Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
110 tokens/sec
GPT-4o
56 tokens/sec
Gemini 2.5 Pro Pro
44 tokens/sec
o3 Pro
6 tokens/sec
GPT-4.1 Pro
47 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

Non-Atomic Arbitrage in Decentralized Finance (2401.01622v3)

Published 3 Jan 2024 in cs.CE and q-fin.GN

Abstract: The prevalence of maximal extractable value (MEV) in the Ethereum ecosystem has led to a characterization of the latter as a dark forest. Studies of MEV have thus far largely been restricted to purely on-chain MEV, i.e., sandwich attacks, cyclic arbitrage, and liquidations. In this work, we shed light on the prevalence of non-atomic arbitrage on decentralized exchanges (DEXes) on the Ethereum blockchain. Importantly, non-atomic arbitrage exploits price differences between DEXes on the Ethereum blockchain as well as exchanges outside the Ethereum blockchain (i.e., centralized exchanges or DEXes on other blockchains). Thus, non-atomic arbitrage is a type of MEV that involves actions on and off the Ethereum blockchain. In our study of non-atomic arbitrage, we uncover that more than a fourth of the volume on Ethereum's biggest five DEXes from the merge until 31 October 2023 can likely be attributed to this type of MEV. We further highlight that only eleven searchers are responsible for more than 80% of the identified non-atomic arbitrage volume sitting at a staggering $132 billion and draw a connection between the centralization of the block construction market and non-atomic arbitrage. Finally, we discuss the security implications of these high-value transactions that account for more than 10% of Ethereum's total block value and outline possible mitigations.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (60)
  1. Y. Wang, Y. Chen, H. Wu, L. Zhou, S. Deng, and R. Wattenhofer, “Cyclic Arbitrage in Decentralized Exchanges,” in Companion Proceedings of the Web Conference 2022.   ACM, 4 2022, pp. 12–19.
  2. “Total Value Locked,” https://defillama.com/chains.
  3. “MEV-Boost Dashboard,” https://mevboost.pics/.
  4. T. Gupta, M. M. Pai, and M. Resnick, “The centralizing effects of private order flow on proposer-builder separation,” in 5th Conference on Advances in Financial Technologies, 10 2023.
  5. “Proposer-Builder Separation,” https://ethereum.org/roadmap/pbs/.
  6. P. Daian, S. Goldfeder, T. Kell, Y. Li, X. Zhao, I. Bentov, L. Breidenbach, and A. Juels, “Flash boys 2.0: Frontrunning in decentralized exchanges, miner extractable value, and consensus instability,” in 2020 IEEE Symposium on Security and Privacy (SP), 5 2020, pp. 910–927.
  7. K. Qin, L. Zhou, P. Gamito, P. Jovanovic, and A. Gervais, “An empirical study of defi liquidations: incentives, risks, and instabilities,” in Proceedings of the 21st ACM Internet Measurement Conference.   ACM, 11 2021, pp. 336–350.
  8. “DefiLlama,” https://defillama.com/protocols/dexes/Ethereum.
  9. “Zeromev,” https://zeromev.org/.
  10. Mempool Guru, “Mempool Guru,” https://mempool.guru/, 2023.
  11. S. Yang, F. Zhang, K. Huang, X. Chen, Y. Yang, and F. Zhu, “SoK: MEV Countermeasures: Theory and Practice,” arXiv preprint arXiv:2212.05111, 2023.
  12. Binance, “Historical Market Data,” https://www.binance.com/en/landing/data, 2023.
  13. “Today’s cryptocurrency prices by market cap,” 2023. [Online]. Available: https://coinmarketcap.com/
  14. H. Adams, N. Zinsmeister, and D. Robinson, “Uniswap v2 core,” 2020.
  15. H. Adams, N. Zinsmeister, M. Salem, R. Keefer, and D. Robinson, “Uniswap v3 core,” 2021.
  16. Sushiswap, “Be a Crypto Chef with Sushi,” https://docs.sushi.com/pdf/whitepaper.pdf, 2023.
  17. K. Qin, L. Zhou, and A. Gervais, “Quantifying blockchain extractable value: How dark is the forest?” in 2022 IEEE Symposium on Security and Privacy (SP).   IEEE, 5 2022, pp. 198–214.
  18. L. Zhou, K. Qin, C. F. Torres, D. V. Le, and A. Gervais, “High-Frequency Trading on Decentralized On-Chain Exchanges,” in 2021 IEEE Symposium on Security and Privacy (SP).   IEEE, 5 2021, pp. 428–445.
