Zobrazeno 1 - 10
of 14
pro vyhledávání: '"Yaser Faghan"'
Autor:
Amirhosein Mosavi, Yaser Faghan, Pedram Ghamisi, Puhong Duan, Sina Faizollahzadeh Ardabili, Ely Salwana, Shahab S. Band
Publikováno v:
Mathematics, Vol 8, Iss 10, p 1640 (2020)
The popularity of deep reinforcement learning (DRL) applications in economics has increased exponentially. DRL, through a wide range of capabilities from reinforcement learning (RL) to deep learning (DL), offers vast opportunities for handling sophis
Externí odkaz:
https://doaj.org/article/3e4bfc74ee004d0eb36fcf6ee4fcd72d
In this paper we investigate a nonlinear generalization of the Black-Scholes equation for pricing American style call options in which the volatility term may depend on the underlying asset price and the Gamma of the option. We propose a numerical me
Externí odkaz:
http://arxiv.org/abs/1707.00358
We analyze and calculate the early exercise boundary for a class of stationary generalized Black-Scholes equations in which the volatility function depends on the second derivative of the option price itself. A motivation for studying the nonlinear B
Externí odkaz:
http://arxiv.org/abs/1707.00356
Publikováno v:
Optical Memory and Neural Networks. 31:46-58
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::b26e2c438a4e12d30a0717d76edcd081
https://doi.org/10.3103/s1060992x22010088
https://doi.org/10.3103/s1060992x22010088
Autor:
Pedram Ghamisi, Yaser Faghan, Puhong Duan, Sina Ardabili, Shahab S. Band, Ely Salwana, Amirhosein Mosavi
Publikováno v:
Mathematics
Mathematics, Vol 8, Iss 1640, p 1640 (2020)
Mathematics, Vol 8, Iss 1640, p 1640 (2020)
The popularity of deep reinforcement learning (DRL) applications in economics has increased exponentially. DRL, through a wide range of capabilities from reinforcement learning (RL) to deep learning (DL), offers vast opportunities for handling sophis
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::11ded58b71e0f0da7ab0013ddb6d024d
https://doi.org/10.31219/osf.io/jrc58
https://doi.org/10.31219/osf.io/jrc58
The popularity of deep reinforcement learning (DRL) methods in economics have been exponentially increased. DRL through a wide range of capabilities from reinforcement learning (RL) and deep learning (DL) for handling sophisticated dynamic business e
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::881317225c89774babcc3e37580a72cf
http://arxiv.org/abs/2004.01509
http://arxiv.org/abs/2004.01509
Publikováno v:
The Journal of Computational Finance.
In this paper we analyze a nonlinear generalization of the Black-Scholes equation for pricing American style call option in which the volatility may depend on the underlying asset price and the Gamma of the option. We propose a novel method of pricin
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::96b63f9fc47c331ce700d0fddcd46225
https://doi.org/10.21314/jcf.2020.379
https://doi.org/10.21314/jcf.2020.379
Akademický článek
Tento výsledek nelze pro nepřihlášené uživatele zobrazit.
K zobrazení výsledku je třeba se přihlásit.
K zobrazení výsledku je třeba se přihlásit.
We investigate qualitative and quantitative behavior of a solution to the problem of pricing American style of perpetual put options. We assume the option price is a solution to a stationary generalized Black-Scholes equation in which the volatility
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=od______1503::3ce9fb104bea788561fe52dab8a6b370
https://hdl.handle.net/10400.5/16343
https://hdl.handle.net/10400.5/16343
Publikováno v:
Novel Methods in Computational Finance ISBN: 9783319612812
We analyze and calculate the early exercise boundary for a class of stationary generalized Black- Scholes equations in which the volatility function depends on the second derivative of the option price itself. A motivation for studying the nonlinear
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::28f69b59909fa17891f419046a6a28be
https://doi.org/10.1007/978-3-319-61282-9_8
https://doi.org/10.1007/978-3-319-61282-9_8