Evaluating Value-at-Risk Models via Quantile Regression
Wagner Gaglianone (),
Luiz Lima (),
Oliver Linton () and
Daniel R. Smith
Journal of Business & Economic Statistics, 2011, vol. 29, issue 1, 150-160
This article 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 Manganelli (2004) are based on such variables. In this article 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 and Xiao 2002). Our theoretical findings are corroborated through a Monte Carlo simulation and an empirical exercise with daily S&P500 time series.
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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 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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