Comparison of the Effectiveness of Option Price Forecasting: Black-Scholes vs. Simple and Hybrid Neural Networks

Abstract

The purpose of this study is to forecast option prices with simple backpropagation neural networks and to compare the results between conventional Black-Scholes model, the Black-Scholes model with pure implied volatility and neural network models over a seven-year period. This longitudinal study used 64,280 OEX 100 index call option prices trading on the Chicago Board Options Exchange from January 1986 to June 1993. In addition to simple models, two hybrid models were constructed. Using optimal models in each sub-period, the following results are demonstrated: 1. neural networks outperform the conventional Black-Scholes model when using historical volatility as an input; 2. the Black-Scholes model has better predictability when implied volatility is used; and 3. the hybrid neural network model with implied volatility often outperforms the implied volatility version of the Black-Scholes model.

Keywords: Neural networks, Option pricing, Implied volatility

JEL Classification: C13, C32, C45, C53, G10, G13