Forecasting the yield curve: A statistical model with market survey data

Common factors, principal components analysis, and the term structure of interest rates

“…Due to its ability to explain intuition in the decomposition of the yield curve, traditional principal components analysis (PCA) is a popular tool for analyzing the term structure of interest rates (Bikbov & Chernov, 2011;Chernov & Bikbov, 2010;Collin-Dufresne, Goldstein, & Jones, 2008;Hamilton & Wu, 2012, forthcoming;Joslin, Singleton, & Zhu, 2011;Joslin, Priebsch, & Singleton, 2010;Joslin, Singleton, & Zhu, 2011;Leite, Filho, & Vicente, 2010). However, while it has an abundance of applications to yield curve decomposition, its applications to analyze joint factor structure of several yield curves in a global setting have been relatively few in number (Egorov, Li, & Ng, 2011;Novosyolov & Satchkov, 2008).…”

Yield Curve Forecasting with the Burg Model

“…Leite et al . () proposed a statistical model using data from a market survey and the forward rate risk premium. Füss and Nikitina () dusted off yield curve dynamics in terms of the unobservable components level, slope and curvature, and applied the factor‐augmented vector autoregression (FAVAR) framework for forecasting interest rates.…”

“…Vicente and Tabak (2008), after testing the predictive ability of a variety of models, compared affine term-structure models with the Diebold and Li (2006) model and suggested that forecasts made with the Diebold and Li model are superior, and appear to be more accurate at long horizons than other different benchmark forecasts. Leite et al (2010) proposed a statistical model using data from a market survey and the forward rate risk premium. Füss and Nikitina (2011) dusted off yield curve dynamics in terms of the unobservable components level, slope and curvature, and applied the factor-augmented vector autoregression (FAVAR) framework for forecasting interest rates.…”

Forecasting the Yield Curve with the "Stock Dog" Technique

“…These findings confirm our sensible choice of the Diebold and Li (2006) model as a source of inspiration and as a benchmark of the "stock dog" technique. Leite, Filho and Vicente (2010) proposed 'a statistical model to forecast the yield curve, using two major sources of information: data from a market survey and the forward rate risk premium. They forecasted the yield curve six months ahead and compared the results with the Diebold and Li (2006) model, a random walk process and the predictions based on the forward rate.…”