Finite-Sample Theory and Bias Correction of Maximum Likelihood Estimators in the EGARCH Model

Antonis Demos and Kyriakopoulou Dimitra ()
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Kyriakopoulou Dimitra: Center for Operations Research and Econometrics (CORE), Université catholique de Louvain, Louvain-la-Neuve, Belgium

Journal of Time Series Econometrics, 2019, vol. 11, issue 1, 20

Abstract: We derive the analytical expressions of bias approximations for maximum likelihood (ML) and quasi-maximum likelihood (QML) estimators of the EGARCH (1,1) parameters that enable us to correct after the bias of all estimators. The bias-correction mechanism is constructed under the specification of two methods that are analytically described. We also evaluate the residual bootstrapped estimator as a measure of performance. Monte Carlo simulations indicate that, for given sets of parameters values, the bias corrections work satisfactory for all parameters. The proposed full-step estimator performs better than the classical one and is also faster than the bootstrap. The results can be also used to formulate the approximate Edgeworth distribution of the estimators.

Keywords: exponential GARCH; maximum likelihood estimation; finite-sample properties; bias approximations; bias correction; Edgeworth expansion; bootstrap (search for similar items in EconPapers)
JEL-codes: C13 C22 (search for similar items in EconPapers)
Date: 2019
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Related works:
Working Paper: Finite Sample Theory and Bias Correction of Maximum Likelihood Estimators in the EGARCH Model (2018)
Working Paper: Finite sample theory and bias correction of maximum likelihood estimators in the EGARCH model (2018)
Working Paper: Finite-sample theory and bias correction of maximum likelihood estimators in the EGARCH model (2018)
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DOI: 10.1515/jtse-2018-0010

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