Functional Dynamic Factor Models with Application to Yield Curve Forecasting

• Activity Number: 591
• Type: Invited
• Date/Time: Thursday, August 2, 2012 : 8:30 AM to 10:20 AM
• Sponsor: Business and Economic Statistics Section
• Author(s): Haipeng Shen*+ and Spencer Hays and Jianhua Huang
• Companies: The University of North Carolina at Chapel Hill and Pacific Northwest Research Laboratory and Texas A&M University
• Keywords: functional data analysis ; time series ; penalization

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. Model estimation is achieved through an expectation maximization algorithm, where the time series parameters and factor loading curves are simultaneously estimated. Efficient computing is implemented and a data-driven smoothing parameter is nicely incorporated. We show that our model performs well in forecasting actual yield data compared with existing approaches.