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Bounded Relative Error Importance Sampling and Rare Event Simulation

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  • McLeish, Don L.

Abstract

We consider estimating tail events using exponential families of importance sampling distributions. When the cannonical sufficient statistic for the exponential family mimics the tail behaviour of the underlying cumulative distribution function, we can achieve bounded relative error for estimating tail probabilities. Examples of rare event simulation from various distributions including Tukey's g&h distribution are provided.

Suggested Citation

  • McLeish, Don L., 2010. "Bounded Relative Error Importance Sampling and Rare Event Simulation," ASTIN Bulletin, Cambridge University Press, vol. 40(1), pages 377-398, May.
  • Handle: RePEc:cup:astinb:v:40:y:2010:i:01:p:377-398_00
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    Cited by:

    1. Targino, Rodrigo S. & Peters, Gareth W. & Shevchenko, Pavel V., 2015. "Sequential Monte Carlo Samplers for capital allocation under copula-dependent risk models," Insurance: Mathematics and Economics, Elsevier, vol. 61(C), pages 206-226.
    2. Mélina Mailhot & Mhamed Mesfioui, 2016. "Multivariate TVaR-Based Risk Decomposition for Vector-Valued Portfolios," Risks, MDPI, vol. 4(4), pages 1-16, September.

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