically low correlation all of a sudden started moving in sync. They were all moving in the same direction – down – and so were the supposedly diversified investment portfolios. Having investments with low beta generally<br />improves portfolio risk-adjusted returns and contributes to the goal of<br />capital preservation.</span></span><br />
<br />
<h3 style="text-align: left;">
<span style="color: magenta;"><span style="color: black;">Insurance-linked securities as a portfolio diversifier</span></span></h3>
<h3>
<span style="color: magenta;"></span></h3>
<span style="color: magenta;"><span style="color: black;">While insurance-linked securities are not zero-beta assets, they do represent<br />a valuable and effective form of diversification. Many of them provide exposure<br />to risks that have a low degree of correlation with the rest of the<br />financial markets, while still generating a very competitive yield. </span></span><br />
<br />
<span style="color: magenta;"><span style="color: black;">Securities such as cat bonds issued after 2008, designed with an express intent to strip<br />away, as much as possible, all risks besides the true insurance risk of natural<br />catastrophes, provide a good illustration of this diversification.<br /> </span></span><br />
<span style="color: magenta;"><span style="color: black;">A storm on Wall Street might shake the very foundation of financial<br />markets, but it is not going to lead to a hurricane in Florida or an earthquake<br />in California. A catastrophe bond is not going be triggered because of the<br />condition of the markets. The relatively low degree of correlation with<br />market risk is the greatest advantage of insurance-linked securities, and for this reason insurance-linked securities can be an important component of<br />most investment portfolios.</span></span><br />
<br />
<span style="color: magenta;"><span style="color: black;"> </span> </span></div>
Unknownnoreply@blogger.com0tag:blogger.com,1999:blog-6468318357328951173.post-54865352679329273962005-08-10T07:30:00.000-07:002019-03-15T07:42:25.889-07:00Insurance Policy Number Definition and examples<div dir="ltr" style="text-align: left;" trbidi="on">
<div class="separator" style="clear: both; text-align: center;">
<a href="http://2.bp.blogspot.com/-lSubfBKO0HM/VcnLaHzve9I/AAAAAAAAauU/VmOWr_n9uBE/s1600/insurance%2Bpolicy%2Bdefinition.jpg" imageanchor="1" style="clear: left; float: left; margin-bottom: 1em; margin-right: 1em;"><img alt="health insurance policy number" border="0" height="212" src="https://2.bp.blogspot.com/-lSubfBKO0HM/VcnLaHzve9I/AAAAAAAAauU/VmOWr_n9uBE/s320/insurance%2Bpolicy%2Bdefinition.jpg" title="health insurance policy number" width="320" /></a></div>
The <a href="https://websitesgh.com/listing/insurance-centre-of-excellence-ltd/#" target="_blank">Insurance</a> Policy number is part of the insurance policy structure which is generated uniquely to the insured and serves as an identification number to the Insurance Policy.<br />
<br />
<a href="https://en.wikipedia.org/wiki/Insurance_policy" target="_blank">Insurance Policy</a> Number is the unique number that identifies the insured to the insurance about the contract which is also termed as policyholder. Insurance number is different from the group number in a sense that the group number identifies a group of workers under a company or organization which has the same policy that benefits all insured holders.<br />
<h3 style="text-align: left;">
Where can i find my Insurance Policy Number?</h3>
The policy number can be found on the health insurance card or any insurance card. The health insurance card or most insurance cards are printed with some general standard information. When an insurance policy becomes effective, the insurance card is received by the insured. The health insurance card contains the following information:<br />
<ul style="text-align: left;">
<li>The Name of the Health Insurance Company.</li>
<li>The Member Name or the insured.</li>
<li>The name of dependents if rectified on the forms.</li>
<li>A unique identification number of the insurance policy number.</li>
<li>The Group Number which identifies a specific of insured organization with a contract.</li>
<li>Group Name as in XYZ.. company.</li>
<li>The address of the insured</li>
</ul>
The following is an example of the ABC health insurance policy card.<br />
<br />
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<a href="http://3.bp.blogspot.com/-TpcbIz3a5Aw/VcnRHpqj0-I/AAAAAAAAauk/whKbKihWieI/s1600/ABC%2BInsurance%2Bpolicy%2Bcard.gif" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img alt="ABC insurance policy card" border="0" src="https://3.bp.blogspot.com/-TpcbIz3a5Aw/VcnRHpqj0-I/AAAAAAAAauk/whKbKihWieI/s1600/ABC%2BInsurance%2Bpolicy%2Bcard.gif" title="ABC insurance policy card" /></a></div>
<br />
If the health insurance card is misplaced, a person may be denied service at a healthcare centre unless the card is produced. Some healthcare attendants can allow you to provide your insurance policy number which can be able to track your insurance company to verify your information.<br />
<div style="left: -99999px; position: absolute;">
You can normally find your policy number on your health insurance card.</div>
