Oracle Counterpoint: Relationships Between On-Chain and Off-Chain Market Data

Zhimeng Yang, Ariah Klages-Mundt () and Lewis Gudgeon
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Zhimeng Yang: Coinbase
Ariah Klages-Mundt: Cornell University
Lewis Gudgeon: Imperial College London

A chapter in Mathematical Research for Blockchain Economy, 2023, pp 133-151 from Springer

Abstract: 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.

Keywords: Oracles; DeFi; On-chain data; Blockchain economics; Ensemble learning (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:spr:lnopch:978-3-031-48731-6_8

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DOI: 10.1007/978-3-031-48731-6_8

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