High Dimensional Yield Curves: Models and Forecasting

Clive Bowsher and Roland Meeks

OFRC Working Papers Series from Oxford Financial Research Centre

Abstract: Functional Signal plus Noise (FSN) models are proposed for analysing the dynamics of a large cross-section of yields or asset prices in which contemporaneous observations are functionally related. The FSN models are used to forecast high dimensional yield curves for US Treasury bonds at the one month ahead horizon. The models achieve large reductions in mean square forecast errors relative to a random walk for yields and readily dominate both the Diebold and Li (2006) and random walk forecasts across all maturities studied. We show that the Expectations Theory (ET) of the term structure completely determines the conditional mean of any zero-coupon yield curve. This enables a novel evaluation of the ET in which its 1-step ahead forecasts are compared with those of rival methods such as the FSN models, with the results strongly supporting the growing body of empirical evidence against the ET. Yield spreads do provide important information for forecasting the yield curve, especially in the case of shorter maturities, but not in the manner prescribed by the Expectations Theory.

Keywords: Yield curve; term structure; expectations theory; FSN models; functional time series; forecasting; state space form; cubic spline. (search for similar items in EconPapers)
JEL-codes: C33 C51 C53 E47 G12 (search for similar items in EconPapers)
Pages: 37
Date: 2006
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Citations: View citations in EconPapers (4)

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Related works:
Working Paper: High Dimensional Yield Curves: Models and Forecasting (2006)
Working Paper: High Dimensional Yield Curves: Models and Forecasting (2006)
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