Can We Trust Nat Cat Models?

1. Can We Trust Nat Cat Models?

2. Reliance on model output has become large. Do these models provide reasonable output? 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

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. 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. Hazard Vulnerability Value Distribution Coverage Conditions Where?How strong? Damage? What is covered by insurance where... and how? Example Hurricane “Charley” Aug 2004 • Sum insured • Cover limits • Deductibles • Exclusions • … Key ingredients of Nat Cat Modeling

6. Example Hurricane “Charley” Aug 2004 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

7. Hazard Vulnerability Value Distribution Coverage Conditions How often?How strong? Damage? What is covered by insurance where... and how? Example Hurricane “Charley” Aug 2004 • Sum insured • Cover limits • Deductibles • Exclusions • … Key ingredients of Nat Cat Modeling

8. Earthquake Model ApproachVulnerability

9. Damage estimate based on hazard intensity and the type of exposed object Earthquake Model ApproachVulnerability Average degree of loss[in % of sum insured] Tremor intensity [modified Mercalli intensity]

10. Hazard Vulnerability Value Distribution Coverage Conditions How often?How strong? Damage? What is covered by insurance where... and how? Example Hurricane “Charley” Aug 2004 • Sum insured • Cover limits • Deductibles • Exclusions • … Key ingredients of Nat Cat Modeling

11. 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.

12. Hazard Vulnerability Value Distribution Coverage Conditions How often?How strong? Damage? What is covered by insurance where... and how? Example Hurricane “Charley” Aug 2004 • Sum insured • Cover limits • Deductibles • Exclusions • … Key ingredients of Nat Cat Modeling

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. 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. historic (1‘000 events, representing 100 years) probabilistic (1‘000‘000 events, representing 100‘000 years) Nat Cat Risk Assessment Hurricane North Atlantic 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. Nat Cat Risk Assessment

17. How are nat cat models used at Swiss Re? 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

18. Loading Expected Loss Pricing Risk Management Capacity Pre/Post EventLoss Estimate Use of event loss sets from nat cat models Event Loss Set

19. ... event based E1 E2 E3 E4 E5 E6 E7 E8 E9 xs frequency Event set based group portfolio aggregation Client A Client B Client C Swiss Re group

20. Client 1: High Capacity Client 2: Low Capacity Capacity CalculationComparing Client Exposure to Swiss Re’s Portfolio event losses • Expected loss of both client portfolios identical • Client 1 strongly correlates with Swiss Re portfolio Capacity intensity f

21. low high Example: Winter storm EuropeRequired Capacity per Granted Cover

22. Reliance on model output has become large. Do these models provide reasonable output? 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

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. Starting point (1):Historical catalogue evaluation Excerpt from: GSHAP project catalogue RAA 2008 Earthquake modelling Martin Bertogg, Swiss Re

25. Exceedance Probability Green – Historical Catalogue from 1960 Blue – Historical Catalogue from 1900 Red – Stochastic event set Estimates of earthquake recurrence intervals are surprisingly reliable. Magnitude Gutenberg–Richter accepted as a general concept

26. Difficulty to estimate earthquake impact at specific location. Step 2: AttenuationExample – ChiChi EQ 1999

27. Model uncertainties have large impact on model results. Taiwan Earthquake Model:Attenuation impact on risk assessment

28. Nat Cat Risk Assessment Model Calibration is Key!

29. Commonly used nat cat models are well calibrated, where experience is available. Spread of model opinions (1):EQ Turkey – Commercial portfolio

30. Significant uncertainty remains in markets with little loss experience. Spread of model opinions (2): EQ Israel – Commercial portfolio

31. Risk factors beyond the currentmodel perimeter – what do we miss? not monitoredrisk Economicalsituation Secondaryeffects Policy wording Dams Lossadjustmentcost OK Unknown correlations Hazardousgoods Untestedrisk type

32. Low cat markets with little awareness <> considerable insurance density Hong Kong Singapore Malta Malaysia Eastern Europe … Policy wording Untestedrisk type not monitoredrisk Newspaper report of the 1931 Dogger Bank earthquake ; British Geological Survey, Robert Musson

33. San Francisco Tokyo Untestedrisk type RAA 2008 Earthquake modelling Martin Bertogg, Swiss Re

34. not monitoredrisk Unknown correlations Policy wording Secondaryeffects From: Historical Earthquakes in EuropeDr. Jan Kozak/Swiss Re 1991 Messina, Italy, 1783

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. 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. Do you have any questions?