CATASTROPHE MODELLING
FOR MUTUALS
Taking a broader view
August 2011
Catastrophe modelling
Catastrophe modelling (commonly known as ‘cat modelling’) is
the process of using computer-assisted calculations to estimate
the losses that may be sustained due to a catastrophic event such
as a hurricane or earthquake. Several disciplines are involved,
including statistics/probability, physical sciences, engineering,
technology, actuarial science and insurance. A set of assumptions
and parameters are generated and applied to represent complex
natural and man-made phenomena and events. While the process
is rigorous and highly analytical, it also requires expert judgement
in terms of model development and results interpretation.
Development
Cat modelling for insurance purposes has been around since
the late nineteen eighties. Spurred by increasingly affordable
computing power, early proponents combined developments
in the field of Geographical Information Systems software with
greater scientific understanding of natural hazards to develop
the first commercial catastrophe models.
Concurrent with a number of major catastrophe events, including
Hurricane Gilbert in 1988; Hurricane Hugo and the Loma Prieta
earthquake in 1989 and Typhoon Mireille in 1991, simple statistical
models based on limited historical data were developed. Initial
industry interest was limited, in part due to the models predicting
financial losses far greater than historical market experience;
regardless of the soundness of these predictions, they were not well
received by many market practitioners.
The unprecedented size of Hurricane Andrew in 1992 proved
a turning point with confirmation that financial losses much
larger than previously indicated were indeed possible, leading
to increased pressure from regulators, rating agencies and
shareholders for better estimates of potential losses. The
hard reinsurance market conditions following this event also
incentivised the creation of a new group of model-focused
mono-line catastrophe reinsurers, collectively referred to as
the ‘class of 1992’.
A new generation of loss calibrated probabilistic models were
developed during the nineteen nineties, and the territories
and perils covered began to expand. Growth in the use of
cat models continued beyond the millennium, with several
generations of ever more sophisticated models evolving, and
greater acceptance of these models by the insurance industry.
Scope
Today, catastrophe models have become a key tool in assessing
capital adequacy, and are used by regulators, rating agencies,
insurers and reinsurers alike. Over 92%1 of global Gross
Domestic Product is now covered by the top four commercial
models. The near ubiquitous adoption of models by the
industry in recent years has made it increasingly difficult for
any organisation to ignore, regardless of their understanding
or acceptance of the underlying technologies.
Unfortunately greater usage by the industry has not always gone
hand-in-hand with greater understanding; the theoretical and
common-sense limitations of cat models and their output are
rarely critically examined, and results all too often taken at face
value. In this article we discuss some of the areas where caution
is needed, and give practical examples of some of the issues
based on recent catastrophe events.
Source: Willis Re, CIA World Factbook 2010.
1
N.B. Throughout this article we use the word “Mutual” as short-hand for any policyholder owned entity, including Co-operatives, Mutuals, Risk Retention Group, Trusts,
Reciprocals, Exchanges, Pools, Societies, Affinity and Group Captives, and any other form of policyholder or association owned insurance vehicle. We trust the reader will
recognise the convenience of using a single term to cover all forms of policyholder owned insurance entities.
Willis Re Catastrophe Modelling for Mutuals | 1
How have the models performed recently?
Although the last 18 months have seen an unprecedented series
of human tragedies arising from natural catastrophe events, the
insurance and reinsurance industry has generally responded well.
Whilst it is important that we reflect on how providing valuable
support to the long process of rebuilding lives and communities
can be improved, we should also take this opportunity to evaluate
the tools that we use to manage and regulate the risks arising
from future events.
New Zealand
It has long been known that New Zealand is acutely vulnerable to
tectonic movements. Significant fault lines bisect the islands, with
a major fault moving up the western coast of the South Island
before splitting in two just south of the capital Wellington. For
those tasked with the trying to predict where the next major quake
will strike, the focus was always the city of Wellington thanks to
a prominent fault running through the city and it being a major
centre of insured exposure in the country. In contrast to this, the
Canterbury region was always considered to be relatively safe,
being thought to lie more than 80 miles away from the nearest
known major fault line.
