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September 2012 Functional dynamic factor models with application to yield curve forecasting
Spencer Hays, Haipeng Shen, Jianhua Z. Huang
Ann. Appl. Stat. 6(3): 870-894 (September 2012). DOI: 10.1214/12-AOAS551

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 trade-off 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 in a single step. Efficient computing is implemented and a data-driven smoothing parameter is nicely incorporated. 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.

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Spencer Hays. Haipeng Shen. Jianhua Z. Huang. "Functional dynamic factor models with application to yield curve forecasting." Ann. Appl. Stat. 6 (3) 870 - 894, September 2012. https://doi.org/10.1214/12-AOAS551

Information

Published: September 2012
First available in Project Euclid: 31 August 2012

zbMATH: 06096514
MathSciNet: MR3012513
Digital Object Identifier: 10.1214/12-AOAS551

Rights: Copyright © 2012 Institute of Mathematical Statistics

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Vol.6 • No. 3 • September 2012
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