Looking for efficient qml estimation of conditional value-at-risk at multiple risk levels

Christian Francq and Jean-Michel Zakoian

MPRA Paper from University Library of Munich, Germany

Abstract: We consider joint estimation of conditional Value-at-Risk (VaR) at several levels, in the framework of general GARCH-type models. The conditional VaR at level $\alpha$ is expressed as the product of the volatility and the opposite of the $\alpha$-quantile of the innovation. A standard method is to estimate the volatility parameter by Gaussian Quasi-Maximum Likelihood (QML) in a first step, and to use the residuals for estimating the innovations quantiles in a second step. We argue that the Gaussian QML may be inefficient with respect to more general QML and can even be in failure for heavy tailed conditional distributions. We therefore study, for a vector of risk levels, a two-step procedure based on a generalized QML. For a portfolio of VaR's at different levels, confidence intervals accounting for both market and estimation risks are deduced. An empirical study based on stock indices illustrates the theoretical results.

Keywords: Asymmetric Power GARCH; Distortion Risk Measures; Estimation risk; Non-Gaussian Quasi-Maximum Likelihood; Value-at-Risk (search for similar items in EconPapers)
JEL-codes: C13 C22 C58 (search for similar items in EconPapers)
Date: 2015-10
New Economics Papers: this item is included in nep-ecm and nep-rmg
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Persistent link: https://EconPapers.repec.org/RePEc:pra:mprapa:67195

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