The 6.3 Movement Magnitude event that struck the Canterbury
region of New Zealand on February 22, 2011 arose from a fault
line in the Earth’s crust of which seismologists were previously
unaware. Seismologists believe that a fault which might have lain
dormant for thousands of years has sprung back into life with
devastating consequences. ‘It’s not a new fault in the sense that
it has only just been created but it is a new fault that has only
just been discovered,’ according to Dr Roger Musson, head of
seismic hazards and archives at the British Geological Society2.
‘Some fault lines are very easy to see but the one under
Christchurch is covered by sediment and would have been
invisible without thorough geophysical searches.’
As a result this fault was simply not included in the catalogue
of modelled events, and the broader risk of a major event in the
Canterbury area was therefore massively under represented.
In the meantime industry stakeholders relying on commercially
available models have had to deal with the severe financial
repercussions of an unexpectedly large loss as well as the terrible
human tragedy of the event.
Japan
On March 11, a major earthquake and tsunami event hit
Japan, which at a 9.0 magnitude was the most powerful in
modern times. The earthquake triggered extremely destructive
tsunami waves of up to 38.93 metres (128 ft) in some cases
travelling up to 10 km (6 miles) inland. These events brought
destruction along the Pacific coastline of Japan’s northern
islands, resulted in the loss of thousands of lives and
devastated entire towns.
As with the New Zealand event that struck less than a month
before, this fault was not well represented in the catalogue of
modelled events. A quake of this magnitude usually has a long
rupture length (typically at least 480 kilometres/300 miles)
and generally requires a long, relatively straight fault surface.
Because the local geology in the area of the rupture is not
very straight, the magnitude of this earthquake was a surprise
to some seismologists, many of whom thought that such an
event would not exceed 8.5. The ‘cascade’ of earthquakes that
made up this event is forcing a re-thinking in the seismological
community about the probability and maximum size of major
events, not just in Japan but in many areas world-wide.
In addition to an unexpectedly large loss compared to catastrophe
models, this event highlighted other limitations in commercial
available models; the scope of losses they consider. By way of
example, the three commercially available models for Japan
have no representation of the Tsunami peril currently available.
There is a considerable risk of automatic fire suppression systems
accidentally discharging in the event of an earthquake, something
only one of these models takes into account.
A further issue was soil liquefaction; where wet soil loses strength
in response to an earthquake shaking causing it to behave like a
liquid. This may cause rigid structures, like buildings and bridges,
to tilt or sink into the liquefied deposits. The same effect can be
felt by agitating your feet in damp sand on a beach; pretty soon the
sand will turn soft and water will rises to the surface. In Tokyo, 373
kilometres (232 miles) from the epicentre of the earthquake, soil
liquefaction was evident in areas of reclaimed land, with about 30
buildings destroyed and over 1,000 damaged4.
Of the commercially available models for Japan, one allows
for soil liquefaction, one has a partial allowance (for Marine
Cargo business only), and one takes no account of it.
In an interview with the Independent newspaper published February 23, 2011.
Estimated at Omoe peninsula, Miyako city, Iwate prefecture. Yomiuri Shimbun evening edition April 15, 2011.
Yomiuri Shimbun, ‘Liquefaction Damage Widespread’, April 10, 2011.
2
3
4
2 | Willis Re Catastrophe Modelling for Mutuals
Australia
Between September 2010 and April 2011 Australia suffered
a series of very significant flooding events, the rebuilding
costs for which are estimated to be as much as AUS 20 billion
(USD 19.9 billion, or about 1.5 percent of gross domestic
product) by some commentators.
