A DARE for VaR
Christophe Hurlin (),
Patrick Kouontchou and
Finance, 2015, vol. Vol.36, issue 1, 7-38
This paper introduces a new class of models for the Value-at-Risk (VaR) and Expected Shortfall (ES), called the Dynamic AutoRegressive Expectiles (DARE) models. Our approach is based on a weighted average of expectile-based VaR and ES models, i.e. the Conditional Autoregressive Expectile (CARE) models introduced by Taylor (2008a) and Kuan et al. (2009). First, we briefly present the main non-parametric, parametric and semi-parametric estimation methods for VaR and ES. Secondly, we detail the DARE approach and show how the expectiles can be used to estimate quantile risk measures. Thirdly, we use various backtesting tests to compare the DARE approach to other traditional methods for computing VaR forecasts on the French stock market. Finally, we evaluate the impact of several conditional weighting functions and determine the optimal weights in order to dynamically select the more relevant global quantile model.
References: Add references at CitEc
Citations Track citations by RSS feed
Downloads: (external link)
Working Paper: A DARE for VaR (2015)
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: http://EconPapers.repec.org/RePEc:cai:finpug:fina_361_0007
Access Statistics for this article
More articles in Finance from Presses universitaires de Grenoble
Series data maintained by Jean-Baptiste de Vathaire ().