Convergence analysis of quasi-Monte Carlo sampling for quantile and expected shortfall

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Abstract

Quantiles and expected shortfalls are usually used to measure risks of stochastic systems, which are often estimated by Monte Carlo methods. This paper focuses on the use of the quasi-Monte Carlo (QMC) method, whose convergence rate is asymptotically better than Monte Carlo in the numerical integration. We first prove the convergence of QMC-based quantile estimates under very mild conditions, and then establish a deterministic error bound of $ O(N^{-1/d})$ for the quantile estimates, where $ d$ is the dimension of the QMC point sets used in the simulation and $ N$ is the sample size. Under certain conditions, we show that the mean squared error (MSE) of the randomized QMC estimate for expected shortfall is $ o(N^{-1})$. Moreover, under stronger conditions the MSE can be improved to $ O(N^{-1-1/(2d-1)+\epsilon })$ for arbitrarily small $ \epsilon >0$.

Publication
Mathematics of Computation
Zhijian He
Zhijian He
Associate Professor

My research interests include Monte Carlo and quasi-Monte methods with applications in finance and statistics.

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