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Evaluating Value-at-Risk Models via Quantile Regression

Evaluating Value-at-Risk Models via Quantile Regression 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. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Business & Economic Statistics Taylor & Francis

Evaluating Value-at-Risk Models via Quantile Regression

11 pages

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References (92)

Publisher
Taylor & Francis
Copyright
© 2011 American Statistical Association
ISSN
1537-2707
eISSN
0735-0015
DOI
10.1198/jbes.2010.07318
Publisher site
See Article on Publisher Site

Abstract

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.

Journal

Journal of Business & Economic StatisticsTaylor & Francis

Published: Jan 1, 2011

Keywords: Backtesting; Risk exposure

There are no references for this article.