Pricing Options with an Artificial Neural Network: A Reinforcement Learning Approach

Abstract

I develop and present a non-parametric and empirical method for pricing derivative securities. The method involves estimating an artificial neural network. The estimation is based on a time series of the underlying asset price and relies on the no-arbitrage argument. I focus on the pricing of European call options. To assess the feasibility of the method I first apply it on a simulated data set, satisfying the assumptions of the Black-Scholes model. The results show that the method is able to accurately estimate both the option price and its derivatives, based on a two-year sample of the price of the underlying asset. Further, I apply the method on the S&P500 index, with data from 2014 to 2016. I compare the out-of-sample performance of my method to two rival models, the Black-Scholes model and a traditional non-parametric method. The models are evaluated on both pricing and delta-hedging. My method outperforms the Black-Scholes model in this analysis, but is mostly outperformed by the other nonparametric method.