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
کلیدواژهها
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
شیوه ارجاع
Motameni, Mahsa and Mehrdoust, Farshid,1402,Deep learning approach to American option pricing,5th International Conference on Software Computing,Rudsar
ارائهشده در
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