Machine Learning and Options Pricing: a Comparison of Black-Scholes and a Deep Neural Network in Pricing and Hedging DAX 30 Index Options
Loading...
URL
Journal Title
Journal ISSN
Volume Title
School of Business |
Bachelor's thesis
Unless otherwise stated, all rights belong to the author. You may download, display and print this publication for Your own personal use. Commercial use is prohibited.
Authors
Date
2017
Department
Major/Subject
Mcode
Degree programme
Rahoitus
Language
en
Pages
19
Series
Abstract
In this paper I study whether a deep feedforward network model performs better than the Black- Scholes model in pricing and delta hedging European-style call options. I apply the methodologies from selected original works on daily prices of call options written on DAX 30 between years 2013 and 2017. My results are mostly consistent with earlier literature and they indicate that the out-of- sample pricing performance of the neural network is superior to Black-Scholes with medium-term and long-term maturities and the out-of-sample delta-hedging performance of the neural network is superior with out-of-the-money options.Description
Thesis advisor
Lof, MatthijsKeywords
derivatives pricing, machine learning, neural networks, options pricing, finance, deep learning