  19. C. F. Torres, R. Camino, and R. State, “Frontrunner Jones and the Raiders of the Dark Forest: An Empirical Study of Frontrunning on the Ethereum Blockchain,” in 30th USENIX Security Symposium, 8 2021, pp. 1343–1359.
  20. L. Heimbach, L. Kiffer, C. Ferreira Torres, and R. Wattenhofer, “Ethereum’s Proposer-Builder Separation: Promises and Realities,” in 2023 ACM Internet Measurement Conference (IMC), Montreal, QC, Canada, Oct. 2023.
  21. “DefiLlama API,” https://defillama.com/docs/api.
  22. “Jump Etherscan,” https://etherscan.io/address/0x9507c04b10486547584c37bcbd931b2a4fee9a41, 2023.
  23. Investopedia, “The Collapse of FTX: What Went Wrong With the Crypto Exchange?” https://www.investopedia.com/what-went-wrong-with-ftx-6828447, 2023.
  24. Cointelegraph, “TUSDC depegs as Circle confirms $3.3B stuck with Silicon Valley Bank,” https://cointelegraph.com/news/usdc-depegs-as-circle-confirms-3-3b-stuck-with-silicon-valley-bank, 2023.
  25. Blockworks, “The Investor’s Guide to Crypto Correlation,” https://blockworks.co/news/the-investors-guide-to-crypto-correlation, 2023.
  26. K. Q. Nguyen, “The correlation between the stock market and bitcoin during covid-19 and other uncertainty periods,” Finance Research Letters, vol. 46, p. 102284, 2022. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S1544612321003238
  27. “Meeting calendars, statements, and minutes (2018-2024),” https://www.federalreserve.gov/monetarypolicy/fomccalendars.html, 2023.
  28. “How the fed impacts stocks, crypto and other investments,” https://www.bankrate.com/investing/federal-reserve-impact-on-stocks-crypto-other-investments/#crypto, 2023.
  29. G. Konstantopoulos and V. Buterin, “Ethereum Reorgs After The Merge,” https://www.paradigm.xyz/2021/07/ethereum-reorgs-after-the-merge, 2021.
  30. D. Grandjean, L. Heimbach, and R. Wattenhofer, “Ethereum Proof-of-Stake Consensus Layer: Participation and Decentralization,” arXiv preprint arXiv:2306.10777, 2023.
  31. J. Milionis, C. C. Moallemi, T. Roughgarden, and A. L. Zhang, “Automated Market Making and Loss-Versus-Rebalancing,” arXiv preprint arXiv:2208.06046, 2022.
  32. J. Milionis, C. C. Moallemi, and T. Roughgarden, “Automated Market Making and Arbitrage Profits in the Presence of Fees,” arXiv preprint arXiv:2305.14604, 2023.
  33. S. Eskandari, S. Moosavi, and J. Clark, “SoK: Transparent Dishonesty: Front-Running Attacks on Blockchain,” in Financial Cryptography and Data Security, vol. 11599.   Springer International Publishing, 2020, pp. 170–189.
  34. I. Bentov, Y. Ji, F. Zhang, L. Breidenbach, P. Daian, and A. Juels, “Tesseract: Real-time cryptocurrency exchange using trusted hardware,” in Proceedings of the 2019 ACM SIGSAC Conference on Computer and Communications Security, ser. CCS ’19, 2019, p. 1521–1538.
  35. C. Stathakopoulou, S. Rüsch, M. Brandenburger, and M. Vukolić, “Adding Fairness to Order: Preventing Front-Running Attacks in BFT Protocols using TEEs,” in 2021 40th International Symposium on Reliable Distributed Systems (SRDS).   IEEE, 2021, pp. 34–45.
  36. M. Kelkar, F. Zhang, S. Goldfeder, and A. Juels, “Order-Fairness for Byzantine Consensus,” in Annual International Cryptology Conference.   Springer, 2020, pp. 451–480.
  37. L. Baird, “The Swirlds Hashgraph Consensus Algorithm: Fair, Fast, Byzantine Fault Tolerance,” Swirlds Tech Reports SWIRLDS-TR-2016-01, Tech. Rep, 2016.
  38. K. Kursawe, “Wendy, the Good Little Fairness Widget: Achieving Order Fairness for Blockchains,” in Proceedings of the 2nd ACM Conference on Advances in Financial Technologies, 2020, pp. 25–36.
  39. Y. Zhang, S. Setty, Q. Chen, L. Zhou, and L. Alvisi, “Byzantine Ordered Consensus Without Byzantine Oligarchy,” in 14th {normal-{\{{USENIX}normal-}\}} Symposium on Operating Systems Design and Implementation ({normal-{\{{OSDI}normal-}\}} 20), 2020, pp. 633–649.
  40. M. Kelkar, S. Deb, and S. Kannan, “Order-Fair Consensus in the Permissionless Setting,” IACR Cryptol. ePrint Arch., vol. 2021, p. 139, 2021.