<div style="left: -99999px; position: absolute;">
You can normally find your policy number on your health insurance card.</div>
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You can normally find your policy number on your health insurance card.</div>
<h3 style="text-align: left;">
Insurance Policy Definition</h3>
The insurance policy is a contract or an extensive standard contract form which is between the insured and the insurer. The insurance policy control, decide, regulate, direct and dictate claims which the insurance company is legally bound to pay to the insured.<br />
<br />
<h3 style="text-align: left;">
What is an Insurance Claim?</h3>
The insurance claim is the law or statement of cause of action which states and set facts sufficient to justify a right to sue to obtain money, property, or the enforcement of a right against another party in the insurance policy.<br />
<br />
Therefore <a href="http://www.investopedia.com/articles/pf/07/five_policies.asp" target="_blank">knowing more about the insurance claims</a> is very effective to a successful and valid insurance payment. However, insurance claims can be drafted with different levels of clauses and policies normally called a linguistic tower of babel. Before anyone signs an insurance contract, the claims and policy must be understood.<br />
<br />
<h3 style="text-align: left;">
What is an Insurance Group Number?</h3>
The group number has multiple policy numbers associated with it as a single policy entity pertaining the group. The insurance policy number solely identifies an individual or entity and their family to a contract or policy holder. It also serves as a reference number which is connected to your file in the insurance agency.<br />
<br />
Without the insurance policy number, any Doctor or Medical practitioner cannot charge or bill the insurance company. Therefore your insurance number should be kept safe or even memorized if appropriate in case your insurance card get missing.<br />
<br />
Different insurance companies identifies the policy number using a number format. The insurance company anyone belongs has a different identification policy number. The below images shows the examples of some of the top health insurance companies and their id cards with the policy numbers.<br />
<h3 style="text-align: left;">
What is the purpose of a Policy Number?</h3>
The purpose of the policy number is to enable the insurance company and the healthcare agent to track the billing of the insured. The services rendered will be billed to the insurance company. Then the insurance company will use the policy number to pay the healthcare agent.<br />
<h3 style="text-align: left;">
What to do when the insurance card or number of lost.</h3>
When the <a href="https://www.gov.uk/lost-national-insurance-number" target="_blank">insurance card or you lost your policy</a> number, the first thing to do is to call the insurance provider immediately. This first action helps to prevent fraud. It also helps to prevent future denial of health insurance services when needed.<br />
<h3 style="text-align: left;">
What is an insurance policy card fraud?</h3>
<div class="MsoNormal" style="line-height: normal; mso-margin-bottom-alt: auto; mso-margin-top-alt: auto;">
<span style="font-family: "times new roman" , "serif"; font-size: 12.0pt;">Every individual has the right to a good and affordable medical care. Unfortunately some criminals use various fraud schemes to use other peoples medical insurance information to receive care and services without paying or billing to their own card. This can also be termed as medical identity theft. An investigation and study by the Ponemon Institute estimated about 1.5 million people in the United States have been victims of this crime.</span></div>
<div class="MsoNormal" style="line-height: normal; mso-margin-bottom-alt: auto; mso-margin-top-alt: auto;">
<span style="font-family: "times new roman" , "serif"; font-size: 12.0pt;"><br />If your insurance card get missing or someone get hold of your insurance policy number with your personal details, your insurance card can be used to receive medical services.<br />They can also be used to obtain money by falsifying claims or medical services. </span></div>
<div class="MsoNormal" style="line-height: normal; mso-margin-bottom-alt: auto; mso-margin-top-alt: auto;">
<br /></div>
<h3 class="MsoNormal" style="line-height: normal; text-align: left;">
<b><span style="font-family: "times new roman" , "serif"; font-size: 13.5pt;">Steps to take to prevent insurance policy card fraud.</span></b></h3>
<ul style="text-align: left;">
<li><span style="font-family: "symbol"; font-size: 12.0pt;"><span style="mso-list: Ignore;"><span style="font: 7.0pt "Times New Roman";"></span></span></span><span style="font-family: "times new roman" , "serif"; font-size: 12.0pt;">You should frequently request a copy of your prescription or medical claims history by contacting or calling your insurance provider. By reviewing your claims history you can pinpoint any fraud or prevent any further fraud. </span></li>
</ul>
<ul style="text-align: left;">
