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Finite-Sample Theory and Bias Correction of Maximum Likelihood Estimators in the EGARCH Model
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Finite-Sample Theory and Bias Correction of Maximum Likelihood Estimators in the EGARCH Model
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Journal Article
Finite-Sample Theory and Bias Correction of Maximum Likelihood Estimators in the EGARCH Model
2019
Overview
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.
Publisher
De Gruyter,Walter de Gruyter GmbH
