An Exponential Chi-Squared QMLE for Log-GARCH Models Via the ARMA Representation
Christian Francq () and
MPRA Paper from University Library of Munich, Germany
Estimation of log-GARCH models via the ARMA representation is attractive because it enables a vast amount of already established results in the ARMA literature. We propose an exponential Chi-squared QMLE for log-GARCH models via the ARMA representation. The advantage of the estimator is that it corresponds to the theoretically and empirically important case where the conditional error of the log-GARCH model is normal. We prove the consistency and asymptotic normality of the estimator, and show that, asymptotically, it is as efficient as the standard QMLE in the log-GARCH(1,1) case. We also verify and study our results in finite samples by Monte Carlo simulations. An empirical application illustrates the versatility and usefulness of the estimator.
Keywords: Log-GARCH; EGARCH; Quasi Maximum Likelihood; Exponential Chi- Squared; ARMA (search for similar items in EconPapers)
JEL-codes: C13 C22 C58 (search for similar items in EconPapers)
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Journal Article: An Exponential Chi-Squared QMLE for Log-GARCH Models Via the ARMA Representation* (2018)
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Persistent link: https://EconPapers.repec.org/RePEc:pra:mprapa:51783
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