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Catastrophe Assessment: Actuarial SOPs and Model Validation. CAS Seminar on Catastrophe Issues New Orleans – October 22, 1998 Session 12 Panel: Douglas J. Collins Karen F. Terry Patrick B. Woods. Model Validation and Uncertainty. Session 12 Panel: Presentation by Douglas J. Collins.

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catastrophe assessment actuarial sops and model validation

Catastrophe Assessment:Actuarial SOPs and Model Validation

CAS Seminar on Catastrophe Issues

New Orleans – October 22, 1998

Session 12 Panel:

Douglas J. Collins

Karen F. Terry

Patrick B. Woods

model validation and uncertainty

Model Validation and Uncertainty

Session 12 Panel:

Presentation by Douglas J. Collins

outline of presentation catastrophe model validation and uncertainty
How catastrophe models work

Hurricane model validation

types of validation

validation data

Model uncertainty

Outline of presentationCatastrophe model validation and uncertainty
how catastrophe models work general logic
How catastrophe models workGeneral logic

Science

  • 1. Model Physical Event
  • Select a peril
  • Assess likelihood at location
  • Assess intensity, given location

Engineering

  • 2. Predict Damage
  • Values (building, contents, loss of use)
  • Vulnerability functions
    • building type
    • construction

Insurance

  • 3. Model Insured Claims
  • Limits relative to values
  • Deductibles
  • Ancillary exposures
  • Reinsurance
how catastrophe models work hurricane modeling
How catastrophe models workHurricane modeling
  • 1. Model Storm Path and Intensity
  • Landfall probabilities
  • Minimum central pressure
  • Path properties
  • Windfield
  • Land friction effects

Meteorology

Engineering

  • 2. Predict Damage
  • Values (building, contents, loss of use)
  • Vulnerability functions
    • building type
    • construction

Insurance

  • 3. Model Insured Claims
  • Limits relative to values
  • Deductibles
  • Ancillary exposures
  • Reinsurance
hurricane model validation types of validation
Hurricane model validationTypes of validation
  • Component validation
    • probabilistic parameters (e.g., landfall probability)
    • wind speeds
    • vulnerability functions
  • Micro validation
    • compare modeled versus actual company losses
      • individual claim detail
      • various levels of aggregation
  • Macro validation
    • compare modeled versus actual industry losses by event
    • compare probabilistic and historical size-of-loss distributions and loss costs
hurricane model validation component validation
Hurricane model validationComponent validation
  • Probabilistic parameters
    • landfall probability
    • minimum central pressure
    • radius
  • Wind validation – comparisons with
    • anemometer readings
    • National Hurricane Center reports
    • 100-year winds
  • Vulnerability function validation
    • compare damage ratios by zip/coverage and wind speed with insurer claim data
    • input from engineers
  • Validation of model changes
    • component changes logical
    • software testing procedures
hurricane model validation micro validation modeled versus actual losses
Hurricane model validation Micro validation – modeled versus actual losses
  • Aggregated company data
    • by lob, coverage
    • by county, zip
    • by construction type, quality
  • Individual claim detail
      • distributions of damage ratios
      • deductible effects
      • local land friction and land use effects
hurricane model validation macro validation
Hurricane model validationMacro validation
  • Compare modeled versus actual industry losses by event
    • requires estimate of industry exposures
    • requires historical loss dataset
    • tests for overall bias, consistency
  • Compare probabilistic and historical loss distributions and loss costs
    • size-of-loss distributions by state
      • actual and modeled historical versus probabilistic
      • return periods from 10 years to 100 years
    • loss costs by state and county
hurricane model validation constructing a macro validation dataset
Hurricane model validationConstructing a macro validation dataset
  • Data sources
    • NWS total economic impact
    • PCS insured losses
    • Insurers and reinsurers
    • special studies (AIRAC, Andrew)
  • Methodology
    • select best estimate of industry loss
    • allocate to state and county
    • trending
      • inflation (implicit price deflator)
      • current inventory of properties and values (real net stock of FRTW, housing units)
      • current insurance system (PCS versus NWS)
model uncertainty why do different hurricane models produce different results
Model uncertaintyWhy do different hurricane models produce different results?
  • There is considerable uncertainty in estimating probabilities of rare events
    • meteorological records (100-150 years)
    • paleo proxy studies (500-5,000 years)
  • Hurricanes are complex systems
    • the effect of landfall is not fully understood
    • each storm has unique characteristics
      • microbursts, tornados, rainfall
      • demand surge
  • There is considerable uncertainty in estimating damage at a given location
  • Uncertainty depends on use of model
    • PMLs versus loss costs
model uncertainty how credible are the models
Model uncertaintyHow credible are the models?
  • Average hurricane loss costs by county vary significantly between modelers in some counties in Florida
    • this should not be surprising
  • Models are continually being improved due to:
    • growth in modeler resources
    • growth in information
    • opening the black boxes
    • greater computer power
  • There is no better alternative
    • robust handling of nearly all possible scenarios
    • historical insurance experience alone is insufficient
    • use of multiple models is growing