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Oracle Counterpoint: Relationships between On-chain and Off-chain Market Data

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  • Zhimeng Yang
  • Ariah Klages-Mundt
  • Lewis Gudgeon

Abstract

We investigate the theoretical and empirical relationships between activity in on-chain markets and pricing in off-chain cryptocurrency markets (e.g., ETH/USD prices). The motivation is to develop methods for proxying off-chain market data using data and computation that is in principle verifiable on-chain and could provide an alternative approach to blockchain price oracles. We explore relationships in PoW mining, PoS validation, block space markets, network decentralization, usage and monetary velocity, and on-chain Automated Market Makers (AMMs). We select key features from these markets, which we analyze through graphical models, mutual information, and ensemble machine learning models to explore the degree to which off-chain pricing information can be recovered entirely on-chain. We find that a large amount of pricing information is contained in on-chain data, but that it is generally hard to recover precise prices except on short time scales of retraining the model. We discuss how even noisy information recovered from on-chain data could help to detect anomalies in oracle-reported prices on-chain.

Suggested Citation

  • Zhimeng Yang & Ariah Klages-Mundt & Lewis Gudgeon, 2023. "Oracle Counterpoint: Relationships between On-chain and Off-chain Market Data," Papers 2303.16331, arXiv.org, revised Jul 2023.
    • Handle: RePEc:arx:papers:2303.16331
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    References listed on IDEAS

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    1. Julien Prat & Benjamin Walter, 2021. "An Equilibrium Model of the Market for Bitcoin Mining," Journal of Political Economy, University of Chicago Press, vol. 129(8), pages 2415-2452.
    2. David Easley & Marcos López de Prado & Maureen O’Hara & Zhibai Zhang & Wei Jiang, 2021. "Microstructure in the Machine Age [The risk of machine learning]," The Review of Financial Studies, Society for Financial Studies, vol. 34(7), pages 3316-3363.
    3. Guillermo Angeris & Tarun Chitra, 2020. "Improved Price Oracles: Constant Function Market Makers," Papers 2003.10001, arXiv.org, revised Jun 2020.
    4. Lucy Huo & Ariah Klages-Mundt & Andreea Minca & Frederik Christian Munter & Mads Rude Wind, 2021. "Decentralized Governance of Stablecoins with Closed Form Valuation," Papers 2109.08939, arXiv.org, revised Jul 2022.
    5. David Easley & Marcos López de Prado & Maureen O’Hara & Zhibai Zhang, 2021. "Microstructure in the Machine Age," NBER Chapters, in: Big Data: Long-Term Implications for Financial Markets and Firms, pages 3316-3363, National Bureau of Economic Research, Inc.
    6. Athey, Susan & Parashkevov, Ivo & Sarukkai, Vishnu & Xia, Jing, 2016. "Bitcoin Pricing, Adoption, and Usage: Theory and Evidence," Research Papers 3469, Stanford University, Graduate School of Business.
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