Capturing implied volatility with neural nets as a basis for options pricing and delta hedging

摘要

A neural network is used to learn from data the Black-Scholes implied volatility. The implied volatility forecasts, generated from the Neural Net, are converted to option price using the Black-Scholes formula. This approach to option pricing capabilities are shown to be superior to the Black-Scholes and the GARCH option-pricing model. The neural network has also shown that it is able to reproduce the implied volatility well into the future whereas the GARCH option-pricing model shows deterioration in the implied volatility with time. A new Method for Delta Hedging using this approach is also presented.