Can We Trust Nat Cat Models?
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Transcript of Can We Trust Nat Cat Models?
Balz Grollimund, PhDSwiss Re Cat PerilsCAE Fall 2008 Meeting
Can We Trust Nat Cat Models?
Balz Grollimund, PhDSwiss Re Cat PerilsCAE Fall 2008 Meeting
Slide 2
Cat modeling in insurance industry- Swiss Re as an industry “proxy”
Cat modelling has become an industry standard.
– Cat risk assessment for a portfolio of insurance exposures a commodity.
– Cat modelling for individual insured objects more frequent.
Swiss Re: Each piece of property business is assessed by probabilistic Cat modelling.
Cat model output is fully linked into corporate risk model on an event by event basis – for key scenarios
Reliance on model output has become large.
Do these models provide reasonable output?
Balz Grollimund, PhDSwiss Re Cat PerilsCAE Fall 2008 Meeting
Slide 3
Can We Trust Nat Cat Models?
How do we estimate nat cat risks?
–Scenario loss
–Portfolio risk assessment
What do we use nat cat models for?
Sources of uncertainty in our estimates
Can we trust nat cat models?
–caution is warranted if…
–yes if…
Balz Grollimund, PhDSwiss Re Cat PerilsCAE Fall 2008 Meeting
Slide 4
What is the Impact of an Earthquake Event?
Estimated insurance loss for a repeat of the 1906 San Francisco earthquake:
– 10-20 bn USD
– 45-60 bn USD
– 60-120 bn USD
– 300-500 bn USD
Slide 5
Balz Grollimund, PhDSwiss Re Cat PerilsCAE Fall 2008 Meeting
Coverage Conditions
Sum insured Cover limits Deductibles Exclusions …
Hazard
ExampleHurricane “Charley”Aug 2004
Where?How strong?
Vulnerability
Damage? What is covered by insurance
where... and how?
Value Distribution
Key ingredients of Nat Cat Modeling
Slide 6
Balz Grollimund, PhDSwiss Re Cat PerilsCAE Fall 2008 Meeting
Detailed simulation of each event (animated)
Hazard intensity: peak gust [m/s] in color from yellow (weak) to red (strong)Places in greenLoss as blue circles
The simulation software evaluates 100’000 events on each cedent’s portfolio etc
ExampleHurricane “Charley”Aug 2004
Slide 7
Balz Grollimund, PhDSwiss Re Cat PerilsCAE Fall 2008 Meeting
Key ingredients of Nat Cat Modeling
Coverage Conditions
Sum insured Cover limits Deductibles Exclusions …
Hazard
ExampleHurricane “Charley”Aug 2004
How often?How strong?
Vulnerability
Damage? What is covered by insurance
where... and how?
Value Distribution
Slide 8
Balz Grollimund, PhDSwiss Re Cat PerilsCAE Fall 2008 Meeting
Earthquake Model ApproachVulnerability
Slide 9
Balz Grollimund, PhDSwiss Re Cat PerilsCAE Fall 2008 Meeting
Earthquake Model ApproachVulnerability
0%
10%
20%
30%
40%
50%
6 6.5 7 7.5 8 8.5 9 9.5 10 10.5 11
Mea
n da
mag
e ra
tio [%
TIV
]
Single family home, wood frame
Heavy Industry
VI VII VIII IX X
Modified Mercalli Intensity
0%
10%
20%
30%
40%
50%
6 6.5 7 7.5 8 8.5 9 9.5 10 10.5 11
Mea
n da
mag
e ra
tio [%
TIV
]
Single family home, wood frame
Heavy Industry
VI VII VIII IX X
Modified Mercalli Intensity
Avera
ge d
eg
ree o
f lo
ss[i
n %
of
sum
in
sure
d]
Tremor intensity [modified Mercalli intensity]
Damage estimate based on hazard intensity and the type of exposed object
Slide 10
Balz Grollimund, PhDSwiss Re Cat PerilsCAE Fall 2008 Meeting
Key ingredients of Nat Cat Modeling
Coverage Conditions
Sum insured Cover limits Deductibles Exclusions …
Hazard
ExampleHurricane “Charley”Aug 2004
How often?How strong?
Vulnerability
Damage? What is covered by insurance
where... and how?
Value Distribution
Slide 11
Balz Grollimund, PhDSwiss Re Cat PerilsCAE Fall 2008 Meeting
Storm Surge Modeling ApproachLocation of Insured Object Matters
Many clients deliver highly detailed exposure information, including location and value of each building.
Tracking of exposures by zonal aggregations still common in some markets.
Few markets do not yet track nat cat exposure.
Slide 12
Balz Grollimund, PhDSwiss Re Cat PerilsCAE Fall 2008 Meeting
Key ingredients of Nat Cat Modeling
Coverage Conditions
Sum insured Cover limits Deductibles Exclusions …
Hazard
ExampleHurricane “Charley”Aug 2004
How often?How strong?
Vulnerability
Damage? What is covered by insurance
where... and how?
Value Distribution
Balz Grollimund, PhDSwiss Re Cat PerilsCAE Fall 2008 Meeting
Slide 13
What is the Impact of an Earthquake Event?
Estimated insurance loss for a repeat of the 1906 San Francisco earthquake:
– 10-20 bn USD
– 45-60 bn USD
– 60-120 bn USD
– 300-500 bn USD
Balz Grollimund, PhDSwiss Re Cat PerilsCAE Fall 2008 Meeting
Slide 14
Let’s regroup – What do we know so far?
We can calculate the event loss for an individual scenario by considering
– Event characteristics (Where? How strong?)
– Vulnerability of insured objects
– Location and value of insured objects
– Insurance conditions governing the pay out
What else do nat cat models provide?
Slide 15
Balz Grollimund, PhDSwiss Re Cat PerilsCAE Fall 2008 Meeting
Nat Cat Risk Assessment Hurricane North Atlantic
historic (1‘000 events, representing 100 years)
probabilistic (1‘000‘000 events, representing 100‘000 years)
North Atlantic tropical cyclone event set as used operationally in MultiSNAP
Hurricane North Atlantic is one of Swiss Re’s Top 4 Nat Cat Scenarios
Slide 16
Balz Grollimund, PhDSwiss Re Cat PerilsCAE Fall 2008 Meeting
Nat Cat Risk Assessment
Balz Grollimund, PhDSwiss Re Cat PerilsCAE Fall 2008 Meeting
Slide 17
Let’s regroup – What do we know so far?
Based on probabilistic nat cat models, a portfolio of insured objects can be analyzed in terms of
– Annual expected loss
– Expected loss at specific recurrence interval
– Accumulation effects
How are nat cat models used at Swiss Re?
Slide 18
Balz Grollimund, PhDSwiss Re Cat PerilsCAE Fall 2008 Meeting
Use of event loss sets from nat cat models
1 2 3 4 n
Expected Loss
1 2 3 4 n
Event Loss Set
1 2 3 4 n
Pre/Post EventLoss Estimate
Loading
1 2 3 4 n
Pricing
1 2 3 4 n
Capacity
Risk Management
Slide 19
Balz Grollimund, PhDSwiss Re Cat PerilsCAE Fall 2008 Meeting
Event set based group portfolio aggregation
Client A
Client C
Client B
Swiss Re group
... event basedE2 E5 E6E1 E3 E4 E7 E8 E9
xs frequency
Slide 20
Balz Grollimund, PhDSwiss Re Cat PerilsCAE Fall 2008 Meeting
Capacity CalculationComparing Client Exposure to Swiss Re’s
Portfolio event lossesCapacity intensity
f
Client 1: High Capacity
Client 2: Low Capacity
Expected loss of both client portfolios identical
Client 1 strongly correlates with Swiss Re portfolio
Slide 21
Balz Grollimund, PhDSwiss Re Cat PerilsCAE Fall 2008 Meeting
Example: Winter storm EuropeRequired Capacity per Granted Cover
low
high
Balz Grollimund, PhDSwiss Re Cat PerilsCAE Fall 2008 Meeting
Slide 22
Integrated Nat Cat Model at Swiss Re
Calculation of expected loss and capital cost loading for each contract covering nat cat exposures.
=> Premium setting
Determine by how much a piece of business increases Swiss Re’s overall capacity requirement
=> Risk management
Event loss estimate in the aftermath of an event
=> Reserving, public- and investor relations
Reliance on model output has become large.
Do these models provide reasonable output?
Slide 23
Balz Grollimund, PhDSwiss Re Cat PerilsCAE Fall 2008 Meeting
Working with a recent, typical example: Taiwan EQ model
Drivers for review:
Frequency losses not realistic (2-10% probability)
Subsoil information not up to date
1st generation model – poor geographical resolution for individual accounts
Slide 24
Balz Grollimund, PhDSwiss Re Cat PerilsCAE Fall 2008 Meeting
Starting point (1):Historical catalogue evaluation
RAA 2008Earthquake modellingMartin Bertogg, Swiss Re
Excerpt from: GSHAP project catalogue
Slide 25
Balz Grollimund, PhDSwiss Re Cat PerilsCAE Fall 2008 Meeting
Gutenberg–Richter accepted as a general concept
Green – Historical Catalogue from 1960
Blue – Historical Catalogue from 1900
Red – Stochastic event set
Exce
ed
an
ce P
rob
ab
ility
Magnitude
Estimates of earthquake recurrence intervals are surprisingly reliable.
