Home Economics & Management Journal Issue Abstract


2012/1 (Vol. 33)

Email alert

Your alert request has been correctly taken into account.
You will receive an email when new issues are put online.


Previous Pages 79 - 112



In this paper we propose a new tool for backtesting that examines the quality of Value-at-Risk (VaR) forecasts. To date, the most distinguished regression-based backtest, proposed by Engle and Manganelli (2004), relies on a linear model. However, in view of the dichotomic character of the series of violations, a non-linear model seems more appropriate. In this paper we thus propose a new tool for backtesting (denoted DB) based on a dynamic binary regression model. Our discrete-choice model, e.g. Probit, Logit, links the sequence of violations to a set of explanatory variables including the lagged VaR and the lagged violations in particular. It allows us to separately test the unconditional coverage, the independence and the conditional coverage hypotheses and it is easy to implement. Monte-Carlo experiments show that the DB test exhibits good small sample properties in realistic sample settings (5% coverage rate with estimation risk). An application on a portfolio composed of three assets included in the CAC40 market index is finally proposed.


  1. Introduction
  2. Environment and testable hypotheses
  3. A Dynamic Binary Response model
    1. Model Specification
    2. Estimation Technique
    3. Backtesting test
  4. Small Sample Properties
    1. Empirical Size Analysis
    2. Power analysis
  5. Empirical application
  6. Conclusion

To cite this article

Elena-Ivona Dumitrescu, Christophe Hurlin, Vinson Pham, “ Backtesting Value-at-Risk: From Dynamic Quantile to Dynamic Binary Tests ”, Finance 1/2012 (Vol. 33) , p. 79-112
URL : www.cairn.info/revue-finance-2012-1-page-79.htm.

© 2010-2014 Cairn.info
back to top

Message sent

Your email address has been saved.
We will notify you when this article becomes available in English.