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Consistent Estimation of the Value at Risk When the Error Distribution of the Volatility Model is Misspecified

Mohamed El Ghourabi, Christian Francq and Fedya Telmoudi
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Fedya Telmoudi: LARODEC - Laboratoire de Recherche Opérationnelle de Décision et de Contrôle de Processus - Université de Tunis - ISG de Tunis, EQUIPPE - Economie Quantitative, Intégration, Politiques Publiques et Econométrie - Université de Lille, Sciences et Technologies - Université de Lille, Sciences Humaines et Sociales - PRES Université Lille Nord de France - Université de Lille, Droit et Santé

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Abstract: A two‐step approach for conditional value at risk estimation is considered. First, a generalized quasi‐maximum likelihood estimator is employed to estimate the volatility parameter, then the empirical quantile of the residuals serves to estimate the theoretical quantile of the innovations. When the instrumental density h of the generalized quasi‐maximum likelihood estimator is not the Gaussian density, both the estimations of the volatility and of the quantile are generally asymptotically biased. However, the two errors counterbalance and lead to a consistent estimator of the value at risk. We obtain the asymptotic behavior of this estimator and show how to choose optimally h .

Date: 2015-05-08
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Published in Journal of Time Series Analysis, 2015, 37 (1), pp.46-76. ⟨10.1111/jtsa.12136⟩

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Related works:
Journal Article: Consistent Estimation of the Value at Risk When the Error Distribution of the Volatility Model is Misspecified (2016) Downloads
Working Paper: Consistent estimation of the Value-at-Risk when the error distribution of the volatility model is misspecified (2013) Downloads
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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-05417346

DOI: 10.1111/jtsa.12136

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