Forecasting the Yield Curve with the "Stock Dog" Technique

Abstract

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.