Predicting risk with risk measures: an empirical study

Marcel Bräutigam, Michel Dacorogna and Marie Kratz ()
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Marcel Bräutigam: LabEx MME-DII - UCP - Université de Cergy Pontoise - Université Paris-Seine, ESSEC Business School, LPSM (UMR_8001) - Laboratoire de Probabilités, Statistique et Modélisation - UPD7 - Université Paris Diderot - Paris 7 - SU - Sorbonne Université - CNRS - Centre National de la Recherche Scientifique
Marie Kratz: ESSEC Business School, CREAR - Center of Research in Econo-finance and Actuarial sciences on Risk / Centre de Recherche Econo-financière et Actuarielle sur le Risque - ESSEC Business School

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Abstract: 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 e ect is related to the clustering of volatility. We argue that this has important consequences for the regulation in times of crisis.

Keywords: risk measure; sample quantile process; stochastic model; VaR; volatility (search for similar items in EconPapers)
Date: 2018-02-28
New Economics Papers: this item is included in nep-ban and nep-rmg
Note: View the original document on HAL open archive server: https://essec.hal.science/hal-01791026v1
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Citations: View citations in EconPapers (2)

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Working Paper: Predicting risk with risk measures: an empirical study (2018)
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