Conference Paper

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

DOI: 10.1007/11881070_44 Conference: Advances in Natural Computation, Second International Conference, ICNC 2006, Xi'an, China, September 24-28, 2006. Proceedings, Part I
Source: DBLP

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.

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