Multilevel Monte Carlo for Estimating Risk Measures

Joint work with Abdul-Lateef Haji-Ali

This talk will discuss efficient numerical methods for estimating the probability of a large portfolio loss, and associated risk measures such as VaR and CVaR. These involve nested expectations, and following Bujok, Hambly & Reisinger (2015) we use the number of samples for the inner conditional expectation as the key approximation parameter in the Multilevel Monte Carlo formulation. The main difference in this case is the indicator function in the definition of the probability. Here we build on previous work by Gordy & Juneja (2010) who analyse the use of a fixed number of inner samples, and Broadie, Du & Moallemi (2011) who develop and analyse an adaptive algorithm. I will present the algorithm, outline the main theoretical results and give the numerical results for a representative model problem. I will also discuss the extension to real portfolios with a large number of options based on multiple underlying assets.