A hybrid option pricing model using a neural network for estimating volatility

The Black-Scholes model is the standard approach used for pricing financial options. However, although being theoretically strong, option prices valued by the model often differ from the prices observed in the financial markets. This paper applies a hybrid neural network which preprocesses financial input data for improving the estimation of option market prices. This model is comprised of two parts. The first part is a neural network developed to estimate volatility. The second part is an additional neural network developed to value the difference between the Black-Scholes model results and the actual market option prices. The resulting option price is then a summation between the Black-Scholes model and the network response. The hybrid system with a neural network for estimating volatility provides better performance in terms of pricing accuracy than either the Black-Scholes model with historical volatility, or the Black-Scholes model with volatility valued by the neural network.