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|標題:||FORECASTING REALIZED VOLATILITY WITH LINEAR AND NONLINEAR UNIVARIATE MODELS||作者:||McAleer, M.
|關鍵字:||Bagging;Financial econometrics;Neural networks;Nonlinear models;Realized volatility;Volatility forecasting;neural-network models;time-series;stochastic volatility;market;microstructure;feedforward networks;long-memory;inflation;return;noise;variance||Project:||Journal of Economic Surveys||期刊/報告no：:||Journal of Economic Surveys, Volume 25, Issue 1, Page(s) 6-18.||摘要:||
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 intraday returns. We also consider a simple algorithm based on bagging (bootstrap aggregation) in order to specify the models analysed in this paper.
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