Abstract
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 model 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 help investors for reducing their risk in online trading.
| Original language | English |
|---|---|
| Pages (from-to) | 2819-2825 |
| Number of pages | 7 |
| Journal | Expert Systems with Applications |
| Volume | 34 |
| Issue number | 4 |
| DOIs | |
| Publication status | Published - 2008 May 1 |
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All Science Journal Classification (ASJC) codes
- Engineering(all)
- Computer Science Applications
- Artificial Intelligence
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Online option price forecasting by using unscented Kalman filters and support vector machines. / Huang, Shian Chang.
In: Expert Systems with Applications, Vol. 34, No. 4, 01.05.2008, p. 2819-2825.Research output: Contribution to journal › Article
TY - JOUR
T1 - Online option price forecasting by using unscented Kalman filters and support vector machines
AU - Huang, Shian Chang
PY - 2008/5/1
Y1 - 2008/5/1
N2 - 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 model 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 help investors for reducing their risk in online trading.
AB - 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 model 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 help investors for reducing their risk in online trading.
UR - http://www.scopus.com/inward/record.url?scp=38649093286&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=38649093286&partnerID=8YFLogxK
U2 - 10.1016/j.eswa.2007.05.018
DO - 10.1016/j.eswa.2007.05.018
M3 - Article
AN - SCOPUS:38649093286
VL - 34
SP - 2819
EP - 2825
JO - Expert Systems with Applications
JF - Expert Systems with Applications
SN - 0957-4174
IS - 4
ER -