Functional dynamic factor models with application to yield curve forecasting

TL;DR

This paper introduces a novel functional dynamic factor model for yield curve forecasting that combines economic interpretability with improved predictive accuracy, using an efficient EM algorithm and functional data analysis techniques.

Contribution

The paper develops a new functional dynamic factor model with functional factor loading curves, enhancing yield curve forecasting and economic interpretability.

Findings

Model outperforms existing approaches in yield forecasting accuracy.

Efficient EM algorithm enables simultaneous estimation of parameters.

Model applicable to other functional time series beyond yield data.

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.