Forecasting Realized Volatility With Linear And Nonlinear Models
Source: University of Tokyo
In this paper the authors 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, they consider a consistent and unbiased estimator of the integrated volatility that is computed from high frequency intra-day returns. They also consider a simple algorithm based on bagging (bootstrap aggregation) in order to specify the models analyzed in the paper.