Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/257666 
Year of Publication: 
2019
Citation: 
[Journal:] International Journal of Financial Studies [ISSN:] 2227-7072 [Volume:] 7 [Issue:] 4 [Article No.:] 68 [Publisher:] MDPI [Place:] Basel [Year:] 2019 [Pages:] 1-12
Publisher: 
MDPI, Basel
Abstract: 
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.
Subjects: 
artificial intelligence
derivatives
genetic algorithm
machine learning
option pricing
reverse engineering
JEL: 
G13
G11
C52
C53
C61
C65
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by Logo
Document Type: 
Article

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