Forecasting the Term Structure of Interest Rates with Potentially Misspecified Models

Yunjong Eo and Kyu Ho Kang

No 2016-05, Working Papers from University of Sydney, School of Economics

Abstract: This paper assesses the predictive gains of the pooling method in yield curve prediction. We consider three individual yield curve prediction models: the dynamic Nelson-Siegel model (DNS) and the arbitrage-free Nelson-Siegel model in addition to the random walk (RW) model as a benchmark. Despite the popularity of these three frameworks, none of them dominates the others across all maturities and forecast horizons. This fact indicates that those models are potentially misspecified. We investigate whether combining the possibly misspecified models in a linear form helps improve the predictive accuracy. To do this, we evaluate the out-of-sample forecasts of the pooled models in comparison with the individual models. In terms of density prediction, the pooled model of the DNS and RW models consistently outperforms those individual models regardless of maturities and forecast horizons. Our findings strongly suggest that one needs to try the pooling method rather than choosing one of the alternative models.

Keywords: Model combination; Bayesian MCMC method; Markov switching process; Dynamic Nelson-Siegel; Affine term structure model (search for similar items in EconPapers)
Date: 2016-03
New Economics Papers: this item is included in nep-for
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