Autodeleveraging: Impossibilities and Optimization

2025-11-30

AI Summary

This paper identifies an inherent trilemma—among solvency, exchange revenue, and trader fairness—in the Automated Deleveraging (ADL) mechanism of perpetual futures markets, formally modeling and rigorously proving its fundamental trade-offs for the first time. Leveraging game-theoretic and optimization-based analysis, we propose three provably optimal ADL mechanisms that preserve exchange solvency while significantly enhancing fairness. Empirical validation using real-world Hyperliquid trading data demonstrates that, compared to current production strategies, our mechanisms reduce ADL abuse by approximately 8× and prevent over-liquidation of profitable positions worth $630 million. Furthermore, we uncover a progressive escalation of moral hazard induced by scale expansion. This work establishes the first theoretical foundation and practical optimization framework for designing risk-control mechanisms in decentralized derivatives markets.

Technology Category

• Multiagent Systems: Mechanism Design
• Game Theory and Economic Paradigms: Mechanism Design
• Search and Optimization: Adversarial Search

Application Category

• Economics, Online Markets and Human Computation: Fairness and ethical considerations in crowd work and in human-in-the-loop AI systems
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• User Modeling, Personalization and Recommendation: Accountability, Transparency, and Ethics for personalization

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 approximately $8 imes$ relative to our optimal policy, imposing roughly $630 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.

Problem

• Model autodeleveraging mechanisms for perpetual futures exchanges.
• Prove a trilemma between solvency, revenue, and fairness in ADL.
• Optimize ADL to reduce trader losses while ensuring exchange solvency.

Innovation

• First rigorous model of autodeleveraging mechanisms
• Optimal policies navigate solvency, revenue, fairness trilemma
• Empirical validation reduces unnecessary trader haircuts dramatically