Working Paper : 1228

Authors Anyfantaki, S. and Demos, A.
Title Estimation and Properties of a Time-Varying EGARCH(1,1) in Mean Model
Abstract 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.
Creation Date 2012-07-30
Keywords Dynamic heteroskedasticity, in mean models, time varying parameter, Markov chain Monte Carlo, simulated EM algorithm, Bayesian inference
Classification JEL C13;C15;C63
File ar_egarch2_new2.pdf (328902 bytes)
File-Function First version

Copyright © 2009 [D.I.E.S.S. A.U.E.B.]. All rights reserved.