
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
View/ Open
Type
Article accepté pour publication ou publiéDate
2021Journal name
Quantitative FinanceVolume
21Number
7Publisher
Taylor & Francis
Published in
Paris
Pages
1087-1108
Publication identifier
Metadata
Show full item recordAbstract (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 filterJEL
G00 - GeneralRelated items
Showing items related by title and author.
-
Bouchard, Bruno; Moreau, Ludovic; Soner, Halil Mete (2016) Article accepté pour publication ou publié
-
Bonforte, Matteo; Dolbeault, Jean; Muratori, Matteo; Nazaret, Bruno (2017) Article accepté pour publication ou publié
-
Bouchard, Bruno; Gobet, Emmanuel; Jourdain, Benjamin (2017) Article accepté pour publication ou publié
-
Della Croce, Federico; Escoffier, Bruno; Kaminski, Marcin; Paschos, Vangelis (2008) Chapitre d'ouvrage
-
Hansen, Pierre; Jaumard, Brigitte; Minoux, Michel (1986) Article accepté pour publication ou publié