Deep Reinforcement Learning for Market Making in Corporate Bonds: Beating the Curse of Dimensionality

DOI10.1080/1350486X.2020.1714455zbMath1433.91194arXiv1910.13205OpenAlexW3005158858MaRDI QIDQ5217496

Iuliia Manziuk, Olivier Guéant

Publication date: 24 February 2020

Published in: Applied Mathematical Finance (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1910.13205

zbMATH Keywords

stochastic optimal controlreinforcement learningactor-critic algorithmsmarket making

Mathematics Subject Classification ID

Numerical methods (including Monte Carlo methods) (91G60) Optimal stochastic control (93E20) Corporate finance (dividends, real options, etc.) (91G50) Probabilistic methods, particle methods, etc. for initial value and initial-boundary value problems involving PDEs (65M75)

Related Items (13)

Learning a functional control for high-frequency finance ⋮ Closed-form Approximations in Multi-asset Market Making ⋮ Size matters for OTC market makers: General results and dimensionality reduction techniques ⋮ Algorithmic market making in dealer markets with hedging and market impact ⋮ A Mean-Field Game of Market-Making against Strategic Traders ⋮ Computational Methods for Market Making Algorithms ⋮ Recent advances in reinforcement learning in finance ⋮ A data-driven deep learning approach for options market making ⋮ Dynamics of market making algorithms in dealer markets: Learning and tacit collusion ⋮ Improving reinforcement learning algorithms: Towards optimal learning rate policies ⋮ Algorithmic trading in a microstructural limit order book model ⋮ Algorithmic market making for options ⋮ Double-Execution Strategies Using Path Signatures

Cites Work

• Dealing with the inventory risk: a solution to the market making problem
• Buy Low, Sell High: A High Frequency Trading Perspective
• High-frequency trading in a limit order book
• Optimal market making
• Deep hedging
• Deep optimal stopping
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