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Latency and Liquidity Risk

Author

Listed:
  • 'Alvaro Cartea
  • Sebastian Jaimungal
  • Leandro S'anchez-Betancourt

Abstract

Latency (i.e., time delay) in electronic markets affects the efficacy of liquidity taking strategies. During the time liquidity takers process information and send marketable limit orders (MLOs) to the exchange, the limit order book (LOB) might undergo updates, so there is no guarantee that MLOs are filled. We develop a latency-optimal trading strategy that improves the marksmanship of liquidity takers. The interaction between the LOB and MLOs is modelled as a marked point process. Each MLO specifies a price limit so the order can receive worse prices and quantities than those the liquidity taker targets if the updates in the LOB are against the interest of the trader. In our model, the liquidity taker balances the tradeoff between missing trades and the costs of walking the book. We employ techniques of variational analysis to obtain the optimal price limit of each MLO the agent sends. The price limit of a MLO is characterized as the solution to a new class of forward-backward stochastic differential equations (FBSDEs) driven by random measures. We prove the existence and uniqueness of the solution to the FBSDE and numerically solve it to illustrate the performance of the latency-optimal strategies.

Suggested Citation

  • 'Alvaro Cartea & Sebastian Jaimungal & Leandro S'anchez-Betancourt, 2019. "Latency and Liquidity Risk," Papers 1908.03281, arXiv.org.
  • Handle: RePEc:arx:papers:1908.03281
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    References listed on IDEAS

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    1. Charles-Albert Lehalle & Sophie Laruelle (ed.), 2013. "Market Microstructure in Practice," World Scientific Books, World Scientific Publishing Co. Pte. Ltd., number 8967.
    2. Ciamac C. Moallemi & Mehmet Sağlam, 2013. "OR Forum---The Cost of Latency in High-Frequency Trading," Operations Research, INFORMS, vol. 61(5), pages 1070-1086, October.
    3. Edward Hoyle, 2010. "Information-based models for finance and insurance," Papers 1010.0829, arXiv.org.
    4. Weston Barger & Matthew Lorig, 2019. "Optimal Liquidation Under Stochastic Price Impact," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 22(02), pages 1-28, March.
    5. Alvaro Cartea & Sebastian Jaimungal & Jamie Walton, 2018. "Foreign Exchange Markets with Last Look," Papers 1806.04460, arXiv.org.
    6. Duffie, Darrel & Lions, Pierre-Louis, 1992. "PDE solutions of stochastic differential utility," Journal of Mathematical Economics, Elsevier, vol. 21(6), pages 577-606.
    7. Philippe Casgrain & Sebastian Jaimungal, 2018. "Mean-Field Games with Differing Beliefs for Algorithmic Trading," Papers 1810.06101, arXiv.org, revised Dec 2019.
    8. Duffie, Darrell & Epstein, Larry G, 1992. "Stochastic Differential Utility," Econometrica, Econometric Society, vol. 60(2), pages 353-394, March.
    9. Sasha Stoikov & Rolf Waeber, 2016. "Reducing transaction costs with low-latency trading algorithms," Quantitative Finance, Taylor & Francis Journals, vol. 16(9), pages 1445-1451, September.
    10. Duffie, Darrell & Epstein, Larry G, 1992. "Asset Pricing with Stochastic Differential Utility," The Review of Financial Studies, Society for Financial Studies, vol. 5(3), pages 411-436.
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