Autodeleveraging: Impossibilities and Optimization

ArXiv ID: 2512.01112 “View on arXiv”

Authors: Tarun Chitra

Abstract

Autodeleveraging (ADL) is a last-resort loss socialization mechanism for perpetual futures venues. It is triggered when solvency-preserving liquidations fail. Despite the dominance of perpetual futures in the crypto derivatives market, with over $60 trillion of volume in 2024, there has been no formal study of ADL. In this paper, we provide the first rigorous model of ADL. We prove that ADL mechanisms face a fundamental \emph{“trilemma”}: no policy can simultaneously satisfy exchange \emph{“solvency”}, \emph{“revenue”}, and \emph{“fairness”} to traders. This impossibility theorem implies that as participation scales, a novel form of \emph{“moral hazard”} grows asymptotically, rendering `zero-loss’ socialization impossible. Constructively, we show that three classes of ADL mechanisms can optimally navigate this trilemma to provide fairness, robustness to price shocks, and maximal exchange revenue. We analyze these mechanisms on the Hyperliquid dataset from October 10, 2025, when ADL was used repeatedly to close $2.1 billion of positions in 12 minutes. By comparing our ADL mechanisms to the standard approaches used in practice, we demonstrate empirically that Hyperliquid’s production queue overutilized ADL by $\approx 28\times$ relative to our optimal policy, imposing roughly $653 million of unnecessary haircuts on winning traders. This comparison also suggests that Binance overutilized ADL far more than Hyperliquid. Our results both theoretically and empirically demonstrate that optimized ADL mechanisms can dramatically reduce the loss of trader profits while maintaining exchange solvency.

Keywords: autodeleveraging, perpetual futures, trilemma, moral hazard, crypto derivatives

Complexity vs Empirical Score

• Math Complexity: 8.5/10
• Empirical Rigor: 7.0/10
• Quadrant: Holy Grail
• Why: The paper presents a formal impossibility theorem and optimal mechanism design with heavy theoretical modeling, fitting the high math complexity category, while also providing rigorous empirical analysis on a real-world dataset with quantitative results, fitting the high empirical rigor category.

  flowchart TD
    A["Research Goal: Rigorously Model & Optimize ADL"] --> B["Methodology: Formal Modeling & Trilemma Proof"]
    B --> C{"Can ADL satisfy Solvency, Revenue, & Fairness?"}
    C -- Trilemma Proof --> D["Impossibility Theorem: No 'Zero-Loss' Socialization"]
    C -- Constructive Analysis --> E["Optimization: Three Classes of Optimal ADL Mechanisms"]
    D --> F["Empirical Validation: Hyperliquid Oct 10, 2025 Dataset"]
    E --> F
    F --> G["Key Findings: Hyperliquid Overutilized ADL by ~28x<br/>(~$653M Unnecessary Haircuts)"]
    G --> H["Outcome: Optimized ADL Reduces Trader Losses & Maintains Solvency"]