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On variance reduction of mean-CVaR Monte Carlo estimators

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  • Václav Kozmík

Abstract

We formulate an objective as a convex combination of expectation and risk, measured by the $$\mathrm{CVaR }$$ CVaR risk measure. The poor performance of standard Monte Carlo estimators applied on functions of this form is discussed and a variance reduction scheme based on importance sampling is proposed. We provide analytical solution for random variables based on normal distribution and outline the way for the other distributions, either by analytical computation or by sampling. Our results are applied in the framework of stochastic dual dynamic programming algorithm. Computational results which validate the previous analysis are given. Copyright Springer-Verlag Berlin Heidelberg 2015

Suggested Citation

  • Václav Kozmík, 2015. "On variance reduction of mean-CVaR Monte Carlo estimators," Computational Management Science, Springer, vol. 12(2), pages 221-242, April.
    • Handle: RePEc:spr:comgts:v:12:y:2015:i:2:p:221-242
    DOI: 10.1007/s10287-014-0225-7
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    References listed on IDEAS

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