Logo
Munich Personal RePEc Archive

An Exponential Chi-Squared QMLE for Log-GARCH Models Via the ARMA Representation

Francq, Christian and Sucarrat, Genaro (2013): An Exponential Chi-Squared QMLE for Log-GARCH Models Via the ARMA Representation.

[img]
Preview
PDF
MPRA_paper_51783.pdf

Download (671kB) | Preview

Abstract

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.

Atom RSS 1.0 RSS 2.0

Contact us: mpra@ub.uni-muenchen.de

This repository has been built using EPrints software.

MPRA is a RePEc service hosted by Logo of the University Library LMU Munich.