資料載入中.....
|
請使用永久網址來引用或連結此文件:
http://ir.ncue.edu.tw/ir/handle/987654321/1866
|
| 題名: | A Hybrid Unscented Kalman Filter and Support Vector Machine Model in Option Price Forecasting |
| 作者: | Shian-Chang Huang;Tung-Kuang Wu |
| 貢獻者: | 企業管理學系 |
| 日期: | 2006
|
| 上傳時間: | 2010-11-15T07:44:15Z
|
| 摘要: | This study develops a hybrid model that combines unscented Kalman filters (UKFs) and support vector machines (SVMs) to implement an online option price predictor. In the hybrid model, the UKF is used to infer latent variables and make a prediction based on the Black-Scholes formula, while the SVM is employed to capture the nonlinear residuals between the actual option prices and the UKF predictions. Taking option data traded in Taiwan Futures Exchange, this study examined the forecasting accuracy of the proposed model, and found that the new hybrid model is superior to pure SVM models or hybrid neural network models in terms of three types of options. This model can also help investors for reducing their risk in online trading. |
| 關聯: | Lecture Notes in Computer Science, 4221:303-312, DOI:10.1007/11881070_44 |
| 顯示於類別: | [企業管理學系] 期刊論文
|
在NCUEIR中所有的資料項目都受到原著作權保護.
|
