Forecasting Yield Curves in an Adaptive Framework

Ying Chen () and Bo Li ()
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Ying Chen: National University of Singapore
Bo Li: National University of Singapore

Central European Journal of Economic Modelling and Econometrics, 2011, vol. 3, issue 4, 237-259

Abstract: Forecasting yield curves with regime switches is important in academia and financial industry. As the number of interest rate maturities increases, it poses difficulties in estimating parameters due to the curse of dimensionality. To deal with such a feature, factor models have been developed. However, the existing approaches are restrictive and largely based on the stationarity assumption of the factors. This inaccuracy creates non-ignorable financial risks, especially when the market is volatile. In this paper, a new methodology is proposed to adaptively forecast yield curves. Specifically, functional principal component analysis (FPCA) is used to extract factors capable of representing the features of yield curves. The local AR(1) model with time-dependent parameters is used to forecast each factor. Simulation and empirical studies reveal the superiority of this method over its natural competitor, the dynamic Nelson-Siegel (DNS) model. For the yield curves of the U.S. and China, the adaptive method provides more accurate 6- and 12-month ahead forecasts.

Keywords: interest rates; functional principal component analysis; local parametric model; Nelson-Siegel model (search for similar items in EconPapers)
JEL-codes: C53 E47 G17 (search for similar items in EconPapers)
Date: 2011
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Citations: View citations in EconPapers (2)

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