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Bayesian estimation of financial models

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  • Philip Gray

Abstract

This paper outlines a general methodology for estimating the parameters of financial models commonly employed in the literature. A numerical Bayesian technique is utilised to obtain the posterior density of model parameters and functions thereof. Unlike maximum likelihood estimation, where inference is only justified in large samples, the Bayesian densities are exact for any sample size. A series of simulation studies are conducted to compare the properties of point estimates, the distribution of option and bond prices, and the power of specification tests under maximum likelihood and Bayesian methods. Results suggest that maximum–likelihood–based asymptotic distributions have poor finite–sampleproperties.

Suggested Citation

  • Philip Gray, 2002. "Bayesian estimation of financial models," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 42(2), pages 111-130, June.
    • Handle: RePEc:bla:acctfi:v:42:y:2002:i:2:p:111-130
    DOI: 10.1111/1467-629X.00070
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    Cited by:

    1. Alcock, Jamie & Burrage, Kevin, 2004. "A genetic estimation algorithm for parameters of stochastic ordinary differential equations," Computational Statistics & Data Analysis, Elsevier, vol. 47(2), pages 255-275, September.
    2. L. Steinruecke & R. Zagst & A. Swishchuk, 2015. "The Markov-switching jump diffusion LIBOR market model," Quantitative Finance, Taylor & Francis Journals, vol. 15(3), pages 455-476, March.

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