Predicting short-term interest rates using Bayesian model averaging: Evidence from weekly and high frequency data

Chew Lian Chua, Sandy Suardi, Sarantis Tsiaplias

Research output: Journal PublicationArticlepeer-review

11 Citations (Scopus)

Abstract

This paper examines the forecasting performance of Bayesian model averaging (BMA) for a set of single factor models of short-term interest rates. Using weekly and high frequency data for the one-month Eurodollar rate, BMA produces predictive likelihoods that are considerably better than those associated with the majority of the short-rate models, but marginally worse than those of the best model in each dataset. We also find that BMA forecasts based on recent predictive likelihoods are preferred to those based on the marginal likelihood of the entire dataset.

Original languageEnglish
Pages (from-to)442-455
Number of pages14
JournalInternational Journal of Forecasting
Volume29
Issue number3
DOIs
Publication statusPublished - Jul 2013
Externally publishedYes

Keywords

  • Bayesian model averaging
  • Out-of-sample forecasts
  • Short-term interest rates

ASJC Scopus subject areas

  • Business and International Management

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