Nowman, K. Ben and Saltoglu, Burak (2003) Continuous time and nonparametric modelling of U.S. interest rate models. International Review of Financial Analysis, 12 (1). pp. 25-34. ISSN 1057-5219Full text not available from this repository.
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 [BRâ??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.
|Uncontrolled Keywords:||Interest rates, Forecasting, Nonparametric, Continuous time|
|Subjects:||University of Westminster > Westminster Business School|
|Depositing User:||Users 4 not found.|
|Date Deposited:||21 Sep 2005|
|Last Modified:||13 Oct 2009 15:18|
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