Previous Article in Journal
Utility Exchange Traded Fund Performance Evaluation. A Comparative Approach Using Grey Relational Analysis and Data Envelopment Analysis Modelling
Open AccessArticle

Reverse Engineering of Option Pricing: An AI Application

by Bodo Herzog 1,2,* and Sufyan Osamah 1
1
ESB Business School, Reutlingen University, Alteburgstr. 150, 72762 Reutlingen, Germany
2
RRI Reutlingen Research Institute, 72762 Reutlingen, Germany
*
Author to whom correspondence should be addressed.
Int. J. Financial Stud. 2019, 7(4), 68; https://doi.org/10.3390/ijfs7040068
Received: 30 August 2019 / Revised: 13 October 2019 / Accepted: 15 October 2019 / Published: 6 November 2019
This paper studies option pricing based on a reverse engineering (RE) approach. We utilize artificial intelligence in order to numerically compute the prices of options. The data consist of more than 5000 call- and put-options from the German stock market. First, we find that option pricing under reverse engineering obtains a smaller root mean square error to market prices. Second, we show that the reverse engineering model is reliant on training data. In general, the novel idea of reverse engineering is a rewarding direction for future research. It circumvents the limitations of finance theory, among others strong assumptions and numerical approximations under the Black–Scholes model. View Full-Text
Keywords: reverse engineering; option pricing; derivatives; genetic algorithm; artificial intelligence; machine learning reverse engineering; option pricing; derivatives; genetic algorithm; artificial intelligence; machine learning
MDPI and ACS Style

Herzog, B.; Osamah, S. Reverse Engineering of Option Pricing: An AI Application. Int. J. Financial Stud. 2019, 7, 68.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop