European Option Pricing by Using the Support Vector Regression Approach

  • Panayiotis C. Andreou
  • Chris Charalambous
  • Spiros H. Martzoukos
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5768)


We explore the pricing performance of Support Vector Regression for pricing S&P 500 index call options. Support Vector Regression is a novel nonparametric methodology that has been developed in the context of statistical learning theory, and until now it has not been widely used in financial econometric applications. This new method is compared with the Black and Scholes (1973) option pricing model, using standard implied parameters and parameters derived via the Deterministic Volatility Functions approach. The empirical analysis has shown promising results for the Support Vector Regression models.


Option pricing implied volatility non-parametric methods support vector regression 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Ait-Sahalia, Y., Lo, W.A.: Nonparametric estimation of state-price densities implicit in financial asset prices. Journal of Finance 53, 499–547 (1998)CrossRefGoogle Scholar
  2. 2.
    Andreou, P.C., Charalambous, C., Martzoukos, S.H.: Pricing and trading european options by combining artificial neural networks and parametric models with implied parameters. European Journal of Operational Research 185, 1415–1433 (2008)MathSciNetCrossRefzbMATHGoogle Scholar
  3. 3.
    Bakshi, G., Cao, C., Chen, Z.: Empirical performance of alternative options pricing models. Journal of Finance 52, 2003–2049 (1997)CrossRefGoogle Scholar
  4. 4.
    Bates, D.S.: Post-’87 crash fears in the s&p 500 futures option market. Journal of Econometrics 94, 181–238 (2000)MathSciNetCrossRefzbMATHGoogle Scholar
  5. 5.
    Black, F., Scholes, M.: The pricing of options and corporate liabilities. Journal of Political Economy 81, 637–654 (1973)MathSciNetCrossRefzbMATHGoogle Scholar
  6. 6.
    Schittenkopf, C., Dorffner, G.: Risk-neutral density extraction from option prices: Improved pricing with mixture density networks. IEEE Transactions on Neural Networks 12, 716–725 (2001)CrossRefGoogle Scholar
  7. 7.
    Cao, L.J., Tay, F.E.H.: Support vector machine with adaptive parameters in financial time series forecasting. IEEE Transactions on Neural Networks 14, 1506–1518 (2003)CrossRefGoogle Scholar
  8. 8.
    Chernov, M., Ghysels, E.: Towards a unified approach to the joint estimation objective and risk neutral measures for the purpose of option valuation. Journal of Financial Economics 56, 407–458 (2000)CrossRefGoogle Scholar
  9. 9.
    Christoffersen, P., Jacobs, K.: The importance of the loss function in option valuation. Journal of Financial Economics 72, 291–318 (2004)CrossRefGoogle Scholar
  10. 10.
    Dumas, B., Fleming, J., Whaley, R.: Implied volatility functions: Empirical tests. Journal of Finance 53, 2059–2106 (1998)CrossRefGoogle Scholar
  11. 11.
    Garcia, R., Gencay, R.: Pricing and hedging derivative securities with neural networks and a homogeneity hint. Journal of Econometrics 94, 93–115 (2000)CrossRefzbMATHGoogle Scholar
  12. 12.
    Gestel, T.V., Suykens, J.A.K., Baestaens, D.E., Lambrecthts, A., Lanckriet, G., Vandaele, B., Moor, B.D., Vandewalle, J.: Financial time series prediction using least squares support vector machines within the evidence framework. IEEE Transactions on Neural Networks 12, 809–821 (2001)CrossRefGoogle Scholar
  13. 13.
    Hull, J.C.: Option, Futures and Other Derivatives. Pearson Prentice Hall (2008)Google Scholar
  14. 14.
    Hutchinson, J.M., Lo, A.W., Poggio, T.: A nonparametric approach to pricing and hedging derivative securities via learning networks. Journal of Finance 49, 851–889 (1994)CrossRefGoogle Scholar
  15. 15.
    Jackwerth, J.C.: Recovering risk aversion from option prices and realized returns. The Review of Financial Studies 12, 433–451 (2000)CrossRefGoogle Scholar
  16. 16.
    Johnson, N.J.: Modified t-test and confidence intervals for asymmetrical populations. Journal of the American Statistical Association 73, 536–544 (1978)MathSciNetzbMATHGoogle Scholar
  17. 17.
    Müller, K., Smola, A., Ratsch, G., Schölkoph, B., Kohlmorgen, J., Vapnik, V.: Using Support Vector Machines for Time Series Prediction. In: Advances in Kernel Methods: Support Vector Machines. MIT Press, Cambridge (1999)Google Scholar
  18. 18.
    Lajbcygier, P.: Improving option pricing with the product constrained hybrid neural network. IEEE Transactions on Neural Networks 15, 465–476 (2004)CrossRefGoogle Scholar
  19. 19.
    Rubinstein, M.: Implied binomial trees. The Journal of Finance 49, 771–818 (1994)CrossRefGoogle Scholar
  20. 20.
    Smola, A., Scholkoph, B.: A tutorial on support vector regression. Technical report, Royal Holloway College, University of London, UK (1998); NeuroCOLT Technical Report, NC-TR-98-030, Royal Holloway College, University of London, UKGoogle Scholar
  21. 21.
    Suykens, J.A.K., Van Gestel, T., De Brabanter, J., De Moor, B., Vandewaller, J.: Least Squares Support Vector Machines. World Scientific Publishing, Singapore (2002)CrossRefzbMATHGoogle Scholar
  22. 22.
    Trafalis, T.B., Ince, H., Mishina, T.: Support vector regression in option pricing. In: Proceedings of Conference on Computational Intelligence and Financial Engineering. CIFEr 2003, Hong Kong, China (2003)Google Scholar
  23. 23.
    Vapnik, V.: Statistical Learning Theory. John Wiley and Sons, New York (1998)zbMATHGoogle Scholar
  24. 24.
    Whaley, R.E.: Valuation of american call options on dividend-paying stocks. Journal of Financial Economics 10, 29–58 (1982)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Panayiotis C. Andreou
    • 1
  • Chris Charalambous
    • 2
  • Spiros H. Martzoukos
    • 2
  1. 1.Durham UniversityUK
  2. 2.University of CyprusCyprus

Personalised recommendations