A hybrid unscented Kalman filter and support vector machine model in option price forecasting

研究成果: Conference contribution

6 引文 斯高帕斯(Scopus)

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-Scholea 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.

原文English
主出版物標題Advances in Natural Computation - Second International Conference, ICNC 2006, Proceedings,
發行者Springer Verlag
頁面303-312
頁數10
ISBN(列印)3540459014, 9783540459019
DOIs
出版狀態Published - 2006
事件2nd International Conference on Natural Computation, ICNC 2006 - Xi'an, China
持續時間: 2006 九月 242006 九月 28

出版系列

名字Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
4221 LNCS - I
ISSN(列印)0302-9743
ISSN(電子)1611-3349

Other

Other2nd International Conference on Natural Computation, ICNC 2006
國家China
城市Xi'an
期間06-09-2406-09-28

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

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