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

DOI

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.

Copyright

© 2006, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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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  - Proceedings of the 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  - 10.2991/jcis.2006.53
ID  - Huang2006/10
ER  -