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Computation of Expected Shortfall by fast detection of worst scenarios

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
21
Number
7
Publisher
Taylor & Francis
Published in
Paris
Pages
1087-1108
Publication identifier
10.1080/14697688.2021.1880618
Metadata
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Author(s)
Bouchard, Bruno
Reghai, Adil
Virrion, Benjamin
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

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