These events proved to be a significant insurance loss for nearly
all market participants, with concern raised not just by the scale
of losses, but also the unexpected frequency of events. Even where
companies would not have been adversely impacted by losses
occurring during a short period of time or limited geographical
area, the aggregated impact of all events ultimately had a major
impact on their balance sheets.
None of these flooding events were contemplated by standard
commercially available catastrophe models. Whilst there has been
a flurry of new research into insurance and flood related issues,
it is clear that for some years to come there will be little or no
catastrophe model coverage for this key peril by the commerical
modelling companies. It is worth noting that brokers such as
Willis Re, an acknowledged leader in this field, have been building
flood models for some years.
Chile
In the early hours of February 27, 2010 a magnitude 8.8 earthquake
occurred of the coast of the Maulé region of Chile.
However, the financial cost pales into insignificance next to the
estimated 562 lives lost and enormous impact on the lives of
survivors; in the most affected region over 20% of the population
found their homes were destroyed or severely damaged by the
earthquake and resulting tsunami.
From a catastrophe modelling standpoint, the key issue arising
from the Chile event is one of calibration; now that a reasonably
accurate picture of the claims position has emerged, we can draw
the following conclusions about how the models performed:
•Losses from the Personal Lines segments were overstated
•Losses from Commercial lines were understated
•The commercially available models in typical use seem
to have significantly overstated the impact of ‘post loss
amplification’; the growth of claims payments due to higher
repair costs resulting from a shortage of material and labour
following a major event, regulatory intervention, coverage
expansion and other external factors.
While in some Chilean portfolios the modelled loss came close to
the ultimate loss, the results were not always what they seemed.
For example, the model predicted that for accounts containing
large and complex industrial risks, the damage to individual
sites would be frequent, but not severe. Loss experience typically
shows that the losses were made up of a few severe losses, but
that the majority of sites escaped without insured loss.
With ultimate insured losses expected to settle at USD 8.5 billion,
at the time this event was the costliest insured earthquake since
the 1994 Northridge event in California.
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Lessons learned
What can we draw from the turbulence generated by these
events? Detailed lessons from these and other natural
catastrophes will emerge over the next few years, and new
geo-physical data, together with updated scientific thinking,
will be incorporated into catastrophe models as part of the
vendors one to two year update cycle. However, there are
some broader questions that should be asked by those that
rely upon models to help their financial decision making:
•Do you understand which perils are modelled and which
perils are not modelled?
–– What other methods are available to consider those
perils that are not modelled?
•Are all of the lines of business with direct and indirect natural
catastrophe exposure you offer covered by modelling?
–– For example, Personal Accident, Workers
Compensation, Third Party Liability etc.
•For a modelled peril, what is covered by the model?
–– Even if something is included (e.g. earthquake), are all
of the associated perils also modelled (e.g. tsunami, soil
liquefaction)?
–– What allowance should be made for associated perils
that are not modelled?
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•What would be the impact of the largest realistic event you
can conceive?
•What would be the impact of multiple events? Have you
stress tested your reinsurance programme against realistic
disaster scenarios including a series of events?
These questions are pertinent to all insurers, but have particular
resonance to Mutuals. While working with a particular affinity
group of policyholders creates a greater degree of focus and
specialty expertise for Mutuals, it often exposes them to
geographic and homogeneous concentrations of risk, magnifying
the effects of catastrophic losses. Moreover conventional ‘for
profit’ insurance capital is inherently more flexible than hard
earned policyholder-generated Mutual capital, which is not so
easily replenished after a large catastrophe. All this dictates
a conservative approach to Mutual capital preservation, and
demands effective management of Reinsurance Capital, but
first and foremost underlines the importance of an accurate
assessment of catastrophic exposure.
How can these challenges be addressed by mutuals?
