Computation of Expected Shortfall by fast detection of worst scenarios

Bouchard, Bruno; Reghai, Adil; Virrion, Benjamin

Bouchard, Bruno; Reghai, Adil; Virrion, Benjamin (2021), Computation of Expected Shortfall by fast detection of worst scenarios, Quantitative Finance, 21, 7, p. 1087-1108. 10.1080/14697688.2021.1880618

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Type

Article accepté pour publication ou publié

Date

2021

Journal name

Quantitative Finance

Volume

Number

Publisher

Taylor & Francis

Published in

Paris

Pages

1087-1108

Abstract (EN)

We consider multi-step algorithms for the computation of the historical expected shortfall. At each step of the algorithms, we use Monte Carlo simulations to reduce the number of historical scenarios that potentially belong to the set of worst-case scenarios. We show how this can be optimized by either solving a simple deterministic dynamic programming algorithm or in an adaptive way by using a stochastic dynamic programming procedure under a given prior. We prove Lp-error bounds and numerical tests are performed.

Subjects / Keywords

ranking and selection; sequential design; Expected Shortfall; Bayesian filter

JEL

G00 - General