  41. M. Kelkar, S. Deb, S. Long, A. Juels, and S. Kannan, “Themis: Fast, Strong Order-Fairness in Byzantine Consensus,” Cryptology ePrint Archive, Report 2021/1465, 2021.
  42. C. Cachin, J. Mićić, and N. Steinhauer, “Quick Order Fairness,” in Financial Cryptography and Data Security (FC), Grenada, 2022.
  43. M. K. Reiter and K. P. Birman, “How to Securely Replicate Services,” ACM Transactions on Programming Languages and Systems (TOPLAS), vol. 16, no. 3, pp. 986–1009, 1994.
  44. A. Miller, Y. Xia, K. Croman, E. Shi, and D. Song, “The Honey Badger of BFT Protocols,” ser. CCS ’16, 2016, p. 31–42.
  45. A. Asayag, G. Cohen, I. Grayevsky, M. Leshkowitz, O. Rottenstreich, R. Tamari, and D. Yakira, “A Fair Consensus Protocol for Transaction Ordering,” in 2018 IEEE 26th International Conference on Network Protocols (ICNP), 2018.
  46. A. Orda and O. Rottenstreich, “Enforcing Fairness in Blockchain Transaction Ordering,” Peer-to-peer Networking and Applications, vol. 14, no. 6, pp. 3660–3673, 2021.
  47. H. Zhang, L.-H. Merino, V. Estrada-Galinanes, and B. Ford, “Flash freezing flash boys: Countering blockchain front-running,” in 2022 IEEE 42nd International Conference on Distributed Computing Systems Workshops (ICDCSW).   IEEE, 2022, pp. 90–95.
  48. A. Constantinescu, D. Ghinea, L. Heimbach, Z. Wang, and R. Wattenhofer, “A Fair and Resilient Decentralized Clock Network for Transaction Ordering,” in 27th International Conference on Principles of Distributed Systems (OPODIS), Tokyo, Japan, Dec. 2023.
  49. P. Momeni, S. Gorbunov, and B. Zhang, “Fairblock: Preventing Blockchain Front-Running with Minimal Overheads,” in Security and Privacy in Communication Networks: 18th EAI International Conference, SecureComm 2022, Virtual Event, October 2022, Proceedings.   Springer, 2023, pp. 250–271.
  50. A. Tatabitovska, O. Ersoy, and Z. Erkin, “Mitigation of Transaction Manipulation Attacks in UniSwap,” 2021.
  51. L. Breidenbach, P. Daian, F. Tramèr, and A. Juels, “Enter the Hydra: Towards Principled Bug Bounties and Exploit-Resistant Smart Contracts,” in 27th USENIX Security Symposium, 2018, pp. 1335–1352.
  52. Y. Doweck and I. Eyal, “Multi-Party Timed Commitments,” arXiv preprint arXiv:2005.04883, 2020.
  53. L. Heimbach and R. Wattenhofer, “Eliminating Sandwich Attacks with the Help of Game Theory,” in ACM Asia Conference on Computer and Communications Security (ASIA CCS), Nagasaki, Japan, Jun. 2022.
  54. L. Zhou, K. Qin, and A. Gervais, “A2MM: Mitigating Frontrunning, Transaction Reordering and Consensus Instability in Decentralized Exchanges,” 2021.
  55. L. Heimbach and R. Wattenhofer, “SoK: Preventing Transaction Reordering Manipulations in Decentralized Finance,” in 4th ACM Conference on Advances in Financial Technologies (AFT), Cambridge, MA, USA, Sep. 2022.
  56. L. Heimbach, Y. Wang, and R. Wattenhofer, “Behavior of Liquidity Providers in Decentralized Exchanges,” in 2021 Crypto Valley Conference on Blockchain Technology (CVCBT), Rotkreuz, Switzerland, Oct. 2021.
  57. L. Heimbach, E. Schertenleib, and R. Wattenhofer, “Risks and Returns of Uniswap V3 Liquidity Providers,” in 4th ACM Conference on Advances in Financial Technologies (AFT), Cambridge, MA, USA, Sep. 2022.
  58. A. Wahrstätter, J. Ernstberger, A. Yaish, L. Zhou, K. Qin, T. Tsuchiya, S. Steinhorst, D. Svetinovic, N. Christin, M. Barczentewicz, and A. Gervais, “Blockchain Censorship,” arXiv preprint arXiv:2305.18545, 2023.
  59. A. Wahrstätter, L. Zhou, K. Qin, D. Svetinovic, and A. Gervais, “Time to Bribe: Measuring Block Construction Market,” arXiv preprint arXiv:2305.16468, 2023.
  60. C. Schwarz-Schilling, F. Saleh, T. Thiery, J. Pan, N. Shah, and B. Monnot, “Time is Money: Strategic Timing Games in Proof-of-Stake Protocols,” arXiv preprint arXiv:2305.09032, 2023.
User Edit Pencil Streamline Icon: https://streamlinehq.com
Authors (3)
  1. Lioba Heimbach (26 papers)
  2. Vabuk Pahari (4 papers)
  3. Eric Schertenleib (5 papers)
Citations (13)
View on Semantic Scholar

