Likelihood-based estimation of latent generalised ARCH
Enrique Sentana () and
Gabriele Fiorentini ()
No 2004-FE-02, Economics Series Working Papers from University of Oxford, Department of Economics
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 performance 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. GARCH models are commonly used as latent processes in econometrics, financial economics and macroeconomics.
Keywords: Bayesian inference; Dynamic Heteroskedasticity; Factor models; Markov chain (search for similar items in EconPapers)
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