Machine learning and options pricing: A comparison of black-scholes and a deep neural network in pricing and hedging DAX 30 index options

Thumbnail Image

Files

bachelor_Stark_Lauri_2017.pdf (6.97 MB)

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

Rahoituksen laitos

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, Matthijs

Keywords

derivatives pricing, machine learning, neural networks, options pricing, finance, deep learning

Other note

Citation

Permanent link to this item

https://urn.fi/URN:NBN:fi:aalto-201803281865

Collections

[kand] Kauppakorkeakoulu / BIZ (Open Access)