Likelihood-based estimation of latent generalised ARCH structures.

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...

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Main Authors: Fiorentini, G, Sentana, E, Shephard, N
Format: Journal article
Language:English
Published: Wiley 2004
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author Fiorentini, G
Sentana, E
Shephard, N
author_facet Fiorentini, G
Sentana, E
Shephard, N
author_sort Fiorentini, G
collection OXFORD
description 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.
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spelling oxford-uuid:7d98c66c-8994-438f-a16b-5b31fc46b0702022-03-26T21:04:42ZLikelihood-based estimation of latent generalised ARCH structures.Journal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:7d98c66c-8994-438f-a16b-5b31fc46b070EnglishDepartment of Economics - ePrintsWiley2004Fiorentini, GSentana, EShephard, NGARCH 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.
spellingShingle Fiorentini, G
Sentana, E
Shephard, N
Likelihood-based estimation of latent generalised ARCH structures.
title Likelihood-based estimation of latent generalised ARCH structures.
title_full Likelihood-based estimation of latent generalised ARCH structures.
title_fullStr Likelihood-based estimation of latent generalised ARCH structures.
title_full_unstemmed Likelihood-based estimation of latent generalised ARCH structures.
title_short Likelihood-based estimation of latent generalised ARCH structures.
title_sort likelihood based estimation of latent generalised arch structures
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AT sentanae likelihoodbasedestimationoflatentgeneralisedarchstructures
AT shephardn likelihoodbasedestimationoflatentgeneralisedarchstructures