Deep learning for option pricing under Heston and Bates models
Publish Year: 1402
نوع سند: مقاله ژورنالی
زبان: English
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شناسه ملی سند علمی:
JR_JMMF-3-1_004
تاریخ نمایه سازی: 7 آبان 1402
Abstract:
This paper proposes a new approach to pricing European options using deep learning techniques under the Heston and Bates models of random fluctuations. The deep learning network is trained with eight input hyper-parameters and three hidden layers, and evaluated using mean squared error, correlation coefficient, coefficient of determination, and computation time. The generation of data was accomplished through the use of Monte Carlo simulation, employing variance reduction techniques. The results demonstrate that deep learning is an accurate and efficient tool for option pricing, particularly under challenging pricing models like Heston and Bates, which lack a closed-form solution. These findings highlight the potential of deep learning as a valuable tool for option pricing in financial markets.
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Authors
Ali Bolfake
Department of mathematics, Faculty of Sciences, Arak University, arak, iran
Seyed Nourollah Mousavi
Department of Mathematics, Faculty of Sciences, Arak University, Arak, Iran
Sima Mashayekhi
Department of Mathematics, Faculty of Sciences, Arak University, Arak, Iran
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