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

@article{Guant2019DeepRL,
  title={Deep Reinforcement Learning for Market Making in Corporate Bonds: Beating the Curse of Dimensionality},
  author={Olivier Gu{\'e}ant and Iuliia Manziuk},
  journal={Applied Mathematical Finance},
  year={2019},
  volume={26},
  pages={387 - 452}
}
  • Olivier Guéant, Iuliia Manziuk
  • Published 2019
  • Economics, Computer Science
  • Applied Mathematical Finance
  • ABSTRACT In corporate bond markets, which are mainly OTC markets, market makers play a central role by providing bid and ask prices for bonds to asset managers. Determining the optimal bid and ask quotes that a market maker should set for a given universe of bonds is a complex task. The existing models, mostly inspired by the Avellaneda-Stoikov model, describe the complex optimization problem faced by market makers: proposing bid and ask prices for making money out of the difference between… CONTINUE READING
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