Forecasting Realized Volatility with Linear and Nonlinear Univariate Models

Journal of Economic Surveys (Impact Factor: 1.33). 01/2010; DOI: 10.1111/j.1467-6419.2010.00640.x
Source: RePEc

ABSTRACT 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 futures. 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.

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