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Wasserstein Robust Market Making via Entropy Regularization

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  • Zhou Fang
  • Arie Israel

Abstract

In this paper, we introduce a robust market making framework based on Wasserstein distance, utilizing a stochastic policy approach enhanced by entropy regularization. We demonstrate that, under mild assumptions, the robust market making problem can be reformulated as a convex optimization question. Additionally, we outline a methodology for selecting the optimal radius of the Wasserstein ball, further refining our framework's effectiveness.

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  • Zhou Fang & Arie Israel, 2025. "Wasserstein Robust Market Making via Entropy Regularization," Papers 2503.04072, arXiv.org.
    • Handle: RePEc:arx:papers:2503.04072
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    References listed on IDEAS

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    1. Marco Avellaneda & Sasha Stoikov, 2008. "High-frequency trading in a limit order book," Quantitative Finance, Taylor & Francis Journals, vol. 8(3), pages 217-224.
    2. Ho, Thomas & Stoll, Hans R., 1981. "Optimal dealer pricing under transactions and return uncertainty," Journal of Financial Economics, Elsevier, vol. 9(1), pages 47-73, March.
    3. Jose Blanchet & Karthyek Murthy & Nian Si, 2022. "Confidence regions in Wasserstein distributionally robust estimation [Distributionally robust groupwise regularization estimator]," Biometrika, Biometrika Trust, vol. 109(2), pages 295-315.
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