Nonparametric ARCH with additive mean and multiplicative volatility: a new estimation procedure

Motivated by the misspecification problem in time series analysis, the nonparametric approach has quickly developed in the latest years. First models in literature were focused on the estimation of the conditional mean. It is well known that alongside the conditional mean it is important to study the series volatility (conditional variance). The following paper deals with nonparametric autoregression with multiplicative volatility and additive mean as studied by Yang et al. (1999). A new estimation procedure is here provided. The procedure uses the residual-based estimator, backfitting algorithm and the local polynomial estimation. Some applications with simulated and real data will be presented.