Please use this identifier to cite or link to this item: http://hdl.handle.net/11455/69894
標題: FORECASTING REALIZED VOLATILITY WITH LINEAR AND NONLINEAR UNIVARIATE MODELS
作者: McAleer, M.
Medeiros, M.C.
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
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.
URI: http://hdl.handle.net/11455/69894
ISSN: 0950-0804
文章連結: http://dx.doi.org/10.1111/j.1467-6419.2010.00640.x
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