Forecasting Interest Rates - A Comparative Assessment Of Some Second Generation Non-Linear Models
AbstractModelling and forecasting of interest rates has traditionally proceeded in the framework of linear stationary models such as ARMA and VAR, but only with moderate success. We examine here four models which account for several specific features of real world asset prices such as non-stationarity and non-linearity. Our four candidate models are based respectively on wavelet analysis, mixed spectrum analysis, non-linear ARMA models with Fourier coefficients, and the Kalman filter. These models are applied to weekly data on interest rates in India, and their forecasting performance is evaluated vis--vis three GARCH models (GARCH (1,1), GARCH-M (1,1) and EGARCH (1,1)) as well as the random walk model. The Kalman filter model emerges at the top, with wavelet and mixed spectrum models also showing considerable promise.
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Bibliographic InfoPaper provided by East Asian Bureau of Economic Research in its series Finance Working Papers with number 22359.
Date of creation: Jan 2005
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interest rates; wavelets; mixed spectra; non-linear ARMA; Kalman filter; GARCH; Forecast encompassing.;
Other versions of this item:
- Dilip Nachane & Jose Clavel, 2008. "Forecasting interest rates: a comparative assessment of some second-generation nonlinear models," Journal of Applied Statistics, Taylor & Francis Journals, vol. 35(5), pages 493-514.
- Dilip M. Nachane & Jose G. Clavel, 2005. "Forecasting interest rates: A Comparative assessment of some second generation non-linear model," Indira Gandhi Institute of Development Research, Mumbai Working Papers 2005-009, Indira Gandhi Institute of Development Research, Mumbai, India.
- E43 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Interest Rates: Determination, Term Structure, and Effects
- E47 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Forecasting and Simulation: Models and Applications
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