An algorithm for nonparametric GARCH modelling
نویسندگان
چکیده
A simple iterative algorithm for nonparametric 1rst-order GARCH modelling is proposed. This method o4ers an alternative to 1tting one of the many di4erent parametric GARCH speci1cations that have been proposed in the literature. A theoretical justi1cation for the algorithm is provided and examples of its application to simulated data from various stationary processes showing stochastic volatility, as well as empirical 1nancial return data, are given. The nonparametric procedure is found to often give better estimates of the unobserved latent volatility process than parametric modelling with the standard GARCH(1,1) model, particularly in the presence of asymmetry and other departures from the standard GARCH speci1cation. Extensions of the basic iterative idea to more complex time series models combining ARMA or GARCH features of possibly higher order are suggested. c © 2002 Elsevier Science B.V. All rights reserved.
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