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Algorithmic market making in dealer markets with hedging and market impact

Author

Listed:
  • Alexander Barzykin

    (HSBC)

  • Philippe Bergault

    (CMAP - Centre de Mathématiques Appliquées de l'Ecole polytechnique - X - École polytechnique - IP Paris - Institut Polytechnique de Paris - CNRS - Centre National de la Recherche Scientifique)

  • Olivier Guéant

    (UP1 - Université Paris 1 Panthéon-Sorbonne, CES - Centre d'économie de la Sorbonne - UP1 - Université Paris 1 Panthéon-Sorbonne - CNRS - Centre National de la Recherche Scientifique)

Abstract

In dealer markets, dealers provide prices at which they agree to buy and sell the assets and securities they have in their scope. With ever increasing trading volume, this quoting task has to be done algorithmically in most markets such as foreign exchange markets or corporate bond markets. Over the last ten years, many mathematical models have been designed that can be the basis of quoting algorithms in dealer markets. Nevertheless, in most (if not all) models, the dealer is a pure internalizer, setting quotes and waiting for clients. However, on many dealer markets, dealers also have access to an inter-dealer market or even public trading venues where they can hedge part of their inventory. In this paper, we propose a model taking this possibility into account, therefore allowing dealers to externalize part of their risk. The model displays an important feature well known to practitioners that within a certain inventory range the dealer internalizes the flow by appropriately adjusting the quotes and starts externalizing outside of that range. The larger the franchise, the wider is the inventory range suitable for pure internalization. The model is illustrated numerically with realistic parameters for USDCNH spot market.

Suggested Citation

  • Alexander Barzykin & Philippe Bergault & Olivier Guéant, 2022. "Algorithmic market making in dealer markets with hedging and market impact," Working Papers hal-03857976, HAL.
    • Handle: RePEc:hal:wpaper:hal-03857976
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    References listed on IDEAS

    as
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    Cited by:

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    2. Sergio Pulido & Mathieu Rosenbaum & Emmanouil Sfendourakis, 2023. "Understanding the worst-kept secret of high-frequency trading," Papers 2307.15599, arXiv.org, revised Jul 2024.
    3. Philippe Bergault & Olivier Gu'eant, 2023. "Liquidity Dynamics in RFQ Markets and Impact on Pricing," Papers 2309.04216, arXiv.org, revised Jun 2024.
    4. Alexander Barzykin & Robert Boyce & Eyal Neuman, 2024. "Unwinding Toxic Flow with Partial Information," Papers 2407.04510, arXiv.org.
    5. Marcello Monga, 2024. "Automated Market Making and Decentralized Finance," Papers 2407.16885, arXiv.org.
    6. Philippe Bergault & Leandro S'anchez-Betancourt, 2024. "A Mean Field Game between Informed Traders and a Broker," Papers 2401.05257, arXiv.org.
    7. Marcel Nutz & Kevin Webster & Long Zhao, 2023. "Unwinding Stochastic Order Flow: When to Warehouse Trades," Papers 2310.14144, arXiv.org.
    8. Vincent Ragel & Damien Challet, 2024. "Data time travel and consistent market making: taming reinforcement learning in multi-agent systems with anonymous data," Papers 2408.02322, arXiv.org.
    9. Kubo, Kenji & Nakagawa, Kei & Mizukami, Daiki & Acharya, Dipesh, 2023. "Optimal liquidation strategy for cryptocurrency marketplaces using stochastic control," Finance Research Letters, Elsevier, vol. 53(C).

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