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

Author

Listed:
  • 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.

Suggested Citation

  • Chew Lian Chua & Sandy Suardi & Sarantis Tsiaplias, 2011. "Predicting Short-Term Interest Rates: Does Bayesian Model Averaging Provide Forecast Improvement?," Melbourne Institute Working Paper Series wp2011n01, Melbourne Institute of Applied Economic and Social Research, The University of Melbourne.
  • Handle: RePEc:iae:iaewps:wp2011n01
    as

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    File URL: http://melbourneinstitute.unimelb.edu.au/downloads/working_paper_series/wp2011n01.pdf
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    References listed on IDEAS

    as
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    More about this item

    Keywords

    Bayesian model averaging; out-of-sample forecasts;

    JEL classification:

    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation
    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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