SigFormer: Signature Transformers for Deep Hedging-Reference-Cited by-同舟云学术

SigFormer: Signature Transformers for Deep Hedging

Published:2023-11-25 Issue: Volume: Page:
ISSN:
Container-title:4th ACM International Conference on AI in Finance
language:
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Author:

Tong Anh¹^ORCID,Nguyen-Tang Thanh²^ORCID,Lee Dongeun³^ORCID,Tran Toan M⁴^ORCID,Choi Jaesik⁵^ORCID

Affiliation:

1. Graduate School of AI, KAIST, KR

2. Johns Hopkins University, US

3. Texas A&M University-Commerce, US

4. VinAI Research, VN

5. Graduate School of AI, KAIST/INEEJI, KR

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3604237.3626841

Reference53 articles.

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3. Fazl Barez Paul Bilokon Arthur Gervais and Nikita Lisitsyn. 2023. Exploring the Advantages of Transformers for High-Frequency Trading. arxiv:2302.13850 [q-fin.ST] Fazl Barez Paul Bilokon Arthur Gervais and Nikita Lisitsyn. 2023. Exploring the Advantages of Transformers for High-Frequency Trading. arxiv:2302.13850 [q-fin.ST]

4. Pricing under rough volatility

5. Christian Bayer and Benjamin Stemper. 2018. Deep calibration of rough stochastic volatility models. arxiv:1810.03399 [q-fin.PR] Christian Bayer and Benjamin Stemper. 2018. Deep calibration of rough stochastic volatility models. arxiv:1810.03399 [q-fin.PR]