Artificial Neural Networks Performance in WIG20 Index Options Pricing.

Maciej Wysocki,Robert Ślepaczuk

doi:10.3390/e24010035

Abstract

In this paper, the performance of artificial neural networks in option pricing was analyzed and compared with the results obtained from the Black–Scholes–Merton model, based on the historical volatility. The results were compared based on various error metrics calculated separately between three moneyness ratios. The market data-driven approach was taken to train and test the neural network on the real-world options data from 2009 to 2019, quoted on the Warsaw Stock Exchange. The artificial neural network did not provide more accurate option prices, even though its hyperparameters were properly tuned. The Black–Scholes–Merton model turned out to be more precise and robust to various market conditions. In addition, the bias of the forecasts obtained from the neural network differed significantly between moneyness states. This study provides an initial insight into the application of deep learning methods to pricing options in emerging markets with low liquidity and high volatility.

Highlights

The history of neural networks (NNs) started in the early 1940s, when McCulloch andPitts [1] proposed the first computational model for NNs
The performance of NNs has already been described in different papers, most of them focused on simulated markets or data from the New York Stock Exchange, with the approach of boosting the performance of the Black–Scholes–Merton model (BSM)
As this paper aimed to compare the performance of a BSM model and NNs in pricing options, the Machine learning (ML) model was chosen to be introduced to the same data as that used in the BSM model

Summary

Introduction

The history of neural networks (NNs) started in the early 1940s, when McCulloch andPitts [1] proposed the first computational model for NNs. The history of neural networks (NNs) started in the early 1940s, when McCulloch and. The popularity of NNs started to grow in 1974, when Werbos [2] published his work about the backpropagation algorithm that enabled the operational training of models. Machine learning (ML) techniques have been broadly used in finance in many different applications, such as forecasting stock price movements, pricing derivatives, the preventing credit frauds. The performance of NNs has already been described in different papers, most of them focused on simulated markets or data from the New York Stock Exchange, with the approach of boosting the performance of the Black–Scholes–Merton model (BSM). The main aims of this paper were the exploration of deep learning possibilities in option pricing and the analysis of the market data-driven approach for NNs training for a developing market. None of the previous works have covered the topic of the machine learning (ML)

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Conclusion