@inproceedings{f83e45070c22469ba7056abc705d7ec1,
title = "A hybrid unscented Kalman filter and support vector machine model in option price forecasting",
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-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.",
author = "Huang, {Shian Chang} and Wu, {Tung Kuang}",
year = "2006",
doi = "10.1007/11881070_44",
language = "English",
isbn = "3540459014",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "303--312",
booktitle = "Advances in Natural Computation - Second International Conference, ICNC 2006, Proceedings,",
address = "Germany",
note = "2nd International Conference on Natural Computation, ICNC 2006 ; Conference date: 24-09-2006 Through 28-09-2006",
}