Unveiling Market Dynamics and Strategic Interactions in Crypto Arbitrage

We examine the dynamics of crypto arbitrage within the volatile and decentralized cryptocurrency markets. Using the Efficient Market Hypothesis (EMH), we assess market inefficiency and explore strategic interactions among traders through game theoretical models, focusing on Nash Equilibrium and cooperative behaviors. This gives insights into the behaviors investors exhibit in the crypto market, in contrast with the traditional financial assets markets. Our study considers different arbitrage types—spatial, statistical, and cyclic—in centralized and decentralized exchanges. Our analysis reveals persistent and significant price discrepancies that lead to multiple arbitrage opportunities, challenging the Law of One Price. We demonstrate that this market inefficiency is influenced by the amount of liquidity, transaction costs, regulatory differences, and access barriers. While aiming to advance the knowledge of market dynamics and DeFi in the context of crypto arbitrage, our findings offer theoretical insights and practical guidance for leveraging algorithmic strategies in crypto arbitrage. This paves the way for further research in developing machine learning (ML) applications for optimizing real-time arbitrage strategies.