Abstract
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.
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Andreou, P.C., Charalambous, C., Martzoukos, S.H. (2002). Critical Assessment of Option Pricing Methods Using Artificial Neural Networks. In: Dorronsoro, J.R. (eds) Artificial Neural Networks — ICANN 2002. ICANN 2002. Lecture Notes in Computer Science, vol 2415. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-46084-5_183
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DOI: https://doi.org/10.1007/3-540-46084-5_183
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