An Infinite Hidden Markov Model for Short-term Interest Rates


ShanghaiTech SEM Working Paper No. 2020-002

John M. Maheu 

DeGroote School of Business, McMaster University

Qiao Yang

ShanghaiTech University

The time-series dynamics of short-term interest rates are important as they are a key input into pricing models of the term structure of interest rates. In this paper we extend popular discrete time short-rate models to include Markov switching of infinite dimension. This is a Bayesian nonparametric model that allows for changes in the unknown conditional distribution over time. Applied to weekly U.S. data we find significant parameter change over time and strong evidence of non-Gaussian conditional distributions. Our new model with an hierarchical prior provides significant improvements in density forecasts as well as point forecasts. We find evidence of recurring regimes as well as structural breaks in the empirical application. 

Keywords: hierarchical Dirichlet process prior, beam sampling, Markov switching, MCMC

Date Written: May, 2016

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