Summary

  • The paper quantifies non-atomic arbitrage by modeling on-chain and off-chain price differences, finding that it accounts for over 25% of trading volume in top Ethereum DEXes.
  • It reveals that just eleven searchers drive more than 80% of non-atomic arbitrage, highlighting significant centralization risks.
  • The study uses a robust heuristic methodology to identify arbitrage opportunities and suggests strategies to mitigate security and market concentration concerns.

Non-Atomic Arbitrage in Decentralized Finance: An Analytical Review

The research paper "Non-Atomic Arbitrage in Decentralized Finance" provides a meticulous and technical analysis regarding a distinct form of arbitrage within the Ethereum decentralized exchange (DEX) ecosystem: non-atomic arbitrage. This type of arbitrage exploits price disparities not only between DEXes on the Ethereum blockchain but also in conjunction with off-chain exchanges, including centralized exchanges (CEXes) or DEXes on other blockchains. This work significantly extends prior research constrained to purely on-chain maximal extractable value (MEV), highlighting the intricate dynamics between on-chain and off-chain market interactions.

Key Findings

A central contribution of the paper lies in quantifying the scope of non-atomic arbitrage within the Ethereum DEX environment. The authors’ analysis reveals that non-atomic arbitrage comprises over one-fourth of the trading volume across the five largest Ethereum DEXes within the survey period. This suggests a significant, yet previously underappreciated, portion of DeFi activity is influenced by this MEV type. Notably, the volume attributed to non-atomic arbitrage reached $132 billion USD, underscoring the meaningful impact on the DEX ecosystem.

The authors further elucidate that the execution of such arbitrage is concentrated among a small cadre of actors—only eleven searchers were found to be responsible for over 80% of the total non-atomic arbitrage volume. This presents potential centralization concerns within the block construction market, a notable phenomenon given Ethereum's transition to proof-of-stake (PoS) and the advent of proposer-builder separation (PBS).

Model and Methodology

The authors present a theoretical model to calculate profits from non-atomic arbitrage, emphasizing the relationship between asset prices on DEXes and external exchanges. The model establishes that searchers profit by executing trades first on-chain and then off-chain when they detect a favorable price differential. These trades are typically frontrunners, marked privately to evade potential predatory bots, ensuring the efficiency and profitability of the detected arbitrage opportunities.

The paper employs a robust heuristic approach to identify non-atomic arbitrage, leveraging blockchain data and transaction criteria such as transaction simplicity, privacy, and priority fee structures. The heuristic demonstrated efficacy in correlating with known integrated searchers and filtering transactions optimally for those engaged in non-atomic arbitrage.

Implications and Future Directions

The research indicates significant implications for the Ethereum ecosystem both in terms of security and the architecture of financial interactions on chain. The paper articulates potential security risks tied to transaction value concentrations and suggests an evolving landscape where sophisticated traders could dominate arbitrage opportunities, influencing decentralization principles.

Moreover, by detailing practical strategies to mitigate these centralizing effects—such as separating top-of-block transactions—the paper invites further investigative exploration into MEV extraction methods. Future work may pivot on enhancing transparency in MEV activities, refining transaction ordering mechanisms, and exploring shorter block times to diminish arbitrage profitability.

Conclusion

This paper highlights a crucial aspect of decentralized finance that intertwines on-chain mechanics with broader off-chain market reality. By dissecting non-atomic arbitrage, the research not only quantifies its prevalence and resultant centralization but also encourages the community to rethink existing mechanisms in the Ethereum DEX ecosystem. Such insights can help inform and shape ongoing adjustments to Ethereum's infrastructure, potentially safeguarding against centralization while enhancing both composability and security within cryptoeconomic systems.

File Document Download Save Streamline Icon: https://streamlinehq.com PDF File Document Download Save Streamline Icon: https://streamlinehq.com Markdown
Youtube Logo Streamline Icon: https://streamlinehq.com

YouTube