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Published byDelphia Gaines Modified over 9 years ago
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Stochastic Excess-of-Loss Pricing within a Financial Framework CAS 2005 Reinsurance Seminar Doris Schirmacher Ernesto Schirmacher Neeza Thandi
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Agenda Extreme Value Theory Central Limit Theorem Two Extreme Value Theorems Peaks Over Threshold Method Application to Reinsurance Pricing Example Collective Risk Models IRR Model
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Central Limit Theorem Consider a sequence of random variables X 1,…,X n from an unknown distribution with mean and finite variance 2. Let S n = X i be the sequence of partial sums. Then, with a n = n and b n = n (S n -b n )/ a n approaches a normal distribution
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Visualizing Central Limit Theorem
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Distribution of Normalized Maxima M n = max(X 1,X 2,…,X n ) does not converge to normal distributions:
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Fischer-Tippett Theorem Let X i ’s be a sequence of iid random variables. If there exists constants a n > 0 and b n and some non-degenerate distribution function H such that (M n – b n )/a n H, then H belongs to one of the three standard extreme value distributions: Frechet: (x) = 0 x 0 exp( -x - ) x>0, >0 Weibull: (x) = exp(-(-x) ) x 0 0 x>0, > 0 Gumbel: (x) = exp(-e -x ) x real
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Visualizing Fischer-Tippett Theorem
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Pickands, Balkema & de Haan Theorem For a large class of underlying distribution functions F, the conditional excess distribution function F u (y) = (F(y+u) – F(u))/(1-F(u)), for u large, is well approximated by the generalized Pareto distribution.
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Tail Distribution F(x) = Prob (X<= x) = (1-Prob(X<=u)) F u (x-u) + Prob (X<=u) (1-F(u)) GP (x-u) + F(u) for some Generalized Pareto distribution GP as u gets large. GP * (x-u*)
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Peaks Over Threshold Method Mean excess function of a Generalized Pareto: e(u) = /(1- ) u + /(1- )
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Agenda Extreme Value Theory Central Limit Theorem Two Extreme Value Theorems Peaks Over Threshold Method Application to Reinsurance Pricing Example Collective Risk Models IRR Model
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Example Coverage: a small auto liability portfolio Type of treaty: excess-of-loss Coverage year: 2005 Treaty terms: 12 million xs 3 million xs 3 million Data: Past large losses above 500,000 from 1995 to 2004 are provided.
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Collective Risk Models Look at the aggregate losses S from a portfolio of risks. S n = X 1 +X 2 +…+X n X i ’s are independent and identically distributed random variables n is the number of claims and is independent from X i ’s
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Loss Severity Distribution Pickands, Balkema & de Haan Theorem Excess losses above a high threshold follow a Generalized Pareto Distribution. - Develop the losses and adjust to an as-if basis. - Parameter estimation: method of moments, percentile matching, maximum likelihood, least squares, etc.
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Mean Excess Loss
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Fitting Generalized Pareto
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Claim Frequency Distribution Poisson Negative Binomial Binomial Method of Moment Maximum Likelihood Least Squares
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Combining Frequency and Severity Method of Moments Monte Carlo Simulation Recursive Formula Fast Fourier Transform
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Aggregate Loss Distribution
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Risk Measures Standard deviation or Variance Probability of ruin Value at Risk (VaR) Tail Value at Risk (TVaR) Expected Policyholder Deficit (EPD)
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Capital Requirements Rented Capital = Reduction in capital requirement due to the reinsurance treaty = Gross TVaR – Net TVaR Gross Net
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IRR Model Follows the paper “Financial Pricing Model for P/C Insurance Products: Modeling the Equity Flows” by Feldblum & Thandi Equity Flow = U/W Flow + Investment Income Flow + Tax Flow – Asset Flow + DTA Flow
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Determinants of Equity Flows Asset Flow DTA Flow U/W Flow Invest Inc Flow Tax Flow Equity Flow = Cash Flow from Operations - Incr in Net Working Capital Increase in Net Working Capital Cash Flow from Operations = U/W Flow + II Flow + Tax Flow - Asset Flow + DTA Flow
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Equity Flows U/W Cash Flow = WP – Paid Expense – Paid Loss Investment Income Flow = Inv. yield * Year End Income Producing Assets Tax Flow = - Tax on (UW Income Investment Income) Asset Flow = in Required Assets DTA Flow = in DTA over a year
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Overall Pricing Process Inputs Asset flows U/W flows Investment flows Tax flows DTA flows Target Return on Capital Parameters Equity Flows Pricing Model Target Premium
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