Merits and drawbacks of variance targeting in GARCH models
Lajos Horvath and
Jean-Michel Zakoian ()
MPRA Paper from University Library of Munich, Germany
Variance targeting estimation is a technique used to alleviate the numerical difficulties encountered in the quasi-maximum likelihood (QML) estimation of GARCH models. It relies on a reparameterization of the model and a first-step estimation of the unconditional variance. The remaining parameters are estimated by QML in a second step. This paper establishes the asymptotic distribution of the estimators obtained by this method in univariate GARCH models. Comparisons with the standard QML are provided and the merits of the variance targeting method are discussed. In particular, it is shown that when the model is misspecified, the VTE can be superior to the QMLE for long-term prediction or Value-at-Risk calculation. An empirical application based on stock market indices is proposed.
Keywords: Consistency and Asymptotic Normality; GARCH; Heteroskedastic Time Series; Quasi Maximum Likelihood Estimation; Value-at-Risk; Variance Targeting Estimator. (search for similar items in EconPapers)
JEL-codes: C13 C22 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-ecm, nep-ets, nep-ore and nep-rmg
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Journal Article: Merits and Drawbacks of Variance Targeting in GARCH Models (2011)
Working Paper: Merits and Drawbacks of Variance Targeting in GARCH Models (2009)
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Persistent link: https://EconPapers.repec.org/RePEc:pra:mprapa:15143
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