Likelihood-Based Estimation of Latent Generalised Arch Structures
CEMFI Working Paper No. 0204
Posted: 22 Nov 2002
Date Written: September 2002
GARCH models are commonly used as latent processes in econometrics, financial economics and macroeconomics. Yet no exact likelihood analysis of these models has been provided so far. In this paper we outline the issues and suggest a Markov chain Monte Carlo algorithm which allows the calculation of a classical estimator via the simulated EM algorithm or a Bayesian solution in O(T) computational operations, where T denotes the sample size. We assess the perfomance of our proposed algorithm in the context of both artificial examples and an empirical application to 26 UK sectorial stock returns, and compare it to existing approximate solutions.
Keywords: Bayesian inference, Dynamic heteroskedasticity, Factor models, Makov chain Monte Carlo, Simulated EM algorithm, Volatility
JEL Classification: C32, C51, G12
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