The good news is that there are an increasing number of ways to
address the issues raised; these range from the more traditional
scenario based approach to sophisticated analyses:
•Scenario based approaches
•Understanding what is covered
•Use of multiple models
•Model interpretation
•Portfolio analysis
Scenario based approaches
A scenario test is an assessment of the impact of a specific
event without regard to the likelihood of the event. The idea is
to realistically reflect the impact of the event on all aspects of a
company’s exposure to an event. Unlike stochastic modelling,
stress testing:
•Is concrete and intuitive
•Does not require selection of probability levels
•Does not require understanding of overall dependencies
among interlinked risks
•Avoids ‘black-box syndrome’, and offers a level of
transparency not always possible from catastrophe models
Stress tests have recently experienced a resurgence in popularity
amongst regulators and rating agencies, who find them a
valuable supplement to other forms of modelling. Lloyd’s of
London for example are seen by many as a leader in this field.
The Corporation of Lloyd’s has devised a set of what it calls
Realistic Disaster Scenarios (RDS) to stress test both individual
syndicates and the market as a whole to see how they would
stand up to chains of accumulated exposure in very extreme
cases. First introduced in 1995, the scenarios are defined in
detail once a year by Lloyd’s and require the underwriters
to consider all sources of aggregating underwriting losses
(e.g. Marine, Property, Specie, WCA etc.) from:
•Any exposure that can generate losses above the
‘de-minimis’ level
–– 10% of Capacity for Gross Losses and 3% of capacity for
Net Losses
•Exposures to a number of mandatory scenarios
–– Currently 13 in total, such as two consecutive Atlantic
Hurricanes
–– A mixture of windstorm, earthquake, flood and
man-made events
What is covered?
Understanding exactly what is represented by the results of a
cat model is integral to conservative use. For example, exposure
to an earthquake may encompass the effects of many elements:
•Shake
•Fire following
•Sprinkler leakage
•Landslip
•Liquefaction
•Post loss amplification
Once an understanding of this has been established for the
model being used, informed decisions about any additional
considerations can be made. Unfortunately it is not always clear
from commercial model documentation what is covered, and
whether that coverage is an explicit part of the model or implied
by its calibration. Over a long period of time, and through
frequent model use and a close working relationship with the
model vendors, Willis Re has built up a detailed understanding
of these parameters which can be put to good use by our clients.
Use of multiple models
A key tool in the conservative management of catastrophe
exposure is the use of multiple models; this provides an
opportunity to make allowances for the strengths and
weakness of different models, and to provide a balance in
cases where there is divergence in the science that underlies
catastrophe models.
There can be considerable difference in both age and
provenance of competing models. It may be of assistance to
balance these, together with considering how open a vendor
has been to discussing the underpinnings of their model,
when basing commercial decisions upon the output. There
may also be significant differences in frequency, magnitude
and direction of model revisions between different models.
A multi-model approach may also reduce your exposure to a
sudden or large change in one vendor’s model.
Whilst in many ways this seems like common sense, and one
that would be adopted in many other areas of business, many
companies are reluctant to do so given the not inconsiderable
costs that a single Mutual organisation needs to incur to
operate a single model, let alone multiple models. These costs
arise not just from the very large licensing fees demanded
by model vendors, together with any additional hardware
needed, but also from the time that must be taken by trained
staff in preparing data, running the model and reviewing
Willis Re Catastrophe Modelling for Mutuals | 5
the results. Complex issues surrounding the mathematical
blending of results from different models also need to be taken
into consideration.
However, the most common reason that organisations resist
a multi-model approach can be summarised in the question
‘why can’t you just give me one number?’ This is an
understandable reaction to multiple results from those not
familiar with modelling technology, and one that can only be
resolved with a transparent debate at the highest levels of the
organisation.
Addressing these issues can be much simplified using a broker
who regularly runs multiple models, resolves the complex
technical issues associated with blending the results of different
models (and has the infrastructure to do so), and can explain and
help you interpret the differences between results. Many of our
clients, from those with the simplest modelling requirements
to those with extraordinarily complex multi-national portfolios,
value our assistance in this regard.
