Journal article
Bayesian inference for discretely sampled Markov processes with closed-form likelihood expansions
Journal of Financial Econometrics, Vol.8(4), pp.450-480
2009
DOI: 10.1093/jjfinec/nbp027
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.
Details
- Title: Subtitle
- Bayesian inference for discretely sampled Markov processes with closed-form likelihood expansions
- Creators
- Osnat StramerMatthew BognarPaul Schneider
- Resource Type
- Journal article
- Publication Details
- Journal of Financial Econometrics, Vol.8(4), pp.450-480
- DOI
- 10.1093/jjfinec/nbp027
- ISSN
- 1479-8409
- eISSN
- 1479-8417
- Language
- English
- Date published
- 2009
- Academic Unit
- Statistics and Actuarial Science
- Record Identifier
- 9983985941902771
Metrics
34 Record Views