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- W2118485004 abstract "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." @default.
- W2118485004 created "2016-06-24" @default.
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- W2118485004 date "2010-04-14" @default.
- W2118485004 modified "2023-09-26" @default.
- W2118485004 title "Bayesian Inference for Discretely Sampled Markov Processes with Closed-Form Likelihood Expansions" @default.
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- W2118485004 doi "https://doi.org/10.1093/jjfinec/nbp027" @default.
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