Evaluating value-at-risk models via quantile regression

Wagner Piazza Gaglianone, Luiz Renato Lima, Oliver Linton, Daniel R. Smith

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71 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)150-160
Number of pages11
JournalJournal of Business and Economic Statistics
Volume29
Issue number1
DOIs
Publication statusPublished - Jan 2011
Externally publishedYes

Keywords

  • Backtesting
  • Risk exposure

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