Functional dynamic factor models with application to yield curve forecasting
KAUST Grant NumberKUS-C1-016-04
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AbstractAccurate 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.
CitationHays S, Shen H, Huang JZ (2012) Functional dynamic factor models with application to yield curve forecasting. The Annals of Applied Statistics 6: 870–894. Available: http://dx.doi.org/10.1214/12-AOAS551.
SponsorsSupported in part NSF Grants DMS-06-06577, CMMI-0800575 and DMS-11-06912.Supported in part by NCI (CA57030), NSF (DMS-09-07170), and by Award No. KUS-C1-016-04, made by King Abdullah University of Science and Technology (KAUST).
PublisherInstitute of Mathematical Statistics
JournalThe Annals of Applied Statistics