Forecasting Realized Volatility with Linear and Nonlinear Models - Michael McAleer; Marcelo C. Medeiros (Pontifícia Universidade Católica do Rio de Janeiro (PUC-Rio), Departamento de Economia, 2010)
In this paper we consider a nonlinear model based on neural networks as well as linear models to forecast the daily volatility of the S&P 500 and FTSE 100 indexes. As a proxy for daily volatility, we consider a consistent and unbiased estimator of the integrated volatility that is computed from high frequency intra-day returns. We also consider a simple algorithm based on bagging (bootstrap aggregation) in order to specify the models analyzed in this paper.
Keywords: 330; Financial econometrics; volatility forecasting; neural networks; nonlinear models; realized volatility; bagging.; Volatilität; Prognoseverfahren; Nichtlineare Regression; Neuronale Netze; Algorithmus