An infinite hidden Markov model for short-term interest rates

John Maheu and Qiao Yang

Journal of Empirical Finance, 2016, vol. 38, issue PA, 202-220

Abstract: 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 a 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 (search for similar items in EconPapers)
JEL-codes: C11 C14 C22 C58 (search for similar items in EconPapers)
Date: 2016
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Citations: View citations in EconPapers (11)

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Related works:
Working Paper: An Infinite Hidden Markov Model for Short-term Interest Rates (2015)
Working Paper: An Infinite Hidden Markov Model for Short-term Interest Rates (2015)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:empfin:v:38:y:2016:i:pa:p:202-220

DOI: 10.1016/j.jempfin.2016.06.006

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