Critical Assessment of Option Pricing Methods Using Artificial Neural Networks

  • Panayiotis Ch. Andreou
  • Chris Charalambous
  • Spiros H. Martzoukos
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2415)


In this paper we compare the predictive ability of the Black-Scholes Formula (BSF) and Artificial Neural Networks (ANNs) to price call options by exploiting historical volatility measures. We use daily data for the S&P 500 European call options and the underlying asset and furthermore, we employ nonlinearly interpolated risk-free interest rate from the Federal Reserve board for the period 1998 to 2000. Using the best models in each sub-period tested, our preliminary results demon strate that by using historical measures of volatility, ANNs outperform the BSF. In addition, the ANNs performance improves even more when a hybrid ANN model is utilized. Our results are significant and differ from previous literature. Finally, we are currently extending the research in order to: a) incorporate appropriate implied volatility per contract with the BSF and ANNs and b) investigate the applicability of the models using trading strategies.


Option Price Hide Neuron Implied Volatility Spot Price Price Error 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Rubinstein, M.: Nonparametric tests of alternative option pricing models using all reported trades and quotes on the 30 most active CBOE option classes from August 23, 1976 through August 31, 1978. Journal of Finance, Vol. XL (1985) 455–480.CrossRefGoogle Scholar
  2. 2.
    Bakshi, G., Cao, C., Chen, Z.: Empirical performance of alternative options pricing models. Journal of Finance 57 (1997) 2003–2049.CrossRefGoogle Scholar
  3. 3.
    Hutchison, J. M., Lo, A. W., Poggio, T.: A nonparametric approach to pricing and hedging derivative securities via learning networks. Journal of Finance, Vol. 49, No. 3 (1994) 851–889.CrossRefGoogle Scholar
  4. 4.
    Lajbcygier, P., Flitman, A., Swan, A., Hyndman, R.: The pricing and trading of o-ptions using a hybrid neural network model with historical volatility. Neurovest Journal, Vol. 5, No. 1 (1997) 27–41.Google Scholar
  5. 5.
    Yao, J., Li, Y., Tan, C. L.: Option price forecasting using neural networks. The International Journal of Management Science, Vol. 28 (2000) 455–466.Google Scholar
  6. 6.
    Lajbcygier, P.: Improving option pricing with the product constrained hybrid neural network. Working paper, Monash University, (2001) Australia.Google Scholar
  7. 7.
    Watson, P., Gupta, K.C.: EM-ANN models for microstript vias and interconnected in multi-layer circuits. IEEE Trans. Microwave Theory and Techniques (1996) 2495–2503.Google Scholar
  8. 8.
    Cybenko, G.: Approximation by superpositions of a sigmoidal function. Mathematics of Control, Signal and Systems 2 (1989) 303–314.zbMATHCrossRefMathSciNetGoogle Scholar
  9. 9.
    Hagan, M., Demuth, H., Beale, M.: Neural Network Design, PWS Publishing Company (1996).Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  • Panayiotis Ch. Andreou
    • 1
  • Chris Charalambous
    • 1
  • Spiros H. Martzoukos
    • 1
  1. 1.Department of Public and Business AdministrationUniversity of CyprusLefkosiaCyprus

Personalised recommendations