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Adjustments to Cat Modeling
CAS Seminar on RenisuranceSean DevlinMay 7-8, 2007
Slide 2
Model Selection
Slide 3
Model Selection
Major modeling firms AIR EQE RMS Other models, including proprietary
Options in using the models Use one model exclusively Use one model by “territory” Use multiple models for each account
Slide 4
Model Selection
Use One Model Exclusively Benefits
Simplify process for each deal Consistency of rating Lower cost of license Accumulation easier Running one model for each deal involves less time
Drawbacks Can’t see differences by deal and in general Conversion of data to your model format
Slide 5
Model Selection
Use One Model By “Territory” Detailed review of each model by “territory” Territory examples (EU wind, CA EQ, FL wind) Select adjustment factors for the chosen model Benefits
Simplify process for each deal Consistency of rating Accumulation easier Running one model involves less time
Drawbacks Can’t see differences by deal Conversion of data to your model format
Slide 6
Model Selection
Weights
Zone CT RMS EQECA EQ 70% 0% 30%Japan EQ 50% 0% 50%FL WS 0% 100% 0%Euro Wind 20% 40% 40%
Factors
Zone CT RMS EQECA EQ 80% 150% 130%Japan EQ 80% 120% 125%FL WS 90% 120% 50%Euro Wind 150% 80% 110%
Use One Model By “Territory” – An Example
Slide 7
Model Selection
Use Multiple ModelsBenefits
Can see differences by deal and in generalDrawbacks
Consistency of rating? Conversion of data to each model format Simplify process for each deal High cost of licenses Accumulation difficult Running one model for each deal is time consuming
Slide 8
Climate and
Hurricane Prediction
Slide 9
TCNA Adjustments - Climate
Intensity AEF NOAA TSR CSU Actual Average
Named Storms 13.2 12-15 13.9 15 27 9.9
All Hurricanes 7.5 7-9 7.8 8 15 6.0
Major Hurricanes
3.7 3-5 3.6 4 7 2.6
Intensity AEF NOAA TSR CSU Actual Average
Named Storms 14.5 13-16 15.4 17 10 9.9
All Hurricanes 8.5 8-10 8.2 9 5 6.0
Major Hurricanes
3.7 4-6 3.8 5 2 2.6
Despite impressive science, the individual season predictions, the last two years was off the mark.
2005
2006
Slide 10
TCNA Adjustments - Climate
Option 1 - Find no credibility in the forecasts
Use 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
Slide 11
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
Slide 12
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
Slide 13
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
Slide 14
Model Adjustments
Slide 15
TCNA Adjustments – Frequency/Severity
Adjust whole curve equally
Ignores shape change
Treats all regions equally
Adjust whole curve by return period/region
0%
50%
100%
150%
200%
250%
ST/LT 1
ST/LT 2
Slide 16
Modeled Perils – Other Adjustments
Actual vs. Modeled – look for biases (Macro/Micro)
Model recent events with actual portfolio
More confidence on gross results, but some insight may be gained on per risk basis
One or two events may show a material upward miss. Key is to understand why.
Exposure Changes / Missing Exposure/ITV Issues
TIV checks/audits
Scope of data – international, all states & perils
Changes in exposure, important for specialty writers
Slide 17
Modeled Perils – Other Adjustments
Other Biases in modeling
LAE
Fair plans/pools/assessments – know what is covered by client and treaty prospectively
FHCF – Reflect all probable outcomes of recovery
Storm Surge
Demand Surge
Pre Event
Post Event
Slide 18
“Unmodeled” Exposure
Tornado/Hail
Winter Storm
Wildfire
Flood
Terrorism
Fire Following
Other
Slide 19
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 2001: 2.2B 2002: 1.7B
Methodology Experience and exposure ate Compare to peer companies with more data Compare experience data to ISO wind history Weight methods
Slide 20
Unmodeled Perils
Winter storm Not insignificant peril in some areas, esp. low
layers 1994: 100M, 175M, 800M, 105M 1993: 1.75B 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 Aggregate Cover?????
Slide 21
Unmodeled Perils
Wildfire Not just CA Oakland Fires: 1.7B 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
Slide 22
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 2007/8 Other – depends on data
Slide 23
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 Reflect difference in policy T&Cs
Slide 24
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
Slide 25
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