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

TL;DR

This paper introduces Slippage-at-Risk (SaR), a forward-looking liquidity risk framework for perpetual futures exchanges that assesses liquidation risk based on current order book microstructure, improving systemic stress prediction.

Contribution

The paper presents a novel forward-looking liquidity risk metric, SaR, with three complementary measures and a concentration adjustment, linking it to optimal capital requirements and systemic risk prediction.

Findings

SaR effectively predicts systemic stress events.

Empirical validation on Hyperliquid data shows SaR's predictive power.

SaR outperforms traditional backward-looking metrics in stress forecasting.

Abstract

We introduce $\textbf{Slippage-at-Risk (SaR)}$, a quantitative framework for measuring liquidity risk in perpetual futures exchanges. Unlike backward-looking metrics such as Value-at-Risk computed on historical returns or realized deficit distributions, SaR provides a \emph{forward-looking} assessment of liquidation execution risk derived from current order book microstructure. The framework comprises three complementary metrics: $SaR(\alpha)$, the cross-sectional slippage quantile; $ESaR(\alpha)$, the expected slippage in the distributional tail; and $TSaR(\alpha)$, the aggregate dollar-denominated tail slippage. We extend the base framework with a \emph{concentration adjustment} that penalizes fragile liquidity structures where a small number of market makers dominate quote provision. Drawing on recent work by Chitra et al. (2025) on autodeleveraging mechanisms and insurance fund optimization, we establish a direct mapping from SaR metrics to optimal capital requirements. Empirical analysis using Hyperliquid order book data, including the October 10, 2025 liquidation cascade, demonstrates SaR's predictive validity as a leading indicator of systemic stress. We conclude with practical implementation guidance and discuss philosophical implications for risk management in decentralized financial systems.