Evaluating Value-at-Risk Models via Quantile Regression

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

No 67, NCER Working Paper Series from National Centre for Econometric Research

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 Christofferson (1998) and Engle and Mangenelli (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 condtion to assess the finite sample performance of a quantile model whereas the existing ones do not. The proposed methodolgy allows us to identify periods of an increased risk exposure based on a quantile regression model (Koenker and 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)
Pages: 27 pages
Date: 2010-11-05
New Economics Papers: this item is included in nep-ban and nep-rmg
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Citations: View citations in EconPapers (10)

Forthcoming

Downloads: (external link)
http://www.ncer.edu.au/papers/documents/WPNo67.pdf (application/pdf)

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 (2009)
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:qut:auncer:2010_14

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