An Infinite Hidden Markov Model with GARCH for Short-Term Interest Rates

Chenxing Li and Qiao Yang

MPRA Paper from University Library of Munich, Germany

Abstract: This paper introduces a novel Bayesian time series model that combines the nonparametric features of an infinite hidden Markov model with the volatility persistence captured by the GARCH framework, to effectively model and forecast short-term interest rates. When applied to US 3-month Treasury bill rates, the GARCH-IHMM reveals both structural and persistent changes in volatility, thereby enhancing the accuracy of density forecasts compared to existing benchmark models. Out-of-sample evaluations demonstrate the superior performance of our model in density forecasts and in capturing volatility dynamics due to its adaptivity to different macroeconomic environments.

Keywords: Interest rates; Bayesian nonparametrics; GARCH; density forecasts (search for similar items in EconPapers)
JEL-codes: C11 C14 C51 C53 C58 E43 E47 G17 (search for similar items in EconPapers)
Date: 2025-01-04
New Economics Papers: this item is included in nep-ecm and nep-ets
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Persistent link: https://EconPapers.repec.org/RePEc:pra:mprapa:123200

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