Bayesian Analysis of Continuous Time Models of the Australian Short Rate

Andrew D. Sanford () and Gael Martin

No 11/04, Monash Econometrics and Business Statistics Working Papers from Monash University, Department of Econometrics and Business Statistics

Abstract: This paper provides an empirical analysis of a range of alternative single-factor continuous time models for the Australian short-term interest rate. The models are indexed by the level effect parameter for the volatility in the short rate process. The inferential approach adopted is Bayesian, with estimation of the models proceeding via a Markov Chain Monte Carlo simulation scheme. Discrimination between the alternative models is based on Bayes factors, estimated from the simulation output using the Savage-Dickey density ratio. A data augmentation approach is used to improve the accuracy of the discrete time approximation of the continuous time models. An empirical investigation is conducted using weekly observations on the Australian 90 day interest rate from January 1990 to July 2000. The Bayes factors indicate that the square root diffusion model has the highest posterior probability of all the nested models.

Keywords: Interest Rate Models; Markov Chain Monte Carlo; Data Augmentation (search for similar items in EconPapers)
JEL-codes: C11 C15 E43 (search for similar items in EconPapers)
Pages: 40 pages
Date: 2004-05
New Economics Papers: this item is included in nep-ecm and nep-fin
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