Volatility forecasting with smooth transition exponential smoothing

Volatility forecasting with smooth transition exponential smoothing

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Article ID: iaor2005625
Country: Netherlands
Volume: 20
Issue: 2
Start Page Number: 273
End Page Number: 286
Publication Date: Apr 2004
Journal: International Journal of Forecasting
Authors:
Keywords: forecasting: applications
Abstract:

Adaptive exponential smoothing methods allow smoothing parameters to change over time, in order to adapt to changes in the characteristics of the time series. This paper presents a new adaptive method for predicting the volatility in financial returns. It enables the smoothing parameter to vary as a logistic function of user-specified variables. The approach is analogous to that used to model time-varying parameters in smooth transition generalised autoregressive conditional heteroskedastic (GARCH) models. These non-linear models allow the dynamics of the conditional variance model to be influenced by the sign and size of past shocks. These factors can also be used as transition variables in the new smooth transition exponential smoothing (STES) approach. Parameters are estimated for the method by minimising the sum of squared deviations between realised and forecast volatility. Using stock index data, the new method gave encouraging results when compared to fixed parameter exponential smoothing and a variety of GARCH models.

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