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Efficient importance sampling for events of moderate deviations with applications

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  • Cheng-Der Fuh

Abstract

We propose a method for finding the alternative distribution in importance sampling. The alternative distribution is optimal in the sense that the asymptotic variance is minimised for estimating tail probabilities of asymptotically normal statistics. Our contribution to importance sampling is three-fold. To begin with, we obtain an explicit expression for the mean of the optimal alternative distribution and the expression motivates a recursive approximation algorithm. Secondly, a new multi-dimensional exponential tilting formula is presented. Lastly, a conservative estimator of the variance is given to facilitate a quick comparison among different stratified sampling schemes in conjunction with importance sampling. Several numerical examples illustrating the efficacy of the proposed method are also included. These results indicate that the proposed method is considerably more efficient than the method based on large deviations theory and the efficiency gain is more significant in higher dimensions. Copyright Biometrika Trust 2004, Oxford University Press.

Suggested Citation

  • Cheng-Der Fuh, 2004. "Efficient importance sampling for events of moderate deviations with applications," Biometrika, Biometrika Trust, vol. 91(2), pages 471-490, June.
  • Handle: RePEc:oup:biomet:v:91:y:2004:i:2:p:471-490
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    Cited by:

    1. Shih-Kuei Lin & Ren-Her Wang & Cheng-Der Fuh, 2006. "Risk Management for Linear and Non-Linear Assets: A Bootstrap Method with Importance Resampling to Evaluate Value-at-Risk," Asia-Pacific Financial Markets, Springer;Japanese Association of Financial Economics and Engineering, vol. 13(3), pages 261-295, September.
    2. repec:kap:compec:v:51:y:2018:i:4:d:10.1007_s10614-017-9654-z is not listed on IDEAS
    3. Cheng-Der Fuh & Chuan-Ju Wang, 2017. "Efficient Simulation for Portfolio Credit Risk in Normal Mixture Copula Models," Papers 1711.03744, arXiv.org, revised Jun 2018.

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