Support vector machine combined with GARCH models for call option price prediction

2009 International Conference on Artificial Intelligence and Computational Intelligence, AICI 2009

Volumn 1, Issue , 2009, Pages 35-40

  Lu, Chih Chaing ^a   Wu, Chih Hung ^b  

^a NATIONAL TAIPEI UNIVERSITY OF BUSINESS   (Taiwan)

^b NATIONAL TAICHUNG UNIVERSITY OF EDUCATION   (Taiwan)

Author keywords

Asymmetries; GARCH; Mean absolute percentage errors; Root mean square error; Support vector machine; Volatility

Indexed keywords

ARTIFICIAL INTELLIGENCE TOOLS; ARTIFICIAL NEURAL NETWORK; EMPIRICAL ANALYSIS; EMPIRICAL RESULTS; ESTIMATION MODELS; EVALUATION MODELS; GARCH; GARCH MODELS; INPUT VARIABLES; LEPTOKURTIC DISTRIBUTION; MEAN ABSOLUTE PERCENTAGE ERROR; MODEL ESTIMATION; NON-LINEAR; OPTION PRICE; PREDICTIVE MODELS; PREDICTIVE PERFORMANCE; ROOT MEAN SQUARE ERRORS;

ARTIFICIAL INTELLIGENCE; BLOCK CODES; ERRORS; GEARS; MEAN SQUARE ERROR; MULTILAYER NEURAL NETWORKS; VECTORS;

SUPPORT VECTOR MACHINES;

EID: 77749258538     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/AICI.2009.464     Document Type: Conference Paper

References (13)

1. F. Black and M. S. Scholes, "The Pricing of Options and Corporate Liabilities," Journal of Political Economy, vol. 81, pp. 637-654, 1973.
2. J. M. Hutchinson, A. W. Lo, and T. Poggio, "A Nonparametric Approach to Pricing and Hedging Derivative Securities Via Learning Networks," Journal of Finance, vol. 49, pp. 851-889, 1994.
3. U. Anders, O. Korn, and C. Schmitt, "Improving the pricing of options: a neural network approach," Journal of Forecasting, vol. 17, pp. 369-388, 1998.
4. J. Yao, Y. Li, and C. L. Tan, "Option price forecasting using neural networks," Omega, vol. 28, pp. 455-466, 2000.
5. K.-j. Kim, "Financial time series forecasting using support vector machines," Neurocomputing, vol. 55, pp. 307-319, 2003.
6. R. Engle and T. Bollerslev, "Modelling the persistence of conditional variances," Econometric Reviews, vol. 5, pp. 1-50, 1986.
7. T. Bollerslev, "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, vol. 31, pp. 307-327, 1986.
8. L. R. Glosten, R. Jagannathan, and D. E. Runkle, "On the relation between the expected value and the volatility of the nominal excess return on stocks," Journal of Finance, vol. 48, pp. 1779-1801, 1992.
9. D. B. Nelson, "ARCH models as diffusion approximations," Journal of Econometrics, vol. 45, pp. 7-38, 1990.
10. C. Cortes and V. Vapnik, "Support Vector Networks," Machine Learning, vol. 20, pp. 273-297, 1995.
11. V. Vapnik, The Nature of Statistical Learning Theory, 2 ed.: Springer, 1999.
12. C. Campbell, "Kernel Methods: A Survey of Current Techniques," Neurocomputing, vol. 48, pp. 63-84, 2002.
13. V. Kecman, Learning and Soft Computing: Support Vector Machines, Neural Networks, and Fuzzy Logic Models. Cambridge, Massachusetts, London, England,: MIT Press, 2001.