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Detecting and identifying arbitrage in the spot foreign exchange market

Author

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  • Zhenyu Cui
  • Wenhan Qian
  • Stephen Taylor
  • Lingjiong Zhu

Abstract

We propose a theoretical framework for the detection and identification of triangular arbitrage opportunities between currency exchange rates in the spot foreign exchange market. We obtain sufficient conditions for the exclusion of triangular arbitrage opportunities in the setting of non-trivial transaction costs in terms of the currency rates of the market under consideration. Then we propose an efficient computational approach which can detect triangular arbitrage opportunities in real time. Finally, we consider numerical studies that utilize spot currency exchange rate quotes to substantiate and present applications of the theoretical findings as well as to demonstrate the efficiency of the proposed computational arbitrage detection and identification methods.

Suggested Citation

  • Zhenyu Cui & Wenhan Qian & Stephen Taylor & Lingjiong Zhu, 2020. "Detecting and identifying arbitrage in the spot foreign exchange market," Quantitative Finance, Taylor & Francis Journals, vol. 20(1), pages 119-132, January.
    • Handle: RePEc:taf:quantf:v:20:y:2020:i:1:p:119-132
    DOI: 10.1080/14697688.2019.1639801
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    Cited by:

    1. Ariel Neufeld & Julian Sester & Daiying Yin, 2022. "Detecting data-driven robust statistical arbitrage strategies with deep neural networks," Papers 2203.03179, arXiv.org, revised Feb 2024.
    2. Fan, Zhenzhen & Paseka, Alexander & Qi, Zhen & Zhang, Qi, 2022. "Currency carry trade: The decline in performance after the 2008 Global Financial Crisis," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 76(C).

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