Bayesian Inference for Discretely Sampled Markov Processes with Closed-Form Likelihood Expansions.
The writers suggest a new Bayesian Markov chain Monte Carlo (MCMC) methodology to estimate a wide class of multidimensional jump-diffusion models. They base their approach on the closed-form (CF) likelihood approximations of Ait-Sahalia. They report that the CF likelihood approximation does not in...
| Publicado en: | Journal of Financial Econometrics Vol. 8; no. 4; pp. 450 - 481 |
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| Autores principales: | , , |
| Formato: | Artículo |
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Oxford University Press / UK
Fall 2010
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=511535286&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 511535286 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 14798409 T2Y jtl: Journal of Financial Econometrics issn: 14798409 maglogo: N pubinfo: dt: Fall 2010 vid: 8 iid: 4 pid: 622 pub: Oxford University Press / UK artinfo: ui: 511535286 10.1093/jjfinec/nbp027 ppf: 450 ppct: 31 formats: tig: atl: Bayesian Inference for Discretely Sampled Markov Processes with Closed-Form Likelihood Expansions. aug: au: Stramer, Osnat Bognar, Matthew Schneider, Paul su: Bayesian analysis Markov processes Monte Carlo method Diffusion processes Maximum likelihood statistics sug: subj: Bayesian analysis Markov processes Monte Carlo method Diffusion processes Maximum likelihood statistics ab: The writers suggest a new Bayesian Markov chain Monte Carlo (MCMC) methodology to estimate a wide class of multidimensional jump-diffusion models. They base their approach on the closed-form (CF) likelihood approximations of Ait-Sahalia. They report that the CF likelihood approximation does not integrate to 1, being very close to 1 when in the center of the distribution but potentially differing markedly from 1 when far in the tails. Proposing an MCMC algorithm that addresses the problems that arise when the CF approximation is applied in a Bayesian context, they demonstrate the efficacy of their approach in a simulation study of the Cox-Ingersoll-Ross and Heston models and apply it to two well-known datasets. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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