QX DeFi Risks Compass
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        • Defining Tokenomics Risks
        • Assessing Tokenomics Risk
        • Managing Tokenomics Risk
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        • Defining MEV Risk
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  • Risks and Returns of Uniswap V3 Liquidity Providers
  1. UNSYSTEMATIC RISKS
  2. Economic/Financial Risks
  3. Tokenomics Risk

Assessing Tokenomics Risk

PreviousDefining Tokenomics RisksNextManaging Tokenomics Risk

Last updated 1 year ago

Assessing the tokenomics risks of a DeFi protocol involves evaluating several factors: the token distribution model, to ensure it promotes long-term stability and avoids centralization; the supply mechanics, including inflation rates and maximum supply, to guard against devaluation; utility and incentives, to ensure they align with the protocol's health and user engagement; and governance structures, for their impact on decision-making and protocol evolution. This assessment requires a comprehensive understanding of the protocol's design, economic model, and market dynamics, often involving quantitative analysis and scenario planning.

  1. Analyze token distribution - Assess the concentration of token holdings, especially among insiders and early investors. High concentration raises centralization concerns.

  2. Model token supply dynamics - Build models to project circulating supply over time based on factors like emissions, burning, locking, etc. Assess potential for excessive inflation.

  3. Evaluate utility sinks - Examine uses that can continuously lock up tokens, like staking, governance, fees, collateral, etc. The more token sinks, the lower the sell pressure.

  4. Stress test incentives - Model various extreme scenario shifts in user behavior, market dynamics, protocol activity. Check if incentives still hold up.

  5. Perform a red team review - Explicitly challenge assumptions in the protocol's incentive design and economics. Identify exploitable weaknesses.

  6. Backtest with historical data - Take snapshots of major protocol changes and backtest incentive models and tokenomics against historical usage data.

  7. Risk segmentation analysis - Categorize tokenomics risks into factors like design flaws, technical bugs, market manias, black swan events. Estimated likelihood and impact of each.

  8. Benchmark peers - Compare tokenomics designs and observed outcomes against similar protocols. Identify common pitfalls.

  9. Risk monitoring framework - Establish key tokenomics risk indicators to monitor continuously, like ownership concentration, staking ratios, burn rates.

The goal is to rigorously pressure test tokenomics assumptions before launch and maintain ongoing monitoring. Critically challenging tokenomics creates antifragility.

Risks and Returns of Uniswap V3 Liquidity Providers

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Lioba Heimbach
Eric Schertenleib
Roger Wattenhofer
https://arxiv.org/abs/2205.08904
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