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The use of Bayes factors to compare interest rate term structure models

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  • W. Keener Hughen
  • Carmelo Giaccotto
  • Po-Hsuan Hsu

Abstract

Studies of the term structure of interest rates try to explain the relationship between the yield to maturity on zero-coupon bonds and their time to maturity. Over the years, many theoretical models have been developed to explain the stylized facts of U.S. Treasury yields; however, model comparison, parameter estimation and hypothesis testing remain thorny issues. The purpose of this paper is to show that Bayesian methods and Markov Chain Monte Carlo (MCMC) methods, in particular, may help resolve a number of these problems, especially those related to model comparison. We use MCMC to compare the seminal models of Vasicek and Cox, Ingersoll and Ross (CIR). The most surprising result of our analysis is that one of these two models is almost 50 000 times more likely than the other. In contrast, results in the previous literature have been much more ambiguous because they are based on a variety of goodness-of-fit measures. A Monte Carlo study shows that these results are not spurious: the MCMC method is able to select the correct data generation model, whereas goodness-of-fit measures are virtually indistinguishable regardless of whether the data were generated from Vasicek or CIR.

Suggested Citation

  • W. Keener Hughen & Carmelo Giaccotto & Po-Hsuan Hsu, 2013. "The use of Bayes factors to compare interest rate term structure models," Quantitative Finance, Taylor & Francis Journals, vol. 13(3), pages 369-381, February.
    • Handle: RePEc:taf:quantf:v:13:y:2013:i:3:p:369-381
    DOI: 10.1080/14697688.2011.593541
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    References listed on IDEAS

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    1. Konstantijn Maes & Konstantijn Maes, 2003. "Modeling the Term Structure of Interest Rates: Where Do We Stand?," International Economics Working Papers Series wpie008, Katholieke Universiteit Leuven, Centrum voor Economische Studiën, International Economics.
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    Cited by:

    1. Dixon Domfeh & Arpita Chatterjee & Matthew Dixon, 2022. "A Unified Bayesian Framework for Pricing Catastrophe Bond Derivatives," Papers 2205.04520, arXiv.org.

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