<li><span style="font-family: "symbol"; font-size: 12.0pt;"><span style="mso-list: Ignore;"><span style="font: 7.0pt "Times New Roman";"> </span></span></span><span style="font-family: "times new roman" , "serif"; font-size: 12.0pt;">Guard your card. Loaning an insurance card or using an expired card to get medical care is fraud. So is keeping or putting someone on your contract who shouldn’t be there, like an ex-spouse.</span></li>
</ul>
<ul style="text-align: left;">
<li><span style="font-family: "symbol"; font-size: 12.0pt;"><span style="mso-list: Ignore;"><span style="font: 7.0pt "Times New Roman";"></span></span></span><b>Don’t rent when you can buy. </b>If you’re renting durable medical equipment, like a wheelchair or respirator, make sure you know the purchase price of the equipment. The rental company can’t keep billing you once your total rental payments exceed what it would cost to buy the equipment.</li>
</ul>
<h3>
Is the Account Number the same as the policy number?</h3>
<div style="margin-bottom: 12.0pt;">
No, the account number is the 10 number digit which is associated with the SF payment plan. A person may or may not use a payment plan to combine various lines of business into one payment or to break up the payments into smaller amounts.</div>
<h3>
Structure and parts of an Insurance Policy</h3>
<br />
Early insurance policies had a tendency to be composed on the premise of each and every kind of risk (where risks were characterized to a great degree barely), and a different premium was ascertained and charged for each. This structure demonstrated unsustainable in the setting of the Second Industrial Revolution, in that a normal substantial aggregate may have many sorts of risks to guarantee against.<br />
<br />
Insurers have been criticized in some quarters for the development of complex policies with layers of interactions between coverage clauses, conditions, exclusions, and exceptions to exclusions. It is therefore of the essence for the insured to be able to either read the clauses or hire a savvy lawyer to go through these complicated clauses. Most individuals or workers belong to a specific insurance domain or what is termed as a group policy contract. In this case these group is given a group policy number.<br />
<h2 style="text-align: left;">
Parts of an Insurance Policy Contract.</h2>
<h3 style="text-align: left;">
Policy Declarations</h3>
The Declarations of an insurance contract recognizes, distinguish or identifies the insured client, his or her address, the company that is insuring the client, the properties and risks which are covered, the terminal points or boundaries of the amount of insurance, the deductions which will be applied, the premium amount and the period of the insurance policy. <a href="http://2.bp.blogspot.com/-CWn5-SXPMjE/Vct9I-nAXBI/AAAAAAAAau8/y4GIQ6ZPW9o/s1600/insurance%2Bpolicy%2Bnumber%2Bdeclarations.jpg" target="_blank">example of a policy declaration.</a><br />
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</div>
<br />
<h3 style="text-align: left;">
Policy Definitions</h3>
The definition defines and describes the important terms which are used in the language of the policy. The definition makes the policy clear and distinct describing every single information about the contract.<br />
<h3 style="text-align: left;">
Insuring Agreement</h3>
The insuring agreement describes and accounts the relevant characteristics of the risk or the covered perils or the nature of the coverage about the insurer and the insured. It list all the guarantees and assurances of the insurance company.<br />
<br />
<h3 style="text-align: left;">
Policy Exclusions</h3>
The policy exclusions describes the away coverage from the insuring agreement by pinpointing the properties, risks, hazards and losses which when arises from specific events which will not be covered by the insurance company.<br />
<br />
<h3 style="text-align: left;">
Policy Conditions</h3>
The policy conditions are provisions, duties and commitments, obligations and responsibilities, rules of conduct which are required for coverage. If the policy conditions are not me, the insuring company can deny the claim.<br />
<br />
<h3 style="text-align: left;">
Policy Endorsements</h3>
<div style="text-align: left;">
The policy endorsements adds additional forms which are attached to the policy form. The endorsement form normally modifies the policy in some way not limited by the conditions or on the existence of a condition in the policy. Most policy endorsements are difficult to read for someone who is not a lawyer and therefore it is required to hire a lawyer if you are handling an organization or a company. </div>
<h3 style="text-align: left;">
Policy Jackets</h3>
The policy jacket is the cover, the envelope or binder with pockets in which the policy are delivered with.<br />
<br />
<h3 style="text-align: left;">
Policy Rider</h3>
A policy rider is used to convey the terms of a policy amendment and the amendment thereby becomes part of the policy. Riders are dated and numbered so that both insurer and policyholder can determine provisions and the benefit level. Common riders to group medical plans involve name changes, change to eligible classes of employees, change in level of benefits, or the addition of a managed care arrangement such as an Health Maintenance Organization or Preferred Provider Organization (PPO).</div>
Unknownnoreply@blogger.com1