A DARE for VaR

Benjamin Hamidi, Christophe Hurlin, Patrick Kouontchou () and Bertrand Maillet ()
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Benjamin Hamidi: Neuflize OBC Investissements - Neuflize OBC Investissements
Patrick Kouontchou: UL - Université de Lorraine, CEREFIGE - Centre Européen de Recherche en Economie Financière et Gestion des Entreprises - UL - Université de Lorraine
Bertrand Maillet: A.A.Advisors-QCG - ABN AMRO, LEDA-SDFi - Stratégie et Dynamiques Financières - LEDa - Laboratoire d'Economie de Dauphine - IRD - Institut de Recherche pour le Développement - Université Paris Dauphine-PSL - PSL - Université Paris Sciences et Lettres - CNRS - Centre National de la Recherche Scientifique

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Abstract: 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.

Date: 2015-01-01
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Published in Finance, 2015, 36 (2015/1), pp.7-38. ⟨10.3917/fina.361.0007⟩

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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-02312327

DOI: 10.3917/fina.361.0007

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