Rate of convergence of the probability of ruin in the Cramér–Lundberg model to its diffusion approximation

Asaf Cohen, Virginia R. Young

Research output: Contribution to journalArticlepeer-review

Abstract

We analyze the probability of ruin for the scaled classical Cramér–Lundberg (CL) risk process and the corresponding diffusion approximation. The scaling, introduced by Iglehart (1969) to the actuarial literature, amounts to multiplying the Poisson rate λ by n, dividing the claim severity by n, and adjusting the premium rate so that net premium income remains constant. We are the first to use a comparison method to prove convergence of the probability of ruin for the scaled CL process and to derive the rate of convergence. Specifically, we prove a comparison lemma for the corresponding integro-differential equation and use this comparison lemma to prove that the probability of ruin for the scaled CL process converges to the probability of ruin for the limiting diffusion process. Moreover, we show that the rate of convergence for the ruin probability is of order O(n−1∕2), and we show that the convergence is uniform with respect to the surplus. To the best of our knowledge, this is the first rate of convergence achieved for these ruin probabilities, and we show that it is the tightest one in the general case. For the case of exponentially-distributed claims, we are able to improve the approximation arising from the diffusion, attaining a uniform O(n−k∕2) rate of convergence for arbitrary k∈N. We also include two examples that illustrate our results.

Original languageAmerican English
Pages (from-to)333-340
Number of pages8
JournalInsurance: Mathematics and Economics
Volume93
DOIs
StatePublished - 1 Jul 2020
Externally publishedYes

Keywords

  • Approximation error
  • Cramér–Lundberg risk process
  • Diffusion approximation
  • Investment analysis
  • Probability of ruin

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Economics and Econometrics
  • Statistics, Probability and Uncertainty

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