An Infinite Hidden Markov Model for Short-term Interest Rates

34 Pages Posted: 17 Jan 2020 Last revised: 2 Mar 2020

See all articles by John M. Maheu

John M. Maheu

McMaster University - Michael G. DeGroote School of Business; RCEA

Qiao Yang

ShanghaiTech University - School of Entrepreneurship and Management

Date Written: May 3, 2016

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 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

JEL Classification: C58, C14, C22, C11

Suggested Citation

Maheu, John M. and Yang, Qiao, An Infinite Hidden Markov Model for Short-term Interest Rates (May 3, 2016). ShanghaiTech SEM Working Paper No. 2020-003. Available at SSRN: https://ssrn.com/abstract=3521099 or http://dx.doi.org/10.2139/ssrn.3521099

John M. Maheu

McMaster University - Michael G. DeGroote School of Business ( email )

1280 Main Street West
Hamilton, Ontario L8S 4M4
Canada

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RCEA

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Rimini (RN), RN 47900
Italy

HOME PAGE: http://www.rcfea.org/

Qiao Yang (Contact Author)

ShanghaiTech University - School of Entrepreneurship and Management ( email )

100 Haike Rd
Pudong Xinqu, Shanghai
China

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