Model interpretation and sensitivity testing
As major users of the commercial models, much of our time is
spent in understanding which assumptions are most important
to a particular model, and the impact they have on the results.
This process involves testing sensitivity of the model output to
each individual input parameter:
•Geographical Variation
•Geographical Resolution
•Coverage Type
•Construction Characteristics
•Occupancy Type
•Policy Terms and Conditions
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The results of this testing, assists us in better answering common
questions raised by clients:
•Why do different models produce different output?
•Why do model outputs change from year to year?
•How should I prioritise investment in data capture
and reporting?
•Can you verify how the model represents science
and engineering?
Consideration of these common questions provides a robust yet
practical test of any model’s suitability to be used to manage
capital on behalf of your stakeholders.
Conclusion
There is good reason to be aware of the issues raised by
Catastrophe Modelling, which in conjunction with increasing
competition and regulatory pressure are challenging
the conservative capital model traditionally employed by
most Mutuals.
Like generals conducting military exercises based upon the
last war, or regulators changing capital rules to prevent a
recurrence of the previous financial crisis, cat modelling can
never provide the proverbial ‘crystal ball’ and remains at best
an inexact science. Models can only ever approximate the real
world, and we should never be lulled into expecting them to
operate perfectly or to predict the future. Sometimes they will
overstate financial insured loss; more worryingly, and as this
year’s experience has demonstrated, they can also understate
financial loss, in some cases by a distance.
It is in this context, armed with a healthy dose of scepticism
and purposefully setting out to ‘think the unthinkable’, that
today’s leading Mutuals have embraced the best of what cat
modelling technology can offer. There is no doubting the
insight that models can bring in allowing Mutuals to consider
what might happen and how best to protect and nurture their
core risk capital against such outcomes. In doing so they
reinforce once again the movement’s honourable tradition of
adapting to changing times and protecting members’ interests
over the longer term.
Willis Re Catastrophe Modelling for Mutuals | 7
The authors
Brian Owens, Executive Director
Brian is an Executive Director at Willis Re Analytics, based in
London, focusing primarily on catastrophe model interpretation.
Prior to this he managed the catastrophe modelling team for ACE
European Group and spent four years in model management at
Risk Management Solutions. Brian graduated with an MSc in
Atmospheric Science from the Rosenstiel School of Marine and
Atmospheric Science at the University of Miami. He also holds a
BSc in Computer Science from the National University of Ireland
and an MBA in Finance from the Wharton Graduate School
of Business.
Robin Swindell, Executive Vice President
Robin works in the London office of Willis Re North America
team. Since joining Willis in 1989, Robin has continuously been
involved in Japanese, Asian and Global treaty reinsurance.
His experience includes the placement of reinsurance programs
for Property Casualty and Specialty lines for Market Pools, P&C
companies, as well as Mutual Insurers. In addition, he supports
the placement of business from other Willis Re North America
offices into the London and European reinsurance markets.
Robin is also involved in Willis Re’s internal and external
training, covering a range of Casualty and Property subjects.
8 | Willis Re Catastrophe Modelling for Mutuals
Global and local reinsurance
Willis Re employs reinsurance experts worldwide. Drawing on this highly professional
resource, and backed by all the expertise of the wider Willis Group, we offer you every
solution you look for in a top tier reinsurance advisor. One that has comprehensive
capabilities, with on-the-ground presence and local understanding.
Whether your operations are global, national or local, Willis Re can help you make
better reinsurance decisions – access worldwide markets – negotiate optimum terms –
and boost your business performance.
How can we help?
To find out how we can offer you an extra depth of service
combined with extra flexibility, simply contact us.
Begin by visiting our website at www.willisre.com
or calling your local office.
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© Copyright 2011 Willis Re Inc. All rights reserved: The views expressed in this report are not necessarily those of Willis Re Inc., its parent
companies, sister companies, subsidiaries or affiliates (hereinafter ‘Willis’).This report and its contents are provided for informational
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