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Adjustments to Cat Modeling

Adjustments to Cat Modeling. CAS Seminar on Cat Sean Devlin September 18, 2006. TCNA Adjustments - Climate. Options on Using Climate Forecasts Find no credibility in the forecasts Believe that the forecasts are directionally correct Believe completely in the multi-year forecasts

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Adjustments to Cat Modeling

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  1. Adjustments to Cat Modeling CAS Seminar on Cat Sean Devlin September 18, 2006

  2. TCNA Adjustments - Climate • Options on Using Climate Forecasts • Find no credibility in the forecasts • Believe that the forecasts are directionally correct • Believe completely in the multi-year forecasts • Believe completely in the single year forecasts

  3. TCNA Adjustments - Climate • Option 1 - Find no credibility in the forecasts • Use the a vendor model based on long term climate • Adjust the loss curve down of a vendor model that has increased frequency/severity • Use own model • A blend of the above

  4. TCNA Adjustments - Climate • Option 2- Believe that the forecasts are directionally correct • Credibility weighting between models in option 1 and a model with frequency adjustments • Adjust a long-term model for frequency/severity • Adjust long-term version of a vendor model • Adjust own model for frequency/severity • Combination of the above

  5. TCNA Adjustments - Climate • Option 3 - Believe completely in the multi-year forecasts • Implement a vendor model with a multi-year view • Make frequency/severity adjustments to a long term vendor model • Adjust own model • Blend of the above

  6. TCNA Adjustments - Climate • Option 4 - Believe completely in the single year forecasts • Implement seasonal forecast version for a vendor model • Adjust vendor model for frequency/severity • Adjust internal model for frequency/severity • Combination of the above

  7. TCNA Adjustments – Frequency/Severity • Adjust whole curve equally • Ignores shape change • Treats all regions equally • Adjust whole curve by return period/region

  8. Modeled Perils – Other Adjustments • Actual vs. Modeled – look for biases (Macro/Micro) • Other Biases in modeling • Exposure Changes / Missing Exposure/ITV Issues • LAE • Fair plans/pools/assessments • Demand Surge • Pre Event • Post Event

  9. Unmodeled Exposure • Tornado/Hail • Winter Storm • Wildfire • Flood • Terrorism • Fire Following • Other

  10. Unmodeled Perils Tornado Hail • National writers tend not to include TO exposures • Models are improving, but not quite there yet • Significant exposure • Frequency: TX • Severity: • 2003: 3.2B – 12th largest • 2001: 2.2B – 15th largest • 2002: 1.7B – 21st largest • Methodology • Experience and exposure Rate • Compare to peer companies with more data • Compare experience data to ISO wind history • Weight methods

  11. Unmodeled Perils Winter storm • Not insignificant peril in some areas, esp. low layers • 1994: 100M, 175M, 800M, 105M • 1993: 1.75B – 18th largest • 1996: 600M, 110M, 90M, 395M • 2003: 1.6B • # of occurrences in a cluster????? • Possible Understatement of PCS data • Methodology • Degree considered in models • Evaluate past event return period(s) • Adjust loss for today’s exposure • Fit curve to events

  12. Unmodeled Perils Wildfire • Not just CA • Oakland Fires: 1.7B – 19th largest • Development of land should increase freq/severity • Two main loss drivers • Brush clearance – mandated by code • Roof type (wood shake vs. tiled) • Methodology • Degree considered in models • Evaluate past event return period(s), if possible • Incorporate Risk management, esp. changes • No loss history - not necessarily no exposure

  13. Unmodeled Perils Flood • Less frequent • Development of land should increase frequency • Methodology • Degree considered in models • Evaluate past event return period(s),if possible • No loss history – not necessarily no exposure Terrorism • Modeled by vendor model? Scope? • Adjustments needed • Take-up rate – current/future • Future of TRIA – exposure in 2006 • Other – depends on data

  14. Unmodeled Perils Fire Following • No EQ coverage = No loss potential? NO!!!!! • Model reflective of FF exposure on EQ policies? • Severity adjustment of event needed, if • Some policies are EQ, some are FF only • Only EQ was modeled • Methodology • Degree considered in models • Compare to peer companies for FF only • Default Loadings for unmodeled FF • Multiplicative Loadings on EQ runs

  15. Unmodeled Perils Other Perils • Expected the unexpected • Examples: Blackout caused unexpected losses • Methodology • Blanket load • Exclusions, Named Perils in contract • Develop default loads/methodology for an complete list of perils

  16. Summary Don’t trust the Black Box • Understand the weakness/strengths of model • Know which perils/losses were modeled • Perform reasonability checks • Add in loads to include ALL perils • Reflect the prospective exposure

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