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Likelihood-based estimation of latent generalised ARCH structures

By Gabriele Fiorentini, Enrique Sentana and Neil Shephard

Abstract

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

Topics: HG Finance, HB Economic Theory
Publisher: Financial Markets Group, London School of Economics and Political Science
Year: 2003
OAI identifier: oai:eprints.lse.ac.uk:24852
Provided by: LSE Research Online

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