Sub-sampling and other considerations for efficient risk estimation in large portfolios

arXiv1912.05484MaRDI QIDQ6330930

Abdul-Lateef Haji-Ali, Michael B. Giles

Publication date: 11 December 2019

Abstract: Computing risk measures of a financial portfolio comprising thousands of derivatives is a challenging problem because (a) it involves a nested expectation requiring multiple evaluations of the loss of the financial portfolio for different risk scenarios and (b) evaluating the loss of the portfolio is expensive and the cost increases with its size. In this work, we look at applying Multilevel Monte Carlo (MLMC) with adaptive inner sampling to this problem and discuss several practical considerations. In particular, we discuss a sub-sampling 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 MLMC in our setting.

Has companion code repository: https://github.com/haji-ali/nested-risk-mlmc

Mathematics Subject Classification ID

Monte Carlo methods (65C05) Numerical solutions to stochastic differential and integral equations (65C30)

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