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A Hybrid Unscented Kalman Filter and Support Vector Machine Model in Option Price Forecasting

  • Shian-Chang Huang
  • Tung-Kuang Wu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4221)

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-Scholes 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.

Keywords

Support Vector Machine Hybrid Model Option Price Support Vector Regression Support Vector Machine Model 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Shian-Chang Huang
    • 1
  • Tung-Kuang Wu
    • 2
  1. 1.Dept. of Business AdministrationNational Changhua University of EducationChanghuaTaiwan
  2. 2.Dept. of Information ManagementNational Changhua University of EducationChanghuaTaiwan

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