1. Balz Grollimund, PhD Swiss Re Cat Perils CAE Fall 2008 Meeting Can We Trust Nat Cat Models?

2. Balz Grollimund, PhD Swiss Re Cat Perils CAE 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?

3. Balz Grollimund, PhD Swiss Re Cat Perils CAE 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…

4. Balz Grollimund, PhD Swiss Re Cat Perils CAE 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

5. Balz Grollimund, PhD Swiss Re Cat Perils CAE Fall 2008 Meeting Slide 5 Coverage Conditions Sum insured Cover limits Deductibles Exclusions … Hazard Example Hurricane “Charley” Aug 2004 Where? How strong? Vulnerability Damage? What is covered by insurance where... and how? Value Distribution Key ingredients of Nat Cat Modeling

6. Balz Grollimund, PhD Swiss Re Cat Perils CAE Fall 2008 Meeting Slide 6 Detailed simulation of each event (animated) Hazard intensity: peak gust [m/s] in color from yellow (weak) to red (strong) Places in green Loss as blue circles The simulation software evaluates 100’000 events on each cedent’s portfolio etc Example Hurricane “Charley” Aug 2004

7. Balz Grollimund, PhD Swiss Re Cat Perils CAE Fall 2008 Meeting Slide 7 Key ingredients of Nat Cat Modeling Coverage Conditions Sum insured Cover limits Deductibles Exclusions … Hazard Example Hurricane “Charley” Aug 2004 How often? How strong? Vulnerability Damage? What is covered by insurance where... and how? Value Distribution

8. Balz Grollimund, PhD Swiss Re Cat Perils CAE Fall 2008 Meeting Slide 8 Earthquake Model Approach Vulnerability

9. Balz Grollimund, PhD Swiss Re Cat Perils CAE Fall 2008 Meeting Slide 9 Earthquake Model Approach Vulnerability Average degree of loss [in % of sum insured] Tremor intensity [modified Mercalli intensity] Damage estimate based on hazard intensity and the type of exposed object

10. Balz Grollimund, PhD Swiss Re Cat Perils CAE Fall 2008 Meeting Slide 10 Key ingredients of Nat Cat Modeling Coverage Conditions Sum insured Cover limits Deductibles Exclusions … Hazard Example Hurricane “Charley” Aug 2004 How often? How strong? Vulnerability Damage? What is covered by insurance where... and how? Value Distribution

11. Balz Grollimund, PhD Swiss Re Cat Perils CAE Fall 2008 Meeting Slide 11 Storm Surge Modeling Approach Location 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.

12. Balz Grollimund, PhD Swiss Re Cat Perils CAE Fall 2008 Meeting Slide 12 Key ingredients of Nat Cat Modeling Coverage Conditions Sum insured Cover limits Deductibles Exclusions … Hazard Example Hurricane “Charley” Aug 2004 How often? How strong? Vulnerability Damage? What is covered by insurance where... and how? Value Distribution

13. Balz Grollimund, PhD Swiss Re Cat Perils CAE 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

14. Balz Grollimund, PhD Swiss Re Cat Perils CAE 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?

15. Balz Grollimund, PhD Swiss Re Cat Perils CAE Fall 2008 Meeting Slide 15 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

16. Balz Grollimund, PhD Swiss Re Cat Perils CAE Fall 2008 Meeting Slide 16 Nat Cat Risk Assessment

17. Balz Grollimund, PhD Swiss Re Cat Perils CAE 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?

18. Balz Grollimund, PhD Swiss Re Cat Perils CAE Fall 2008 Meeting Slide 18 Use of event loss sets from nat cat models Expected Loss Event Loss Set Pre/Post Event Loss Estimate Loading Pricing Capacity Risk Management

19. Balz Grollimund, PhD Swiss Re Cat Perils CAE Fall 2008 Meeting Slide 19 Event set based group portfolio aggregation Client A Client C Client B Swiss Re group... event based E2E5E6 E1 E3E4E7E8E9 xs frequency

