Size matters for OTC market makers: general results and dimensionality reduction techniques.

@article{Bergault2019SizeMF,
  title={Size matters for OTC market makers: general results and dimensionality reduction techniques.},
  author={Philippe Bergault and Olivier Gu{\'e}ant},
  journal={arXiv: Trading and Market Microstructure},
  year={2019}
}
  • Philippe Bergault, Olivier Guéant
  • Published 2019
  • Economics
  • arXiv: Trading and Market Microstructure
  • In most OTC markets, a small number of market makers provide liquidity to other market participants. More precisely, for a list of assets, they set prices at which they agree to buy and sell. Market makers face therefore an interesting optimization problem: they need to choose bid and ask prices for making money while mitigating the risk associated with holding inventory in a volatile market. Many market making models have been proposed in the academic literature, most of them dealing with… CONTINUE READING
    4 Citations
    Deep Reinforcement Learning for Market Making in Corporate Bonds: Beating the Curse of Dimensionality
    • 17
    • PDF
    Algorithmic market making for options
    • 5
    • PDF
    Adaptive trading strategies across liquidity pools
    • 1
    • PDF

    References

    SHOWING 1-10 OF 17 REFERENCES
    Deep Reinforcement Learning for Market Making in Corporate Bonds: Beating the Curse of Dimensionality
    • 17
    • PDF
    Optimal market making
    • 30
    • PDF
    Option market making under inventory risk
    • 39
    • PDF
    The Financial Mathematics of Market Liquidity : From Optimal Execution to Market Making
    • 69
    Algorithmic Trading with Model Uncertainty
    • 63
    • PDF
    Algorithmic market making for options
    • 5
    • PDF
    A Stochastic Control Approach to Option Market Making
    • 7
    • PDF
    Optimal High Frequency Trading with Limit and Market Orders
    • 153
    • PDF
    Algorithmic market making: the case of equity derivatives
    • 4