Liquidity Jump, Liquidity Diffusion, and Crypto Wash Trading

ArXiv ID: 2411.05803 “View on arXiv”

Authors: Unknown

Abstract

We develop a new framework to detect wash trading in crypto assets through real-time liquidity fluctuation. We propose that short-term price jumps in crypto assets results from wash trading-induced liquidity fluctuation, and construct two complementary liquidity measures, liquidity jump (size of fluctuation) and liquidity diffusion (volatility of fluctuation), to capture the behavioral signature of wash trading. Using US stocks as a benchmark, we demonstrate that joint elevation in both liquidity metrics indicates wash trading in crypto assets. A simulated regulatory treatment that removes likely wash trades confirms this dynamic: it reduces liquidity diffusion significantly while leaving liquidity jump largely unaffected. These findings align with a theoretical model in which manipulative traders amplify both the level and variance of price pressure, whereas passive investors affect only the level. Our model offers practical tools for investors to assess market quality and for regulators to monitor manipulation risk on crypto exchanges without oversight.

Keywords: wash trading detection, liquidity metrics, crypto assets, market manipulation, liquidity jump, Cryptocurrency

Complexity vs Empirical Score

• Math Complexity: 7.0/10
• Empirical Rigor: 6.5/10
• Quadrant: Holy Grail
• Why: The paper introduces novel theoretical constructs (liquidity jump/diffusion) supported by a theoretical model and relies heavily on empirical data analysis (US stocks vs. crypto, simulated regulatory treatment) to validate findings.

  flowchart TD
    A["Research Goal:<br>Detect wash trading in<br>crypto assets via<br>liquidity fluctuations"] --> B
    
    subgraph B ["Data & Benchmarking"]
        direction LR
        B1["Real-time Crypto Data"]
        B2["US Stocks Benchmark"]
    end
    
    B --> C{"Construct Complementary<br>Liquidity Measures"}
    
    C --> C1["Liquidity Jump<br>Size of fluctuation"]
    C --> C2["Liquidity Diffusion<br>Volatility of fluctuation"]
    
    C1 --> D["Computational Process:<br>Joint Elevation Analysis"]
    C2 --> D
    
    D --> E
    
    subgraph E ["Key Findings & Outcomes"]
        direction LR
        E1["Wash Trading Identified<br>by joint high metrics"]
        E2["Regulatory Simulation<br>Reduces Diffusion,<br>preserves Jump"]
        E3["Theoretical Model Validation<br>Manipulators amplify level & variance"]
    end