Forecasting High-Frequency Financial Data Volatility Via Nonparametric Algorithms: Evidence From Taiwan'S Financial Markets
學年 95
學期 1
出版（發表）日期 2006-12-01
作品名稱 Forecasting High-Frequency Financial Data Volatility Via Nonparametric Algorithms: Evidence From Taiwan'S Financial Markets
作品名稱（其他語言） 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
著者 Lee, Wo-chiang
單位 淡江大學財務金融學系
出版者 Singapore: World Scientific Publishing
著錄名稱、卷期、頁數 New Mathematics and Natural Computation Journal 2(3), pp.345-359
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.
關鍵字 Integrated volatility; genetic programming; artificial neural networks
語言 en
ISSN 1793-0057 1793-7027
期刊性質 國外
收錄於 SCI EI
產學合作
通訊作者
審稿制度
國別 SGP
公開徵稿
出版型式 紙本
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