Modeling and Forecasting Serially Dependent Yield Curves
Hao Li
Abstract
Yield curves are serially dependent. By leveraging this feature, this article proposes a semiparametric model to estimate and forecast yield curves based on factors driving the serial dependence. In this model, factor loadings are related to the autocovariance functions of the continuous and smooth yield curve subject to unobservable errors; the dynamic evolution is driven by a vector autoregression for a small set of factors, and the yield data determine the number of factors and aggregation of information over different lags. Applying this method to monthly U.S. government bond yields from January 1985 through December 2023, I find that the dynamic structure of yield curves reduces to a vector process lying in a 3-dimensional space, with 1-month lag information. Yield curve residuals from this new model exhibit less autocorrelation than alternative three-factor models. Moreover, this new model provides favorable forecasting results.
Evidence weight
Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40
| F · citation impact | 0.50 × 0.4 = 0.20 |
| M · momentum | 0.50 × 0.15 = 0.07 |
| V · venue signal | 0.50 × 0.05 = 0.03 |
| R · text relevance † | 0.50 × 0.4 = 0.20 |
† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.