"High Dimensional Yield Curves: Models and Forecasting"
CLIVE BOWSHER and ROLAND MEEKS
Nuffield College, University of Oxford
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.
JEL classification: C33, C51, C53, E47, G12.