Continuous time and nonparametric modelling of U.S. interest rate models
International Review of Financial Analysis, cilt.12, sa.1, ss.25-34, 2003 (Scopus)
- Yayın Türü: Makale / Tam Makale
- Cilt numarası: 12 Sayı: 1
- Basım Tarihi: 2003
- Doi Numarası: 10.1016/s1057-5219(02)00123-0
- Dergi Adı: International Review of Financial Analysis
- Derginin Tarandığı İndeksler: Scopus
- Sayfa Sayıları: ss.25-34
- Anahtar Kelimeler: Continuous time, Forecasting, Interest rates, Nonparametric
- Boğaziçi Üniversitesi Adresli: Evet
Özet
In this paper we compare the forecasting performance of different models of interest rates using parametric and nonparametric estimation methods. In particular, we use three popular nonparametric methods, namely, artificial neural networks (ANN), k-nearest neighbour (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 [CKLS, J. Finance 47 (1992) 1209]; Cos, Ingersoll, and Ross [CIR, Econometrica 53 (1985) 385]; Brennan and Schwartz [BR-SC, J. Financ. Quant. Anal. 15 (1980) 907]; and Vasicek [J. Financ. Econ. 5 (1977) 177]. We find that while the parametric continuous time method, specifically Vasicek, produces the most successful forecasts, the nonparametric k-NN performed well. © 2003 Elsevier Science Inc. All rights reserved.