Combing Extended Kalman Filters and Support Vector Machines for Online Option Price Forecasting
Shian-chang Huang 0
0Dept. of Business Administration, NCUE, Taiwan
Available Online October 2006.
- https://doi.org/10.2991/jcis.2006.53How to use a DOI?
- Online forecast, Extended Kalman filter, Support vector machine, Feedforward neural network
- This study combines extended Kalman filters (EKFs) and support vector machines (SVMs) to implement a fast online predictor for option prices. The EKF is used to infer latent variables and makes 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 EKF predictions. Taking option data traded in Taiwan Futures Exchange, this study examined the forecasting accuracy of the proposed model, and found that the hybrid model is superior to traditional feedforward neural network models, which can significantly reduce the root-mean-squared forecasting errors.
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
TY - CONF AU - Shian-chang Huang PY - 2006/10 DA - 2006/10 TI - Combing Extended Kalman Filters and Support Vector Machines for Online Option Price Forecasting BT - 9th Joint International Conference on Information Sciences (JCIS-06) PB - Atlantis Press SP - 219 EP - 222 SN - 1951-6851 UR - https://doi.org/10.2991/jcis.2006.53 DO - https://doi.org/10.2991/jcis.2006.53 ID - Huang2006/10 ER -