Slide 26
Balz Grollimund, PhDSwiss Re Cat PerilsCAE Fall 2008 Meeting
Step 2: AttenuationExample – ChiChi EQ 1999
3.8
4.3
4.8
5.3
5.8
6.3
6.8
0 50 100 150 200 250Hypocentral distance [km]
Loca
l inte
nsi
ty (Ta
iwan
)
Observed local intensityFinal attenuation (literature based)Attenuation variation #1
Difficulty to estimate earthquake impact at specific location.
Slide 27
Balz Grollimund, PhDSwiss Re Cat PerilsCAE Fall 2008 Meeting
Taiwan Earthquake Model:Attenuation impact on risk assessment
0
2'000
4'000
6'000
8'000
10'000
12'000
14'000
16'000
0 20 40 60 80 100 120
Mill
ions
Return period [years]
Loss
[TW
D]
Taiwan Client: Attenuation variation #1
Taiwan client: Final attenuation
Model uncertainties have large impact on model results.
Slide 28
Balz Grollimund, PhDSwiss Re Cat PerilsCAE Fall 2008 Meeting
Nat Cat Risk Assessment
Model Calibration is Key!
Slide 29
Balz Grollimund, PhDSwiss Re Cat PerilsCAE Fall 2008 Meeting
Spread of model opinions (1):EQ Turkey – Commercial portfolio
Occurance Exceeding Probability Functions -
% (Loss/TIV)
0.000%
1.200%
2.400%
3.600%
4.800%
6.000%
0 50 100 150 200 250Return Period (Years)
Loss
Fac
tor
(Los
s /
TSI)
in %
Commonly used nat cat models are well calibrated, where experience is available.
Slide 30
Balz Grollimund, PhDSwiss Re Cat PerilsCAE Fall 2008 Meeting
Spread of model opinions (2): EQ Israel – Commercial portfolio
Occurance Exceeding Probability Functions -
% (Loss/TIV)
0.000%
0.600%
1.200%
1.800%
2.400%
3.000%
0 100 200 300 400 500Return Period (Years)
Loss
Fac
tor
(Los
s /
TSI)
in %
Significant uncertainty remains in markets with little loss experience.
Slide 31
Balz Grollimund, PhDSwiss Re Cat PerilsCAE Fall 2008 Meeting
Risk factors beyond the currentmodel perimeter – what do we miss?
Secondaryeffects Policy
wording
Hazardousgoods
Dams Lossadjustment
cost
Economicalsituation
OK
Untestedrisk type
Unknown correlations
not monitoredrisk
Slide 32
Balz Grollimund, PhDSwiss Re Cat PerilsCAE Fall 2008 Meeting
Low cat markets with little awareness <> considerable insurance density
Newspaper report of the 1931 Dogger Bank earthquake ;
British Geological Survey, Robert Musson
Hong Kong
Singapore
Malta
Malaysia
Eastern Europe
…
Untestedrisk type
Policy wording not monitored
risk
Slide 33
Balz Grollimund, PhDSwiss Re Cat PerilsCAE Fall 2008 Meeting
RAA 2008Earthquake modellingMartin Bertogg, Swiss Re
San Francisco Tokyo
Untestedrisk type
Slide 34
Balz Grollimund, PhDSwiss Re Cat PerilsCAE Fall 2008 Meeting
Messina, Italy, 1783From: Historical Earthquakes in EuropeDr. Jan Kozak/Swiss Re 1991
Secondaryeffects
Unknown correlations
Policy wording
not monitoredrisk
Balz Grollimund, PhDSwiss Re Cat PerilsCAE Fall 2008 Meeting
Slide 35
To sum it all up…
“Essentially, all models are wrong…
…but some models are useful” (Statistician George E.P. Box)
(if well calibrated and used within their scope)
Balz Grollimund, PhDSwiss Re Cat PerilsCAE Fall 2008 Meeting
Slide 36
Can We Trust Nat Cat Models?
Caution warranted if
– model not calibrated
– exposure information is inappropriate (poor geographic resolution, poor/absent object description, sums insured inadequate)
– model inconsistent with policy wording (consequential perils, secondary effects, CBI, …)
Yes if used within their limits
– model calibrated
– exposure data has sufficient detail level and is of high quality
– unmodeled perils and other risk-impacting factors are properly considered in pricing process
Balz Grollimund, PhDSwiss Re Cat PerilsCAE Fall 2008 Meeting
Slide 37
Do you have any questions?