Multiple kernel support vector regression for pricing nifty option


  • Neetu Verma MANIT Bhopal, MPINDIA
  • Sujoy Das
  • Namita Srivastava





Multiple Kernel Learning, Moneyness, Option Pricing, Support Vector Regression.


The goal of present experiments is to investigate the use of multiple kernel learning as a tool for pricing options in the context of Indian stock market for Nifty index options. In this paper, fair price of an option is predicted by Multiple Kernel Support Vector Regression (MKLSVR) using linear combinations of kernels and Single Kernel Support Vector Regression (SKSVR). Prices of option highly depend on different money market conditions like deep-in-the-money, in-the-money, at-the-money, out-of-money and deep-out-of-money condition. The experimental study attempts to identify the forecasting errors with the help of mean square error; root meant square error, and normalized root meant square error between the market option prices and the calculated option prices by model for all market conditions. The results reflect that multiple kernel support vector regression performed fairly well in comparison to support vector regression with single kernel.


[1] J. C. Hull, Options, futures, and other derivatives, Prentice-Hall (1997).

[2] Hutchinson, “A nonparametric approach to pricing and hedging derivative securities via learning networksâ€, Journal of Finance, Vol.49, No.3, (1998), pp.851-889.

[3] Lajbcygier, Paul R., and Jerome T. Connor. "Improved option pricing using artificial neural networks and bootstrap methods." International journal of neural systems, Vol.8, No.4, 8.04 (1997), pp.457-471.

[4] Yao. J., Li. Y., and Tan. C. L., “Option price forecasting using neural networksâ€, Omega, Vol.28, No.4, (2000), pp.455-466.

[5] Andreou, Panayiotis C., Chris Charalambous, and Spiros H. Martzoukos, “Pricing and trading European options by combining artificial neural networks and parametric models with implied parametersâ€, European Journal of Operational Research, Elsevier, Vol.185,No.3,(2008), pp.1415-1433.

[6] Anant Saxena, “Valuation of S&P CNX Nifty Options: Comparison of Black-Scholes and Hybrid ANN Modeâ€, SAS Global Forum, 162, (2008).

[7] S. K. Mitra, “An Option Pricing Model That Combines Neural Network Approach and Black Scholes Formulaâ€, Global Journals Inc. (USA), Vol.12, No.4, (2012), pp.6-16. (1998).

[8] Vapnik, V. N., the Nature of Statistical Learning Theory, Springer, New York. John Wiley & Sons, (1995).

[9] Abe, Shigeo. Support vector machines for pattern classification. Vol. 2. London: Springer, (2005).

[10] Cherkassky, V., &Mulier, F., Learning from data: Concepts, theory, and methods, New York: Wiley, (2007).

[11] Vapnik, V., Golowich, S., & Smola, “Support vector method for function approximation, regression estimation and signal processingâ€, Advances in Neural Information Processing Systems, Vol.9, (1997), pp.281-287.

[12] Tay, F. E. H. and Cao, L., “Application of support vector machines in financial time-series forecastingâ€, Omega, Vol.29, No.4, (2001), pp.309–317.

[13] Tay, F. E. H. and Cao, L., “Modified support vector machines in financial time series forecastingâ€, Neurocomputing, Vol.48, No.1, (2002), pp.847-861.

[14] Kyoung- jae Kim, “Financial time series forecasting using support vector machines†Neurocomputing, Vol.55, (2003), pp.307 – 319.

[15] Lijuan Cao, “Support vector machines experts for time series forecastingâ€, Neurocomputing, Vol.51, (2003), pp.321 – 339.

[16] C. Y. Yeh, C. W. Huang, and S. J. Lee, “A multiple-kernel support vector regression approach for stock market price forecasting,†Expert Systems with Applications, Vol. 38, No. 3, (2011),pp. 2177–2186.

[17] A. Rakotomamonjy, F. R. Bach, S. Canu, and Y. Grandvalet. “Simple multiple kernel learning†[J]. J. Mach.Learn. Res., Vol.9, (2008), pp.2491-2521.

[18] Gönen, Mehmet, and EthemAlpaydın. “Multiple kernel learning algorithms†The Journal of Machine Learning Research, Vol.12, (2011), pp.2211-2268.

[19] Lanckriet, G. R. G., Cristianini, N., Bartlett, P., Ghaoui, L. E., & Jordan, M. I. “Learning the kernel matrix with semidefinite programmingâ€. Journal of Machine Learning Research, Vol.5, (2004), pp.27–72.

[20] Bach, F. R., Lanckriet, G. R. G., & Jordan, M. I. “Multiple kernel learning, conic duality, and the SMO algorithmâ€. In: Proceedings of the 21th international conference on machine learning, (2004), pp. 6–13.

[21] Fung, G., Dundar, M., Bi, J., & Rao, B. “A fast iterative algorithm for fisher discriminant using heterogeneous kernelsâ€. In Proceedings of the 21st international conference on machine learning, (2004), p. 40.

[22] Kim, S., Magnani, A., & Boyd, S. “Optimal kernel selection in kernel fisher discriminant analysisâ€. In Proceedings of the 23rd international conference on machine learning, (2006), pp. 465–472.

[23] Xiang-rong, Zhang, H. U. Long-ying, and Wang Zhi-sheng. “Multiple kernel support vector regression for economic forecastingâ€. Management Science and Engineering (ICMSE), International Conference on. IEEE, (2010), pp. 129-134.

[24] Cherkassky, Vladimir, and Yunqian Ma. 2004, “Practical selection of SVM parameters and noise estimation for SVM regressionâ€. Neural networks, Vol.17, No.1, pp.113-126.

[25] National stock exchange of India ltd. Accessed: January 2014.

[26] Verma, Neetu, Namita Srivastava, and Sujoy Das. “Forecasting the Price of Call Option Using Support Vector Regressionâ€. (2014) IOSR Journal of Mathematics, Vol. 10, No.6, pp.38-43.

View Full Article: