EconPapers    
Economics at your fingertips  
 

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

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.

Date: 2011
References: Add references at CitEc
Citations: View citations in EconPapers (84)

Downloads: (external link)
http://hdl.handle.net/10.1198/jbes.2010.07318 (text/html)
Access to full text is restricted to subscribers.

Related works:
Journal Article: Evaluating Value-at-Risk Models via Quantile Regression (2011) Downloads
Working Paper: Evaluating Value-at-Risk Models via Quantile Regression (2010) Downloads
Working Paper: Evaluating Value-at-Risk models via Quantile Regression (2009) Downloads
Working Paper: Evaluating Value-at-Risk Models via Quantile Regressions (2008) Downloads
Working Paper: Evaluating Value-at-Risk models via Quantile regressions (2008) Downloads
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:taf:jnlbes:v:29:y:2011:i:1:p:150-160

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/UBES20

DOI: 10.1198/jbes.2010.07318

Access Statistics for this article

Journal of Business & Economic Statistics is currently edited by Eric Sampson, Rong Chen and Shakeeb Khan

More articles in Journal of Business & Economic Statistics from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().

 
Page updated 2025-03-22
Handle: RePEc:taf:jnlbes:v:29:y:2011:i:1:p:150-160