Evaluating Value-at-Risk models via Quantile Regression

Wagner Gaglianone, Luiz Lima (), Oliver Linton and Daniel Smith

UC3M Working papers. Economics from Universidad Carlos III de Madrid. Departamento de Economía

Abstract: This paper is concerned with evaluating value at risk estimates. It is well known that using only binary variables, such as whether or not there was an exception, sacrifices too much information. However, most of the specification tests (also called backtests) available in the literature, such as Christoffersen (1998) and Engle and Maganelli (2004) are based on such variables. In this paper we propose a new backtest that does not rely solely on binary variables. It is shown that the new backtest provides a sufficient condition to assess the finite sample performance of a quantile model whereas the existing ones do not. The proposed methodology allows us to identify periods of an increased risk exposure based on a quantile regression model (Koenker & Xiao, 2002). Our theoretical findings are corroborated through a Monte Carlo simulation and an empirical exercise with daily S&P500 time series.

Keywords: Value-at-Risk; Backtesting; Quantile; Regression (search for similar items in EconPapers)
JEL-codes: C12 C14 C52 G11 (search for similar items in EconPapers)
Date: 2009-05
New Economics Papers: this item is included in nep-ban, nep-ecm and nep-rmg
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Related works:
Journal Article: Evaluating Value-at-Risk Models via Quantile Regression (2011)
Journal Article: Evaluating Value-at-Risk Models via Quantile Regression (2011)
Working Paper: Evaluating Value-at-Risk Models via Quantile Regression (2010)
Working Paper: Evaluating Value-at-Risk Models via Quantile Regressions (2008)
Working Paper: Evaluating Value-at-Risk models via Quantile regressions (2008)
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Persistent link: https://EconPapers.repec.org/RePEc:cte:werepe:we094625

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