Subsampling and other considerations for efficient risk estimation in large portfolios

Michael B. Giles and Abdul-Lateef Haji-Ali

Journal of Computational Finance

Abstract: Computing risk measures of a financial portfolio comprising thousands of derivatives is a challenging problem both because it involves a nested expectation requiring multiple evaluations of the loss of the financial portfolio for different risk scenarios and because evaluating the loss of the portfolio is expensive and the cost increases with portfolio size. We apply multilevel Monte Carlo simulation with adaptive inner sampling to this problem and discuss several practical considerations. In particular, we discuss a subsampling strategy whose computational complexity does not increase with the size of the portfolio. We also discuss several control variates that significantly improve the efficiency of multilevel Monte Carlo in our setting.

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