An almost closed form estimator for the EGARCH model

Christian Hafner and Oliver Linton

No 2013022, LIDAM Discussion Papers CORE from Université catholique de Louvain, Center for Operations Research and Econometrics (CORE)

Abstract: The EGARCH is a popular model for discrete time volatility since it allows for asymmetric effects and naturally ensures positivity even when including exogenous variables. Estimation and inference is usually done via maximum likelihood. Although some progress has been made recently, a complete distribution theory of MLE for EGARCH models is still missing. Furthermore, the estimation procedure itself may be highly sensitive to starting values, the choice of numerical optimation algorithm, etc. We present an alter- native estimator that is available in a simple closed form and which could be used, for example, as starting values for MLE. The estimator of the dynamic parameter is inde- pendent of the innovation distribution. For the other parameters we assume that the innovation distribution belongs to the class of Generalized Error Distributions (GED), profiling out its parameter in the estimation procedure. We discuss the properties of the proposed estimator and illustrate its performance in a simulation study.

Keywords: autocorrelations; generalized error distribution; method of moments estimator; Newton-Raphson (search for similar items in EconPapers)
JEL-codes: C12 C13 C14 (search for similar items in EconPapers)
Date: 2013-05-22
New Economics Papers: this item is included in nep-dcm, nep-ecm and nep-ets
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)

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Related works:
Journal Article: AN ALMOST CLOSED FORM ESTIMATOR FOR THE EGARCH MODEL (2017)
Working Paper: An Almost Closed Form Estimator For The EGARCH Model (2017)
Working Paper: An almost closed form estimator for the EGARCH model (2017)
Working Paper: An Almost Closed Form Estimator for the EGARCH model (2016)
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Persistent link: https://EconPapers.repec.org/RePEc:cor:louvco:2013022

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