Proceedings of the 9th Joint International Conference on Information Sciences (JCIS-06)

Combing Extended Kalman Filters and Support Vector Machines for Online Option Price Forecasting

Authors
Shian-chang Huang 0
Corresponding Author
Shian-chang Huang
0Dept. of Business Administration, NCUE, Taiwan
Available Online October 2006.
DOI
https://doi.org/10.2991/jcis.2006.53How to use a DOI?
Keywords
Online forecast, Extended Kalman filter, Support vector machine, Feedforward neural network
Abstract
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.

Download article (PDF)

Proceedings
9th Joint International Conference on Information Sciences (JCIS-06)
Part of series
Advances in Intelligent Systems Research
Publication Date
October 2006
ISBN
978-90-78677-01-7
ISSN
1951-6851
DOI
https://doi.org/10.2991/jcis.2006.53How to use a DOI?
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  -