1 / 16

Adjustments to Cat Modeling

Explore various adjustment options in climate forecasting and frequency/severity modeling for perils like tornado, hail, wildfire, flood, terrorism, and more. Learn methods for managing unmodeled perils and enhancing risk evaluation processes.

franklynr
Download Presentation

Adjustments to Cat Modeling

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  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

More Related