Deep Learning Application in Rainbow Options

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Bolfake Ali; Mousavi Seyed Nourollah; Mashayekhi Sima

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Journal Paper

Paper Information

Journal: Advances in Mathematical Finance and Applications Year:2023 | Volume:8 | Issue:3 Page(s): 951-963

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Information Journal Paper

Title

Deep Learning Application in Rainbow Options

Author(s)

Bolfake Ali | Mousavi Seyed Nourollah | Mashayekhi Sima | Issue Writer Certificate

Keywords

https://sanad.iau.ir/Journal/amfa/Article/914970

Abstract

Due to the rapid advancements in computer technology, researchers are attracted to solving challenging problems in many different fields. The price of rainbow options is an interesting problem in financial fields and risk management. When there is no closed-form solution to some options, numerical methods must be used. Choosing a suitable numerical method involves the most appropriate combination of criteria for speed, accuracy, simplicity and generality. Monte Carlo simulation methods and traditional numerical methods have expensive repetitive computations and unrealistic assumptions on the model. Deep learning provides an effective and efficient method for options pricing. In this paper, the closed-form formula or Monte-Carlo simulation are used to generate data in European and Asian rainbow option prices for the deep learning model. The results confirm that the deep learning model can price the rainbow options more accurately with less computation time than Monte-Carlo simulation.

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