Forecasting the Yield Curve with the "Stock Dog" Technique

Pierre Rostan, Alexandra Rostan


We design an innovative technique coupled with Monte Carlo simulation that accurately forecasts the yield curve.
This "stock dog" technique forces the simulated yield curve inside bands, using the information embedded in the
shapes of the most recent yield curves captured by the level, the slope and the curvature provided by the Nelson
and Siegel (1987) model. Based on the RMSE criteria, we show that, on a sample of 2,321 U.S. Treasury yield
curves over the 2002-2012 period, the "stock dog" technique, coupled either with the Cox, Ingersoll and Ross
(1985) or a Stochastic Fifth-Order Polynomial models, is superior to the Diebold and Li (2006) model when
forecasting the yield curve over a 20-day horizon. The "stock dog" technique is a variation of the Diebold and Li
(2006) model and improves significantly its forecasting power. It may help market participants in need of an
accurate short term forecast of the yield curve.

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