Critical Assessment of Option Pricing Methods Using Artificial Neural Networks
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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.
KeywordsOption Price Hide Neuron Implied Volatility Spot Price Price Error
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