Forecasting High-Frequency Financial Data Volatility Via Nonparametric Algorithms: Evidence From Taiwan'S Financial Markets

doi:10.1142/S1793005706000543

淡江大學機構典藏 > 商管學院 > 財務金融學系暨研究所 > 期刊論文 > Item 987654321/72281

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题名:	Forecasting High-Frequency Financial Data Volatility Via Nonparametric Algorithms: Evidence From Taiwan'S Financial Markets
其它题名:	利用無母數法來預測高頻率的財務資料波動率－台灣金融市場實證研究
作者:	Lee, Wo-chiang
贡献者:	淡江大學財務金融學系
关键词:	Integrated volatility;genetic programming;artificial neural networks
日期:	2006-12
上传时间:	2011-10-24 10:19:47 (UTC+8)
出版者:	Singapore: World Scientific Publishing
摘要:	This paper uses two computational intelligence algorithms, namely, artificial neural networks (ANN) and genetic programming (GP), for forecasting the volatility of high-frequency TAIEX financial data with four different horizons and compares the out-sample forecasting performance with the GARCH(1,1), EGRACH(1,1) and GJR-GARCH(1,1) models. Based on intraday integrated volatility, the mean squared error (MSE), mean absolute error (MAE), mean absolute percentage error (MAPE), Theil's U and the VaR backtest are used as performance indexes. Our empirical results reveal that the GP and ANN perform reasonably well in forecasting out-sample volatility compared to other parametric volatility forecasting models for most of the performance indexes. Our results also suggest that nonparametric computational intelligence algorithms are powerful for modeling the volatility of high-frequency intraday financial data.
關聯:	New Mathematics and Natural Computation Journal 2(3), pp.345-359
DOI:	10.1142/S1793005706000543
显示于类别:	[財務金融學系暨研究所] 期刊論文

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