期刊
ANNALS OF APPLIED STATISTICS
卷 6, 期 3, 页码 870-894出版社
INST MATHEMATICAL STATISTICS
DOI: 10.1214/12-AOAS551
关键词
Functional data analysis; expectation maximization algorithm; natural cubic splines; cross-validation; roughness penalty
资金
- NSF [DMS-06-06577, CMMI-0800575, DMS-11-06912, DMS-09-07170]
- NCI [CA57030]
- King Abdullah University of Science and Technology (KAUST) [KUS-C1-016-04]
- Direct For Mathematical & Physical Scien
- Division Of Mathematical Sciences [1007618, 1208952] Funding Source: National Science Foundation
- Directorate For Engineering
- Div Of Civil, Mechanical, & Manufact Inn [0800575] Funding Source: National Science Foundation
- Division Of Mathematical Sciences
- Direct For Mathematical & Physical Scien [1106912] Funding Source: National Science Foundation
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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