Efficient pricing and hedging of high-dimensional American options using deep recurrent networks
Year of publication: |
2023
|
---|---|
Authors: | Na, Andrew S. ; Wan, Justin W. L. |
Published in: |
Quantitative finance. - London : Taylor & Francis, ISSN 1469-7696, ZDB-ID 2027557-2. - Vol. 23.2023, 4, p. 631-651
|
Subject: | American option pricing | Deep recurrent neural networks | Delta hedging | Stochastic differential equations | Experiment | Hedging | Optionspreistheorie | Option pricing theory | Neuronale Netze | Neural networks | Optionsgeschäft | Option trading | Stochastischer Prozess | Stochastic process | Analysis | Mathematical analysis | Black-Scholes-Modell | Black-Scholes model |
-
Chen, Yangang, (2021)
-
A second-order discretization with Malliavin weight and Quasi-Monte Carlo method for option pricing
Yamada, Toshihiro, (2020)
-
Pricing barrier options with deep backward stochastic differential equation methods
Ganesan, Narayan, (2022)
- More ...
-
Robust numerical valuation of European and American options under the CGMY process
Wang, Iris R., (2007)
-
Low-Bias Simulation Scheme for the Heston Model by Inverse Gaussian Approximation
Tse, Shu Tong, (2010)
-
Chen, Yangang, (2021)
- More ...