Understanding the 'Stable' in Stablecoin: Volatility Metrics

Detailed Instructions

First, explicitly define the stability benchmark, which is typically a 1:1 peg to a fiat currency like the USD, but could also be a basket of assets or an inflation index. The analysis is meaningless without this reference point. Next, collect historical price data for the stablecoin from multiple, reliable sources such as on-chain DEXs (e.g., Uniswap V3 pools) and centralized exchanges (e.g., Coinbase).

• Sub-step 1: Identify Data Sources: Use APIs from CoinGecko (coingecko.com/api/v3/coins/.../market_chart) or directly query on-chain data via a node or subgraph for the specific liquidity pool (e.g., USDC/WETH pool 0x88e6A0c2dDD26FEEb64F039a2c41296FcB3f5640).
• Sub-step 2: Set Parameters: Determine the analysis timeframe (e.g., 90 days), data granularity (e.g., hourly or daily closing prices), and the specific trading pairs to monitor.
• Sub-step 3: Clean and Align Data: Remove outliers and ensure timestamps are synchronized across sources to create a consistent, continuous price series for the stablecoin against its peg.

Tip: For on-chain data, consider using The Graph subgraph queries to efficiently fetch large datasets of swap events and derived prices.

Detailed Instructions

With the cleaned price series, calculate key volatility metrics. The primary measure is the standard deviation of the percentage price deviations from the peg (e.g., (Price - $1.00) / $1.00). This provides a baseline for dispersion. More specifically for stablecoins, calculate the Mean Absolute Deviation (MAD) and the Maximum Absolute Deviation (MaxAD), which are often more intuitive for measuring peg adherence.

• Sub-step 1: Compute Daily Returns: Calculate the daily log returns or simple percentage changes from the peg. For a series in Python: returns = np.log(df['price'] / 1.0).
• Sub-step 2: Calculate Standard Deviation: Annualize the standard deviation of returns to get a common volatility figure: annualized_vol = returns.std() * np.sqrt(365).
• Sub-step 3: Calculate Deviation Metrics: Compute MAD: mad = np.mean(np.abs(df['price'] - 1.0)). Identify the MaxAD: maxad = np.max(np.abs(df['price'] - 1.0)).

Tip: A stablecoin with an annualized volatility below 5% and a MaxAD consistently under 2% is generally considered well-maintained, though targets are stricter.

Detailed Instructions

Stability is tested during stress. Define a depeg threshold, commonly +/- 1% or +/- 3% from $1.00. Systematically identify all periods where the price breaches this band. For each event, calculate its duration, magnitude, and recovery profile. Additionally, compute the Time-Outside-Band (TOB) metric, which is the percentage of the total observed time the price spent beyond the acceptable deviation band.

• Sub-step 1: Flag Breaches: Programmatically flag all price points where abs(price - 1.0) > 0.01 (for 1% band).
• Sub-step 2: Cluster Events: Group consecutive breach points into single depeg events to analyze their start time, end time, and minimum/maximum price reached.
• Sub-step 3: Calculate TOB: TOB = (total_breach_samples / total_samples) * 100. A TOB of 0.5% means the coin was depegged for 0.5% of the observed period.

Tip: Correlate major depeg events with on-chain events (e.g., large mint/burn transactions from the 0x... admin address) or macroeconomic news to identify root causes.

Detailed Instructions

Liquidity depth is critical for mechanical stability. Analyze the available liquidity within a tight range around the peg (e.g., $0.995 to $1.005) on major DEXs. High depth means large trades can be executed with minimal slippage, preventing price drift. Query the liquidity pools to calculate the theoretical slippage for a standardized sell order (e.g., $1M).

• Sub-step 1: Query Pool State: For a Uniswap V3 pool, use a contract call to slot0() and liquidity() on the pool address to get current tick and liquidity. Use a library to derive the liquidity distribution.
• Sub-step 2: Calculate Slippage: Simulate a swap. For a constant product AMM (like Uniswap V2), slippage can be approximated. For V3, calculate precisely using the liquidity in the active tick.

codeCopy
// Pseudocode for slippage estimate
amountIn = 1000000; // $1M USDC
reserveIn = pool.getReserves()[0]; // USDC in pool
reserveOut = pool.getReserves()[1]; // DAI in pool
amountOut = (reserveOut * amountIn) / (reserveIn + amountIn);
slippage = (1.0 - (amountOut / amountIn)) * 100;

• Sub-step 3: Monitor Depth Over Time: Track how liquidity depth changes, especially during volatile periods. A sharp withdrawal of liquidity can be a leading indicator of instability.

Tip: Consistently high slippage (>0.1% for a $1M trade) indicates thin markets, making the peg vulnerable to large transactions.

Detailed Instructions

The final step is to synthesize the individual metrics into a comprehensive Stability Score. This involves weighting each metric based on its importance for peg maintenance. A common framework assigns weights to Volatility (e.g., 30%), MaxAD (25%), TOB (25%), and Liquidity Slippage (20%). Normalize each metric to a 0-100 scale, where 100 represents perfect stability (zero volatility, zero deviation, 0% TOB, zero slippage).

• Sub-step 1: Normalize Metrics: For volatility, use an inverse scale: score_vol = max(0, 100 - (annualized_vol * 10)). For MaxAD: score_maxad = max(0, 100 - (maxad * 500)).
• Sub-step 2: Apply Weighted Average: Calculate the final score: final_score = (score_vol * 0.3) + (score_maxad * 0.25) + (score_tob * 0.25) + (score_slippage * 0.2).
• Sub-step 3: Create a Rating Tier: Map the score to a tier: 90-100 (Excellent), 75-89 (Good), 60-74 (Moderate), <60 (Poor). This allows for easy comparison across different stablecoins.

Tip: This synthesized score should be recalculated regularly (e.g., weekly) and tracked over time to monitor the stability trend, not just a point-in-time snapshot.