Transcript Download Handout 2

Fitting Pricing
Curves to
Client’s Large Loss Data
European
Methods
CARe Meeting
Bryan Ware
May 7, 2007
Fitting Curves to Client’s Large Loss Data
How is it done?
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Curve is fit to truncated large loss data from a non-proportional
reinsurance pricing submission
Often a single parameter Pareto
Don’t typically get all underlying (ground up) claim data
Difficult to adjust for exposure changes or combine data from multiple
submissions
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Fitting Curves to Client’s Large Loss Data
Challenges:
• Losses need to be at ultimate level and projected to prospective
period.
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Individual claim development
Property is easier
• Credibility of large loss data
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Fitting Curves to Client’s Large Loss Data
How is it used?
• Smoothing of experience rated layers
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Variation from curve fit is random
• Extrapolating to Higher Layers
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Credibility is at issue
Use for loss-free layers
• Often called Exposure Rating
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Not the same as US Exposure Rating
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Fitting Curves to Client’s Large Loss Data
What can we learn about US Exposure Rating from this?
• Industry data is very valuable
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It isn’t as good as we make it out to be
Build European / other foreign databases
• Gather ground up data with large losses or (ideally) all losses
• Preferably industry, but proprietary is better than none
• European reinsurers often have better access to client data than in
US.
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Fitting Curves to Client’s Large Loss Data
What can we learn about US Exposure Rating from this?
• It must be used wisely
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Method is almost never “right”
• Flawed / biased industry data
• Not the intended use for products
• Too generalized, doesn’t represent the specific client
• We often don’t know as much about client as we think we do – false
precision
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Fitting Curves to Client’s Large Loss Data
What else can we learn?
• Mechanical use of models is usually misleading (both European and
US)
• As a profession, are we better at building models or using them?
• Always understand terminology
• Issues are not location-dependent
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