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Arbitrage detection using max plus product iteration on foreign exchange rate graphs

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  • Cui, Zhenyu
  • Taylor, Stephen

Abstract

We propose a novel graph-theoretic method to detect k-currency arbitrage in spot foreign exchange (FX) markets and discuss and compare the runtime performance of this method against the permutation search approach. This technique is applied to a minute level bid/ask quote dataset consisting of rates constructed from all G10 currency pairs. We validate this approach through an example while also demonstrating its runtime efficiency, especially in the case of spot markets consisting of a large number of currency pairs. Finally, several potential extensions including trading applications are discussed.

Suggested Citation

  • Cui, Zhenyu & Taylor, Stephen, 2020. "Arbitrage detection using max plus product iteration on foreign exchange rate graphs," Finance Research Letters, Elsevier, vol. 35(C).
    • Handle: RePEc:eee:finlet:v:35:y:2020:i:c:s1544612319304362
    DOI: 10.1016/j.frl.2019.08.027
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    References listed on IDEAS

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    1. Roman Kozhan & Wing Wah Tham, 2012. "Execution Risk in High-Frequency Arbitrage," Management Science, INFORMS, vol. 58(11), pages 2131-2149, November.
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    4. Gradojevic, Nikola & Erdemlioglu, Deniz & Gençay, Ramazan, 2017. "Informativeness of trade size in foreign exchange markets," Economics Letters, Elsevier, vol. 150(C), pages 27-33.
    5. Tarun Ramadorai, 2008. "What determines transaction costs in foreign exchange markets?," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 13(1), pages 14-25.
    6. Huang, Roger D & Masulis, Ronald W, 1999. "FX Spreads and Dealer Competition across the 24-Hour Trading Day," The Review of Financial Studies, Society for Financial Studies, vol. 12(1), pages 61-93.
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    Cited by:

    1. Ariel Neufeld & Julian Sester, 2023. "Neural networks can detect model-free static arbitrage strategies," Papers 2306.16422, arXiv.org, revised Aug 2024.

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