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

Abdul-Lateef Haji-Ali (Heriot-Watt U, Edinburgh)

May 05. 2020, 15:50 — 16:30

The computational complexity of a naïve Monte Carlo estimator of a simple risk measure, involving a nested expectation, of a financial portfolio of P options is O (max(P ϵ^-2, ϵ^-3})) for an error ϵ. In this talk, we show that combining MLMC with an adaptive sampling strategy reduces the complexity to O(P ϵ^-2, ϵ^-2 |logϵ|^2). Moreover, by sub-sampling the options in the portfolio the cost is reduced further to O(ϵ^-2 |logϵ|^2), independently of P.

Further Information
Erwin Schrödinger Institute - virtual
Associated Event:
Multilevel and multifidelity sampling methods in UQ for PDEs (Online Workshop)
Kody Law (U Manchester)
Fabio Nobile (EPFL, Lausanne)
Robert Scheichl (U Heidelberg)
Karen Willcox (U of Texas, Austin)