balz grollimund, phd swiss re cat perils cae fall 2008 meeting 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?