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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Clemen, R.: Combining forecasts: a review and annotated bibliography with discussion. International Journal of Forecasting 5, 559–608 (1989)
Black, F., Scholes, M.S.: The pricing of options and corporate liabilities. Journal of Political Economy 81, 637–654 (1973)
Castillo, O., Melin, P.: Hybrid intelligent systems for time series prediction using neural networks, fuzzy logic, and fractal theory. IEEE Trans. Neural Networks 13, 1395–1408 (2002)
Cristianini, N., Shawe-Taylor, J.: An Introduction to Support Vector Machines. Cambridge University Press, Cambridge (2000)
Han, M., Xi, J., Xu, S., Yin, F.-L.: Prediction of chaotic time series based on the recurrent predictor neural network. IEEE Trans. Signal Processing 52, 3409–3416 (2004)
Julier, S.J., Uhlmann, J.K.: A New Extension of the Kalman Filter to Nonlinear Systems. In: The Proceedings of AeroSense: The 11th International Symposium on Aerospace/Defense Sensing,Simulation and Controls, Multi Sensor Fusion, Tracking and Resource Management II, SPIE (1997)
Kecman, V.: Learning and Soft Computing, Support Vector machines, Neural Networks and Fuzzy Logic Models. The MIT Press, Cambridge (2001)
Lawerence, M.J., Edmundson, R.H., O’Connor, M.J.: The accuracy of combining judgemental and stastical forecasts. Management Science 32, 1521–1532 (1986)
Makridakis, S.: Why combining works? International Journal of Forecasting 5, 601–603 (1989)
Terui, N., Dijk, H.K.: Combined forecasts from linear and nonlinear time series models. International Journal of Forecasting 18, 421–438 (2002)
Schoelkopf, B., Burges, C.J.C., Smola, A.J.: Advances in kernel methods - support vector learning. MIT Press, Cambridge (1999)
Vapnik, V.N.: The Nature of Statistical Learning Theory. Springer, New York (1995)
Wan, E.A., van der Merwe, R.: The Unscented Kalman Filter for Nonlinear Estimation. In: Proceedings of Symposium 2000 on Adaptive Systems for Signal Processing, Communication and Control (AS-SPCC), IEEE Press, Los Alamitos (2000)
Wang, L.P. (ed.): Support Vector Machines: Theory and Application. Springer, Berlin, Heidelberg, New York (2005)
Wang, L.P., Teo, K.K., Lin, Z.: Predicting time series using wavelet packet neural networks. In: Proc. IJCNN 2001, pp. 1593–1597 (2001)
Zhang, G.P.: Times series forecasting using a hybrid ARIMA and neural network model. Neurocomputing 50, 159–175 (2003)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Huang, SC., Wu, TK. (2006). A Hybrid Unscented Kalman Filter and Support Vector Machine Model in Option Price Forecasting. In: Jiao, L., Wang, L., Gao, Xb., Liu, J., Wu, F. (eds) Advances in Natural Computation. ICNC 2006. Lecture Notes in Computer Science, vol 4221. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11881070_44
Download citation
DOI: https://doi.org/10.1007/11881070_44
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-45901-9
Online ISBN: 978-3-540-45902-6
eBook Packages: Computer ScienceComputer Science (R0)