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Deep Learning for Option Pricing Under Heston and Bates Models | ||
| Journal of Mathematics and Modeling in Finance | ||
| مقالات آماده انتشار، پذیرفته شده، انتشار آنلاین از تاریخ 11 مرداد 1402 | ||
| نوع مقاله: Research Article | ||
| شناسه دیجیتال (DOI): 10.22054/jmmf.2023.73263.1085 | ||
| نویسندگان | ||
| Ali Bolfake1؛ Seyed Nourollah Mousavi* 2؛ Sima Mashayekhi2 | ||
| 1Department of mathematics, Faculty of Sciences, Arak University, arak, iran | ||
| 2Department of Mathematics, Faculty of Sciences, Arak University, Arak, Iran | ||
| چکیده | ||
| 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. | ||
| کلیدواژه‌ها | ||
| Option pricing؛ Heston model؛ Bates model؛ Deep learning؛ Monte Carlo simulation؛ Variance reduction technique | ||
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آمار تعداد مشاهده مقاله: 60 |
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