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Predicting Short-Term Interest Rates: Does Bayesian Model Averaging Provide Forecast Improvement?

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  • Chew Lian Chua

    ()
    (Melbourne Institute of Applied Economic and Social Research, The University of Melbourne)

  • Sandy Suardi

    (School of Economics and Finance, La Trobe University)

  • Sarantis Tsiaplias

    (KPMG, Australia)

Abstract

This paper examines the forecasting qualities of Bayesian Model Averaging (BMA) over 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 the majority of the short-rate models, but marginally worse off than the best model in each dataset. We observe preference for models incorporating volatility clustering for weekly data and simpler short rate models for high frequency data. This is contrary to the popular belief that a diffusion process with volatility clustering best characterizes the short rate.

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Bibliographic Info

Paper provided by Melbourne Institute of Applied Economic and Social Research, The University of Melbourne in its series Melbourne Institute Working Paper Series with number wp2011n01.

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Length: 41 pages
Date of creation: Jan 2011
Date of revision:
Handle: RePEc:iae:iaewps:wp2011n01

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Keywords: Bayesian model averaging; out-of-sample forecasts;

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References

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