| 杂志 | Insurance: Mathematics and Eco
65 (2015) 55–65 |
| 作者 | Angelos Dassios, Jiwook Jang, Hongbiao Zhao |
| 正文 | In this paper we generalise the risk models beyond the ordinary framework of affine processes or Markov
processes and study a risk process where the claim arrivals are driven by a Cox process with renewal
shot-noise intensity. The upper bounds of the finite-horizon and infinite-horizon ruin probabilities
are investigated and an efficient and exact Monte Carlo simulation algorithm for this new process
is developed. A more efficient estimation method for the infinite-horizon ruin probability based on
importance sampling via a suitable change of probability measure is also provided; illustrative numerical
examples are also provided. |
| JEL-Codes： | G22 C10 C60 |
| 关键词： | Risk model Ruin probability Renewal shot-noise Cox process Piecewise-deterministic Markov process Martingale method Importance sampling Change of probability measure Rare-event simulation |
