Predicting risk with risk measures: an empirical study

Bräutigam Marcel, Michel Dacorogna and Kratz Marie ()
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Bräutigam Marcel: Sorbonne University
Kratz Marie: ESSEC Research Center, ESSEC Business School, Postal: ESSEC Research Center, BP 105, 95021 Cergy, France

No WP1803, ESSEC Working Papers from ESSEC Research Center, ESSEC Business School

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 eect 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)
JEL-codes: C13 C22 C52 C53 G01 G32 (search for similar items in EconPapers)
Pages: 45 pages
Date: 2018-02
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Working Paper: Predicting risk with risk measures: an empirical study (2018)
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