Bayesian estimation of financial models

Research output: Contribution to journalArticleResearchpeer-review

3 Citations (Scopus)

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 finitesample properties.

Original languageEnglish
Pages (from-to)111-130
Number of pages20
JournalAccounting and Finance
Volume42
Issue number2
DOIs
Publication statusPublished - 1 Jan 2002

Keywords

  • Bayesian estimation
  • Black-Scholes, maximum likelihood estimation
  • Short-rate model

Cite this

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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 finitesample properties.",
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Bayesian estimation of financial models. / Gray, Philip.

In: Accounting and Finance, Vol. 42, No. 2, 01.01.2002, p. 111-130.

Research output: Contribution to journalArticleResearchpeer-review

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AB - 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 finitesample properties.

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