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Forecasting interest rates: A Comparative assessment of some second generation non-linear model

  • Dilip M. Nachane

    ()

    (Indira Gandhi Institute of Development Research)

  • Jose G. Clavel

    ()

    (Universidad de Murcia)

Modelling 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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Paper provided by Indira Gandhi Institute of Development Research, Mumbai, India in its series Indira Gandhi Institute of Development Research, Mumbai Working Papers with number 2005-009.

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Length: 32 pages
Date of creation: 2005
Date of revision:
Handle: RePEc:ind:igiwpp:2005-009
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  1. Simon M. Potter, 1993. "A Nonlinear Approach to U.S. GNP," UCLA Economics Working Papers 693, UCLA Department of Economics.
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  8. Engle, Robert F, 1982. "Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation," Econometrica, Econometric Society, vol. 50(4), pages 987-1007, July.
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