%0 Unpublished work %T Predicting risk with risk measures : an empirical study %+ LabEx MME-DII %+ ESSEC Business School %+ Laboratoire de Probabilités, Statistique et Modélisation (LPSM (UMR_8001)) %+ SCOR SE %+ DEAR Consulting %+ Information Systems, Decision Sciences and Statistics Department (SID) %A Bräutigam, Marcel %A Dacorogna, Michel %A Kratz, Marie %Z ESSEC Working paper. Document de Recherche ESSEC / Centre de recherche de l’ESSEC. ISSN : 1291-9616. WP 1803 %8 2018-02-28 %D 2018 %K risk measure %K sample quantile process %K stochastic model %K VaR %K volatility %Z Quantitative Finance [q-fin]/Risk Management [q-fin.RM]Preprints, Working Papers, ... %X In this study we consider the risk estimation as a stochastic process based on the Sample Quantile Process (SQP) - which is a generalization of the Value-at-Risk calculated on a rolling sample. Using SQP's, we are able to show and quantify the pro-cyclicality of the current way nancial institutions measure their risk. Analysing 11 stock indices, we show that, if the past volatility is low, the historical computation of the risk measure underestimates the future risk, while in periods of high volatility, the risk measure overestimates the risk.Moreover, using a simple GARCH(1,1) model, we conclude that this pro-cyclical eect is related to the clustering of volatility. We argue that this has important consequences for the regulation in times of crisis. %G English %2 https://essec.hal.science/hal-01791026/document %2 https://essec.hal.science/hal-01791026/file/WP1803.pdf %L hal-01791026 %U https://essec.hal.science/hal-01791026 %~ UNIV-PARIS7 %~ ESSEC %~ CNRS %~ UNIV-CERGY %~ AO-ECONOMIE %~ ESSEC-WP %~ USPC %~ LPSM %~ SORBONNE-UNIVERSITE %~ SORBONNE-UNIV %~ SU-SCIENCES %~ UNIV-PARIS %~ UP-SCIENCES %~ TEST-HALCNRS %~ SU-TI %~ ALLIANCE-SU