Bayesian comparison of several continuous time models of the Australian short rate


The authors would like to thank the Editor and a referee for some very thoughtful and constructive comments on an earlier draft of the paper. This research has been supported by Australian Research Council Discovery Grant No. DP0208333 and a PhD. Scholarship from the School of Business Systems, Monash University. All numerical results in the paper have been produced using the MATLAB software.


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 nested in a general single-factor diffusion process for the short rate, with each alternative model indexed by the level effect parameter for the volatility. The inferential approach adopted is Bayesian, with estimation of the models proceeding through a Markov chain Monte Carlo simulation scheme. Discrimination between the alternative models is based on Bayes factors. 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 models considered.