Deep learning approach to American option pricing
محل انتشار: پنجمین کنفرانس بین المللی محاسبات نرم
سال انتشار: 1402
نوع سند: مقاله کنفرانسی
زبان: انگلیسی
مشاهده: 25
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شناسه ملی سند علمی:
CSCG05_085
تاریخ نمایه سازی: 9 اردیبهشت 1403
چکیده مقاله:
This study focuses on pricing the American put option by applying a deep learning-based algorithm under the double Heston model. The double Heston model is a multi-factor stochastic volatility model that offers more flexibility in modeling the volatility term structure and better empirical fit to option prices compared to one-factor models. The option price derivation under this model leads to a linear complementarity problem. To solve this problem, we utilize the deep Galerkin method (DGM), which is a method based on deep learning. Our numerical results show the efficiency and accuracy of the algorithm as evidenced by comparing it with the antithetic variable Least-square Monte Carlo (AV-LSM) method.
کلیدواژه ها:
نویسندگان
Mahsa Motameni
Department of Applied Mathematics, Faculty of Mathematical Sciences University of Guilan, P.O. Box: ۴۱۹۳۸-۱۹۱۴, Rasht, Iran
Farshid Mehrdoust
Department of Applied Mathematics, Faculty of Mathematical Sciences University of Guilan, P.O. Box: ۴۱۹۳۸-۱۹۱۴, Rasht, Iran