Nested Deep Hedging Mechanism for Multiple Options Hedging [in Japanese]

Masanori HIRANO, Kentaro IMAJO, Kentaro Minami, Takuya SHIMADA

The 28th meeting of Special Interest Group on Financial Informatics of Japanese Society for Artificial Intelligence, pp. 27-34, Mar. 12, 2022

Conference

The 28th meeting of Special Interest Group on Financial Informatics of Japanese Society for Artificial Intelligence (SIG-FIN)

Abstract

Deep Hedging, which uses deep learning and price time-series simulations to optimize option hedging, has recently been in the spotlight because it enables more realistic hedging that can take into account frictions such as transaction fees (imperfect market). However, the situation of hedging an option by other options has never been addressed by deep hedging because of its simulation difficulties. In that situation, pricing for tradable options should also be performed via deep hedging in simulations for realizing imperfect market simulations, which has required unrealizable enormous computational resources because of the nested architecture of deep hedging. Thus, in this study, we proposed a new deep-hedging mechanism for learning hedging strategies under such a nested situation. As a result, we showed better hedging via proposed deep hedging with multiple tradable options.

Keywords

Deep Hedging; Option; Derivative; Simulation; Deep Learning;

Paper

PDF, Official page

doi

10.11517/jsaisigtwo.2022.FIN-028_27

bibtex

@inproceedings{Hirano2022-sigfin28,
  title={{Nested Deep Hedging Mechanism for Multiple Options Hedging [in Japanese]}},
  author={Masanori HIRANO and Kentaro IMAJO and Kentaro Minami and Takuya SHIMADA},
  booktitle={The 28th meeting of Special Interest Group on Financial Informatics of Japanese Society for Artificial Intelligence},
  pages={27-34},
  doi={10.11517/jsaisigtwo.2022.FIN-028_27},
  url={https://sigfin.org/?028-06},
  year={2022}
}