Nonparametric Autoregression with Multiplicative Volatility and Additive mean

Lijian Yang, Wolfgang Hardle and Jens Nielsen

Journal of Time Series Analysis, 1999, vol. 20, issue 5, 579-604

Abstract: For over a decade, nonparametric modelling has been successfully applied to studying nonlinear structures in financial time series. It is well known that the usual nonparametric models often have less than satisfactory performance when dealing with more than one lag. When the mean has an additive structure, however, better estimation methods are available which fully exploit such a structure. Although in the past such nonparametric applications had been focused more on the estimation of the conditional mean, it is equally if not more important to measure the future risk of the series along with the mean. For the volatility function, i.e. the conditional variance given the past, a multiplicative structure is more appropriate than an additive structure, as the volatility is a positive scale function and a multiplicative model provides a better interpretation of each lagged value's influence on such a function. In this paper we consider the joint estimation of both the additive mean and the multiplicative volatility. The technique used is marginally integrated local polynomial estimation. The procedure is applied to the deutschmark/US dollar daily exchange returns.

Date: 1999
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