Abstract

http://ssrn.com/abstract=480461
 
 

References (133)



 
 

Citations (58)



 


 



MCMC Methods for Continuous-Time Financial Econometrics


Michael S. Johannes


Columbia Business School - Finance and Economics

Nick Polson


University of Chicago - Booth School of Business

December 22, 2003



Abstract:     
This chapter develops Markov Chain Monte Carlo (MCMC) methods for Bayesian inference in continuous-time asset pricing models. The Bayesian solution to the inference problem is the distribution of parameters and latent variables conditional on observed data, and MCMC methods provide a tool for exploring these high-dimensional, complex distributions. We first provide a description of the foundations and mechanics of MCMC algorithms. This includes a discussion of the Clifford-Hammersley theorem, the Gibbs sampler, the Metropolis-Hastings algorithm, and theoretical convergence properties of MCMC algorithms. We next provide a tutorial on building MCMC algorithms for a range of continuous-time asset pricing models. We include detailed examples for equity price models, option pricing models, term structure models, and regime-switching models. Finally, we discuss the issue of sequential Bayesian inference, both for parameters and state variables.

Number of Pages in PDF File: 96

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Date posted: December 27, 2003  

Suggested Citation

Johannes, Michael S. and Polson, Nick, MCMC Methods for Continuous-Time Financial Econometrics (December 22, 2003). Available at SSRN: http://ssrn.com/abstract=480461 or http://dx.doi.org/10.2139/ssrn.480461

Contact Information

Michael Slater Johannes (Contact Author)
Columbia Business School - Finance and Economics ( email )
3022 Broadway
New York, NY 10027
United States

Nick Polson
University of Chicago - Booth School of Business ( email )
5807 S. Woodlawn Avenue
Chicago, IL 60637
United States
773-702-7513 (Phone)
773-702-0458 (Fax)
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References:  133
Citations:  58

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