Abstract
Accurate forecasting of zero coupon bond yields for a continuum of maturities is paramount to bond portfolio management and derivative security pricing. Yet a universal model for yield curve forecasting has been elusive, and prior attempts often resulted in a tradeoff between goodness-of-fit and consistency with economic theory. To address this, herein we propose a novel formulation which connects the dynamic factor model (DFM) framework with concepts from functional data analysis: a DFM with functional factor loading curves. This results in a model capable of forecasting functional time series. Further, in the yield curve context we show that the model retains economic interpretation. We show that our model performs very well on forecasting actual yield data compared with existing approaches, especially in regard to profit-based assessment for an innovative trading exercise. We further illustrate the viability of our model to applications outside of yield forecasting.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
See http://www.ssc.upenn.edu/~fdiebold/papers/paper49/FBFITTED.txt for the actual data.
References
Basilevsky, A. (1994), Statistical Factor Analysis and Related Methods, Wiley: New York.
Bowsher, C. G. and Meeks, R. (2008), “The Dynamics of Economic Functions: Modeling and Forecasting the Yield Curve,” Journal of the American Statistical Association, 103, 1419–37.
Dempster, A. P., Laird, N. M., and Rubin, D. B. (1977), “Maximum Likelihood from Incomplete Data via EM Algorithm,” Journal of the Royal Statistical Society, Series B (Methodological), 39, 1–38.
Diebold, F. X. and Li, C. (2006), “Forecasting the Term Structure of Government Bond Yields,” Journal of Econometrics, 130, 337–64.
Fama, E. and Bliss, R. (1987), “The Information in Long-Maturity Forward Rates,” American Economic Review, 77, 680–92.
Green, P. J. and Silverman, B. W. (1994), Nonparametric Regression and Generalized Linear Models: A Roughness Penalty Approach, Chapman and Hall: New York.
Hays, S., Shen, H., and Huang, J. Z. (2012a), “Functional dynamic factor models with application to yield curve forecasting,” Annals of Applied Statistics, 6, 870–894.
Hays, S., Shen, H., and Huang, J. Z. (2012b), “Supplement to “Functional Dynamic Factor Models with Application to Yield Curve Forecasting”,” Available online at http://www.unc.edu/~haipeng/publication/FDFMsupplement.pdf.
Judge, G. G. (1985), The Theory and Practice of Econometrics, Wiley: New York.
Koopman, S. J., Mallee, M. I. P., and der Wel, M. V. (2010), “Analyzing the Term Structure of Interest Rates Using the Dynamic Nelson-Siegel Model with Time-Varying Parameters,” Journal of Business and Economic Statistics, 28, 329–343.
Meng, X. L. and Rubin, D. B. (1993), “Maximum Likelihood Estimation via the ECM Algorithm: A General Framework,” Biometrika, 80, 267–278.
Press, W. H., Teukolsky, S. A., Vetterling, W. T., and Flannery, B. P. (1992), Numerical Recipes in Fortran: The Art of Scientific Computing, Cambridge University Press: New York.
Ramsay, J. O. and Silverman, B. W. (2002), Applied Functional Data Analysis: Methods and Case Studies, Springer-Verlag: New York.
Ramsay, J. O. and Silverman, B.W. –(2005), Functional Data Analysis, Springer-Verlag: New York, 2nd ed.
Shen, H. (2009), “On Modeling and Forecasting Time Series of Curves,” Technometrics, 51, 227–38.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer Science+Business Media New York
About this paper
Cite this paper
Hays, S., Shen, H., Huang, J.Z. (2013). Applications of Functional Dynamic Factor Models. In: Hu, M., Liu, Y., Lin, J. (eds) Topics in Applied Statistics. Springer Proceedings in Mathematics & Statistics, vol 55. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-7846-1_3
Download citation
DOI: https://doi.org/10.1007/978-1-4614-7846-1_3
Published:
Publisher Name: Springer, New York, NY
Print ISBN: 978-1-4614-7845-4
Online ISBN: 978-1-4614-7846-1
eBook Packages: Mathematics and StatisticsMathematics and Statistics (R0)