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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

6 Citations (Scopus)

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.

Original languageEnglish
Title of host publicationAdvances in Natural Computation - Second International Conference, ICNC 2006, Proceedings,
PublisherSpringer Verlag
Pages303-312
Number of pages10
ISBN (Print)3540459014, 9783540459019
DOIs
Publication statusPublished - 2006
Event2nd International Conference on Natural Computation, ICNC 2006 - Xi'an, China
Duration: 2006 Sep 242006 Sep 28

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume4221 LNCS - I
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other2nd International Conference on Natural Computation, ICNC 2006
CountryChina
CityXi'an
Period06-09-2406-09-28

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

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    Huang, S. C., & Wu, T. K. (2006). A hybrid unscented Kalman filter and support vector machine model in option price forecasting. In Advances in Natural Computation - Second International Conference, ICNC 2006, Proceedings, (pp. 303-312). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4221 LNCS - I). Springer Verlag. https://doi.org/10.1007/11881070_44