期刊論文
| 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 |
| 相關連結 |
機構典藏連結 ( http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/72281 ) |