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

Melbourne Institute Working Paper No. 01/11

Date: January 2011


Chew Lian Chua
Sandy Suardi
Sarantis Tsiaplias


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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