As computers increase their power, machine learning gains an important role in various industries. We consider how to apply this method of analysis and pattern identification to complement extant financial models, specifically option pricing methods. We first prove the discussed model is arbitrage-free to confirm it will yield appropriate results. Next, we apply a neural network algorithm and study its ability to approximate option prices from existing models. The results show great potential for applying machine learning where traditional methods fail. As an example, we study the implied volatility surface of highly liquid stocks using real data, which is computationally intensive, to justify the practical impact of the methods proposed.

• This report represents the work of one or more WPI undergraduate students submitted to the faculty as evidence of completion of a degree requirement. WPI routinely publishes these reports on its website without editorial or peer review.

Creator

• Phongsopa, Pavee
• Wotton, Nicholas B.
• Mendonca Filho, Vital Tavares B M

Publisher

• Worcester Polytechnic Institute

Identifier

• E-project-050120-154222

Advisor

• Wang, Gu
• Li, Yanhua
• Song, Qingshuo

Year

• 2020

Date created

• 2020-05-01

Resource type

• Major Qualifying Project

Major

• Mathematical Sciences

Rights statement

• In Copyright