Computation of market risk measures with stochastic liquidity horizon

The Basel Committee of Banking Supervision has recently set out the revised standards for minimum capital requirements for market risk. The Committee has focused, among other things, on the two key areas of moving from Value-at-Risk (VaR) to Expected Shortfall (ES) and considering a comprehensive in...

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Detalles Bibliográficos
Autores: Colldeforns Papiol, Gemma, Ortiz Gracia, Luis
Tipo de recurso: artículo
Estado:Versión aceptada para publicación
Fecha de publicación:2018
País:España
Institución:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
Repositorio:Recercat. Dipósit de la Recerca de Catalunya
OAI Identifier:oai:recercat.cat:2445/127589
Acceso en línea:https://hdl.handle.net/2445/127589
Access Level:acceso abierto
Palabra clave:Risc (Economia)
Mercat financer
Liquiditat (Economia)
Valor (Economia)
Risk
Financial market
Liquidity (Economics)
Value (Economics)
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repository_id_str
spelling Computation of market risk measures with stochastic liquidity horizonColldeforns Papiol, GemmaOrtiz Gracia, LuisRisc (Economia)Mercat financerLiquiditat (Economia)Valor (Economia)RiskFinancial marketLiquidity (Economics)Value (Economics)The Basel Committee of Banking Supervision has recently set out the revised standards for minimum capital requirements for market risk. The Committee has focused, among other things, on the two key areas of moving from Value-at-Risk (VaR) to Expected Shortfall (ES) and considering a comprehensive incorporation of the risk of market illiquidity by extending the risk measurement horizon. The estimation of the ES for several trading desks and taking into account different liquidity horizons is computationally very involved. We present a novel numerical method to compute the VaR and ES of a given portfolio within the stochastic holding period framework. Two approaches are considered, the delta-gamma approximation, for modelling the change in value of the portfolio as a quadratic approximation of the change in value of the risk factors, and some of the state-of-the-art stochastic processes for driving the dynamics of the log-value change of the portfolio like the Merton jump-diffusion model and the Kou model. Central to this procedure is the application of the SWIFT method developed for option pricing, that appears to be a very efficient and robust Fourier inversion method for risk management purposes.Elsevier B.V.2019202020182019info:eu-repo/semantics/articleinfo:eu-repo/semantics/acceptedVersion20 p.application/pdfhttps://hdl.handle.net/2445/127589Articles publicats en revistes (Econometria, Estadística i Economia Aplicada)reponame:Recercat. Dipósit de la Recerca de Catalunyainstname:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)InglésVersió postprint del document publicat a: https://doi.org/10.1016/j.cam.2018.03.038Journal of Computational and Applied Mathematics, 2018, vol. 342, num. November, p. 431-450https://doi.org/10.1016/j.cam.2018.03.038cc-by-nc-nd (c) Elsevier B.V., 2018http://creativecommons.org/licenses/by-nc-nd/3.0/esinfo:eu-repo/semantics/openAccessoai:recercat.cat:2445/1275892026-05-29T05:05:01Z
dc.title.none.fl_str_mv Computation of market risk measures with stochastic liquidity horizon
title Computation of market risk measures with stochastic liquidity horizon
spellingShingle Computation of market risk measures with stochastic liquidity horizon
Colldeforns Papiol, Gemma
Risc (Economia)
Mercat financer
Liquiditat (Economia)
Valor (Economia)
Risk
Financial market
Liquidity (Economics)
Value (Economics)
title_short Computation of market risk measures with stochastic liquidity horizon
title_full Computation of market risk measures with stochastic liquidity horizon
title_fullStr Computation of market risk measures with stochastic liquidity horizon
title_full_unstemmed Computation of market risk measures with stochastic liquidity horizon
title_sort Computation of market risk measures with stochastic liquidity horizon
dc.creator.none.fl_str_mv Colldeforns Papiol, Gemma
Ortiz Gracia, Luis
author Colldeforns Papiol, Gemma
author_facet Colldeforns Papiol, Gemma
Ortiz Gracia, Luis
author_role author
author2 Ortiz Gracia, Luis
author2_role author
dc.subject.none.fl_str_mv Risc (Economia)
Mercat financer
Liquiditat (Economia)
Valor (Economia)
Risk
Financial market
Liquidity (Economics)
Value (Economics)
topic Risc (Economia)
Mercat financer
Liquiditat (Economia)
Valor (Economia)
Risk
Financial market
Liquidity (Economics)
Value (Economics)
description The Basel Committee of Banking Supervision has recently set out the revised standards for minimum capital requirements for market risk. The Committee has focused, among other things, on the two key areas of moving from Value-at-Risk (VaR) to Expected Shortfall (ES) and considering a comprehensive incorporation of the risk of market illiquidity by extending the risk measurement horizon. The estimation of the ES for several trading desks and taking into account different liquidity horizons is computationally very involved. We present a novel numerical method to compute the VaR and ES of a given portfolio within the stochastic holding period framework. Two approaches are considered, the delta-gamma approximation, for modelling the change in value of the portfolio as a quadratic approximation of the change in value of the risk factors, and some of the state-of-the-art stochastic processes for driving the dynamics of the log-value change of the portfolio like the Merton jump-diffusion model and the Kou model. Central to this procedure is the application of the SWIFT method developed for option pricing, that appears to be a very efficient and robust Fourier inversion method for risk management purposes.
publishDate 2018
dc.date.none.fl_str_mv 2018
2019
2019
2020
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/acceptedVersion
format article
status_str acceptedVersion
dc.identifier.none.fl_str_mv https://hdl.handle.net/2445/127589
url https://hdl.handle.net/2445/127589
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Versió postprint del document publicat a: https://doi.org/10.1016/j.cam.2018.03.038
Journal of Computational and Applied Mathematics, 2018, vol. 342, num. November, p. 431-450
https://doi.org/10.1016/j.cam.2018.03.038
dc.rights.none.fl_str_mv cc-by-nc-nd (c) Elsevier B.V., 2018
http://creativecommons.org/licenses/by-nc-nd/3.0/es
info:eu-repo/semantics/openAccess
rights_invalid_str_mv cc-by-nc-nd (c) Elsevier B.V., 2018
http://creativecommons.org/licenses/by-nc-nd/3.0/es
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 20 p.
application/pdf
dc.publisher.none.fl_str_mv Elsevier B.V.
publisher.none.fl_str_mv Elsevier B.V.
dc.source.none.fl_str_mv Articles publicats en revistes (Econometria, Estadística i Economia Aplicada)
reponame:Recercat. Dipósit de la Recerca de Catalunya
instname:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
instname_str Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
reponame_str Recercat. Dipósit de la Recerca de Catalunya
collection Recercat. Dipósit de la Recerca de Catalunya
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,812429

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