Slippage-at-Risk (SaR): A Forward-Looking Liquidity Risk Framework for Perpetual Futures Exchanges

Battle Count: 73

Highly relevant to quantitative trading in perpetual futures markets. The framework directly addresses execution risk during liquidation events, which is critical for leveraged trading strategies. SaR metrics provide actionable signals for position sizing, leverage adjustment, and risk management. The concentration adjustment is particularly relevant for market-making strategies. The empirical validation on Hyperliquid data demonstrates practical applicability. The insurance fund sizing formula provides a quantitative basis for capital allocation decisions. The leading indicator properties (6-24 hour advance warning) are directly useful for trading risk management. However, the framework is primarily a risk measurement tool rather than a trading signal generator.

Moderate complexity. The core slippage computation (Algorithm 1) is straightforward - walking the order book. The concentration adjustment requires account-level attribution data which may be challenging to obtain. The full pipeline (Algorithm 2) involves multiple steps but each is well-defined. Parameter calibration (β, α, N_target, CR_thresh, λ, μ, c) requires domain expertise and empirical tuning. The main implementation challenges are: (1) obtaining reliable account-level liquidity attribution, (2) handling edge cases like insufficient liquidity (returning infinity), (3) real-time computation at 5-minute frequency across 184+ tokens, and (4) calibrating the deficit multiplier c for specific exchange architectures.

The paper provides detailed algorithms (Algorithm 1 for slippage computation, Algorithm 2 for the full SaR pipeline), explicit parameter recommendations (Table 9), and uses Hyperliquid on-chain order book data which is publicly accessible. Account-level attribution is available via on-chain data. However, the specific 5-minute order book snapshots and liquidation records for the October 10, 2025 event would need to be independently reconstructed. The Granger causality test and regression specifications are fully described. The deficit multiplier c is empirically calibrated and may vary.