The DOI matches its Crossref record; 14 of the 14 references it cites were found in a registry or an index.
Deep Hedging: Hedging Derivatives Under Generic Market Frictions Using Reinforcement Learning
Open accessAbstract
This article discusses a new application of reinforcement learning: to the problem of hedging a portfolio of “over-the-counter” derivatives under under market frictions such as trading costs and liquidity constraints. It is an extended version of our recent work https://www.ssrn.com/abstract=3120710, here using notation more common in the machine learning literature. The objective is to maximize a non-linear risk-adjusted return function by trading in liquid hedging instruments such as equities or listed options. The approach presented here is the first efficient and model-independent algorithm which can be used for such problems at scale. There are now some code examples on GitHub.
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In the citing paper
“DRL framework is powerful in solving dynamic decision making problems by learning through interaction with an unknown environment, and thus providing two major advantages - portfolio scalability and market model independence [5].”
From FinRL: A Deep Reinforcement Learning Library for Automated Stock Trading in Quantitative Finance (2020) · cites this paper as [5]
In the citing paper
“FinRL supports user-defined reward functions to reflect risk-aversion or market friction [6, 54] and provides commonly used ones [13] as follows:”
From FinRL: Deep Reinforcement Learning Framework to Automate Trading in Quantitative Finance (2021) · cites this paper as [6]
In the citing paper
“DRL algorithms are powerful in solving dynamic decision making problems by learning through interactions with an unknown environment, and offer two major advantages of portfolio scalability and market model independence [6].”
From FinRL: Deep Reinforcement Learning Framework to Automate Trading in Quantitative Finance (2021) · cites this paper as [6]
In the citing paper
“DRL algorithms are powerful in solving dynamic decision making problems by learning through interactions with an unknown environment, and offer two major advantages of portfolio scalability and market model independence [6].”
From FinRL: Deep Reinforcement Learning Framework to Automate Trading in Quantitative Finance (2021) · cites this paper as [6]
Citations
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Research works printed but not linked: 6
- Hans Buehler, 2018, Deep hedging. arxiv
- Xin Du, 2009, Algorithm trading using Q-learning and recurrent reinforcement learning. arxiv
- Igor Halperin, 2017, QLBS: Q-learner in the Black-Scholes (-Merton) worlds. arxiv
- Francis Longstaff, 2001, Valuing american options by simulation: A simple least-squares approach. The Review of Financial Studies, 14, 113
- David Lu, 2017, Agent inspired trading using recurrent reinforcement learning and LSTM neural networks. arxiv
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Metadata
- Authors
- Buehler, H., Gonon, L., Teichmann, J., Wood, B., Mohan, B., & Kochems, J.
- Affiliations
- Ludwig-Maximilians-Universität München (DE)
- ETH Zurich (CH)
- JPMorgan Chase & Co (United States) (US)
- Corresponding author
- Buehler, Hans
- Venue
- SSRN Electronic Journal
- Publisher
- Elsevier BV
- Published
- 2019
- ISSN
- 1556-5068 (ISSN-L)
- OpenAlex
- W2941037157
- Cited by
- 48
- References
- 14
- Citations by year
- 2026: 1 · 2025: 6 · 2024: 6 · 2023: 13 · 2022: 2 · 2021: 13 · 2020: 4 · 2019: 3
- Type
- posted content
- Language
- en
- Open access
- green
- doi.org
- Locations
- SSRN Electronic Journal (acceptedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Swiss Finance Institute Research Paper Series (submittedVersion)
- Alexandria (UniSG) (University of St.Gallen) (submittedVersion)
- Topics
- Social Sciences › Economics, Econometrics and Finance › Finance › Stochastic processes and financial applications
- Social Sciences › Decision Sciences › Management Science and Operations Research › Risk and Portfolio Optimization
- Social Sciences › Economics, Econometrics and Finance › Finance › Financial Markets and Investment Strategies
- Keywords
- deep hedging, derivative hedging, over-the-counter derivatives, market frictions, trade costs, liquidity constraints, reinforcement learning, risk-adjusted returns, options
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