Bayesian inference for discretely sampled Markov processes with closed-form likelihood expansions
Stramer, O., Bognar, M. and Schneider, Paul. (2010) Bayesian inference for discretely sampled Markov processes with closed-form likelihood expansions. Journal of Financial Econometrics, Vol.8 (No.4). pp. 450-480. ISSN 1479-8409Full text not available from this repository.
Official URL: http://dx.doi.org/10.1093/jjfinec/nbp027
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 Ait-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.
|Item Type:||Journal Article|
|Subjects:||H Social Sciences > HG Finance|
|Divisions:||Faculty of Social Sciences > Warwick Business School > Finance Group
Faculty of Social Sciences > Warwick Business School
|Journal or Publication Title:||Journal of Financial Econometrics|
|Publisher:||Oxford University Press|
|Number of Pages:||31|
|Page Range:||pp. 450-480|
|Access rights to Published version:||Restricted or Subscription Access|
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