20. Balz Grollimund, PhD Swiss Re Cat Perils CAE Fall 2008 Meeting Slide 20 Capacity Calculation Comparing Client Exposure to Swiss Re’s Portfolio event losses Capacity intensity f Client 1: High CapacityClient 2: Low Capacity Expected loss of both client portfolios identical Client 1 strongly correlates with Swiss Re portfolio

21. Balz Grollimund, PhD Swiss Re Cat Perils CAE Fall 2008 Meeting Slide 21 Example: Winter storm Europe Required Capacity per Granted Cover low high

22. Balz Grollimund, PhD Swiss Re Cat Perils CAE 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?

23. Balz Grollimund, PhD Swiss Re Cat Perils CAE Fall 2008 Meeting Slide 23 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

24. Balz Grollimund, PhD Swiss Re Cat Perils CAE Fall 2008 Meeting Slide 24 Starting point (1): Historical catalogue evaluation RAA 2008 Earthquake modelling Martin Bertogg, Swiss Re Excerpt from: GSHAP project catalogue

25. Balz Grollimund, PhD Swiss Re Cat Perils CAE Fall 2008 Meeting Slide 25 Gutenberg–Richter accepted as a general concept Green – Historical Catalogue from 1960 Blue – Historical Catalogue from 1900 Red – Stochastic event set Exceedance Probability Magnitude Estimates of earthquake recurrence intervals are surprisingly reliable.

26. Balz Grollimund, PhD Swiss Re Cat Perils CAE Fall 2008 Meeting Slide 26 Step 2: Attenuation Example – ChiChi EQ 1999 Difficulty to estimate earthquake impact at specific location.

27. Balz Grollimund, PhD Swiss Re Cat Perils CAE Fall 2008 Meeting Slide 27 Taiwan Earthquake Model: Attenuation impact on risk assessment Model uncertainties have large impact on model results.

28. Balz Grollimund, PhD Swiss Re Cat Perils CAE Fall 2008 Meeting Slide 28 Nat Cat Risk Assessment Model Calibration is Key!

29. Balz Grollimund, PhD Swiss Re Cat Perils CAE Fall 2008 Meeting Slide 29 Spread of model opinions (1): EQ Turkey – Commercial portfolio Commonly used nat cat models are well calibrated, where experience is available.

30. Balz Grollimund, PhD Swiss Re Cat Perils CAE Fall 2008 Meeting Slide 30 Spread of model opinions (2): EQ Israel – Commercial portfolio Significant uncertainty remains in markets with little loss experience.

31. Balz Grollimund, PhD Swiss Re Cat Perils CAE Fall 2008 Meeting Slide 31 Risk factors beyond the current model perimeter – what do we miss? Secondary effects Policy wording Hazardous goods Dams Loss adjustment cost Economical situation OK Untested risk type Unknown correlations not monitored risk

32. Balz Grollimund, PhD Swiss Re Cat Perils CAE Fall 2008 Meeting Slide 32 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 … Untested risk type Policy wording not monitored risk

33. Balz Grollimund, PhD Swiss Re Cat Perils CAE Fall 2008 Meeting Slide 33 RAA 2008 Earthquake modelling Martin Bertogg, Swiss Re San FranciscoTokyo Untested risk type

34. Balz Grollimund, PhD Swiss Re Cat Perils CAE Fall 2008 Meeting Slide 34 Messina, Italy, 1783 From: Historical Earthquakes in Europe Dr. Jan Kozak/Swiss Re 1991 Secondary effects Unknown correlations Policy wording not monitored risk

35. Balz Grollimund, PhD Swiss Re Cat Perils CAE 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)

36. Balz Grollimund, PhD Swiss Re Cat Perils CAE 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

37. Balz Grollimund, PhD Swiss Re Cat Perils CAE Fall 2008 Meeting Slide 37 Do you have any questions?