Estimation and Properties of a Time-Varying EGARCH(1,1) in Mean Model
Sofia Anyfantaki () and
Antonis Demos ()
No 1228, DEOS Working Papers from Athens University of Economics and Business
Time-varying GARCH-M models are commonly employed in econometrics and financial economics. Yet the recursive nature of the conditional variance makes exact likelihood analysis of these models computationally infeasible. This paper outlines the issues and suggests to employ a Markov chain Monte Carlo algorithm which allows the calculation of a classical estimator via the simulated EM algorithm or a simulated Bayesian solution in only O(T) computational operations, where T is the sample size. Furthermore, the theoretical dynamic properties of a time-varying-parameter EGARCH(1,1)-M are derived. We discuss them and apply the suggested Bayesian estimation to three major stock markets.
Keywords: Dynamic heteroskedasticity; in mean models; time varying parameter; Markov chain Monte Carlo; simulated EM algorithm; Bayesian inference (search for similar items in EconPapers)
JEL-codes: C13 C15 C63 (search for similar items in EconPapers)
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