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Likelihood-based estimation of latent generalised ARCH structures

  • Gabriele Fiorentini


    (IPSC-JRC and University of Alicante, Italy)

  • Enrique Sentana


    (CEMFI, Spain)

  • Neil Shephard


    (Nuffield College, Oxford, UK)

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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Paper provided by Economics Group, Nuffield College, University of Oxford in its series Economics Papers with number 2002-W19.

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Length: 42 pages
Date of creation: 12 Sep 2002
Date of revision:
Handle: RePEc:nuf:econwp:0219
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