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

Conference PaperinLecture Notes in Computer Science · January 2006with32 Reads
Impact Factor: 0.51 · DOI: 10.1007/11881070_44 · Source: DBLP
Conference: Advances in Natural Computation, Second International Conference, ICNC 2006, Xi'an, China, September 24-28, 2006. Proceedings, Part I


    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.