Paper: arXiv 2603.09164

Authors: Otar Sepper

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: $SaR(α)$, the cross-sectional slippage quantile; $ESaR(α)$, the expected slippage in the distributional tail; and $TSaR(α)$, 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.

Complexity vs Empirical Score

  • Math Complexity: 6.5/10
  • Empirical Rigor: 6.0/10 (capped from 8.0: transaction costs not modelled; see penalty flags)
  • Quadrant: Holy Grail — high math complexity, high empirical rigor

Why this score: The paper presents a novel forward-looking risk management framework (SaR) tailored for decentralized perpetual futures. It bridges order book microstructure with liquidation risk and insurance fund sizing, validated by an empirical analysis of the October 10, 2025 Hyperliquid cascade.

Research Flowchart

  flowchart TD
    A["<b>Research Goal:</b> Measure forward-looking liquidation liquidity risk in perpetual futures"] --> B["<b>Inputs:</b> Hyperliquid order book microstructure data & historical liquidation cascades"]
    B --> C["<b>Core Methodology:</b> Calculate base SaR metrics <br><i>(SaR, ESaR, TSaR)</i>"]
    C --> D["<b>Concentration Adjustment:</b> Penalty for order book depth dominance by few market makers"]
    D --> E["<b>Capital Optimization:</b> Map SaR metrics to Insurance Fund requirements via Chitra et al. framework"]
    E --> F["<b>Outcomes:</b> SaR validated as a leading indicator of systemic stress and execution risk"]