Deep learning for option pricing under Heston and Bates models
عنوان مقاله: Deep learning for option pricing under Heston and Bates models
شناسه ملی مقاله: JR_JMMF-3-1_004
منتشر شده در در سال 1402
شناسه ملی مقاله: JR_JMMF-3-1_004
منتشر شده در در سال 1402
مشخصات نویسندگان مقاله:
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
خلاصه مقاله:
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
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.
کلمات کلیدی: Option pricing, Heston Model, Bates model, Deep Learning, Monte Carlo simulation, Variance reduction technique
صفحه اختصاصی مقاله و دریافت فایل کامل: https://civilica.com/doc/1796973/