| Author: | Nowman, K. B. Saltoglu, B. |
| Title: | Continuous time and nonparametric modelling of U.S. interest rate models |
| Journal: | International Review of Financial Analysis
2003 : VOL. 12:1, p. 25-34 |
| Index terms: | INTEREST RATES FORECASTING TIME MODELS USA |
| Language: | eng |
| Abstract: | In this paper the authors compare the forecasting performance of different models of interest rates using parametric and nonparametric estimation methods. In particular, the authors use three popular nonparametric methods, namely, artificial neural networks (ANN), k- nearest neighbor (k-NN), and local linear regression (LL). These are compared with forecasts obtained from two- factor continuous time interest rate models, namely, Chan, Karolyi, Longstaff, and Sanders, Cos, Ingersoll, and Ross, Brennan and Schwartz, and Vasicek. The authors find that while the parametnc continuous time method, specifically Vasicek, produces the most successful forecasts, the nonparametric k-NN performed well. |
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