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Bayesian Inference for Discretely Sampled Markov Processes with Closed-Form Likelihood Expansions

  • Osnat Stramer
  • Matthew Bognar
  • Paul Schneider

This article proposes a new Bayesian Markov chain Monte Carlo (MCMC) methodology for estimation of a wide class of multidimensional jump-diffusion models. Our approach is based on the closed-form (CF) likelihood approximations of Aït-Sahalia�(2002,�2008). The CF likelihood approximation does not integrate to 1; it is very close to 1 when in the center of the distribution but can differ markedly from 1 when far in the tails. We propose an MCMC algorithm that addresses the problems that arise when the CF approximation is applied in a Bayesian context. The efficacy of our approach is demonstrated in a simulation study of the Cox--Ingersoll--Ross and Heston models and is applied to two well-known datasets. Copyright The Author 2010. Published by Oxford University Press. All rights reserved. For permissions, please e-mail:, Oxford University Press.

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Article provided by Society for Financial Econometrics in its journal Journal of Financial Econometrics.

Volume (Year): 8 (2010)
Issue (Month): 4 (Fall)
Pages: 450-480

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Handle: RePEc:oup:jfinec:v:8:y:2010:i:4:p:450-480
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