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Likelihood-based estimation of latent generalised ARCH structures

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Author Info

  • Gabriele Fiorentini
  • Enrique Sentana
  • 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.

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File URL: http://eprints.lse.ac.uk/24852/
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Bibliographic Info

Paper provided by London School of Economics and Political Science, LSE Library in its series LSE Research Online Documents on Economics with number 24852.

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Length: 42 pages
Date of creation: Jun 2003
Date of revision:
Handle: RePEc:ehl:lserod:24852

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Keywords: Bayesian inference; Dynamic heteroskedasticity; Factor models; Markov chain Monte Carlo; Simulated EM algorithm; Volatility;

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