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Autodeleveraging in Perpetual Futures: Solvency, Revenue, and Fairness

Article arXiv papers · Author: Tarun Chitra
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Summary

This paper models autodeleveraging (ADL), a mechanism perpetual futures venues use to socialize losses when liquidations cannot preserve solvency. Its central theoretical result is a trilemma: an ADL policy cannot simultaneously guarantee exchange solvency, exchange revenue, and trader fairness. The authors also identify a scaling moral hazard that makes loss-free socialization unattainable as participation grows. They describe three mechanism classes intended to navigate these competing objectives.

The empirical analysis examines Hyperliquid data from October 10, 2025, when ADL closed $2.1 billion in positions over 12 minutes. Against transparent benchmark allocations, the paper estimates that the production algorithm imposed $45.0 million to $51.7 million in excess profit haircuts, corresponding to about $653.6 million in positions closed. It also reports greater ADL use by Binance than Hyperliquid. These conclusions depend on the model and benchmark comparisons; the short description does not provide implementation details or establish how results generalize to other venues or events.

Key ideas

  • ADL is used when liquidations cannot preserve a perpetual futures venue’s solvency.
  • The model finds that solvency, revenue, and trader fairness cannot all be guaranteed together.
  • The paper describes three ADL mechanism classes for managing the trade-offs.
  • A comparison with benchmark allocations estimates excess profit haircuts in a Hyperliquid event.
  • The reported results depend on the model and allocation benchmarks used.

Tags

CryptoPerpetual futuresRisk managementMarket microstructure
Full text
# Autodeleveraging: Impossibilities and Optimization


# Autodeleveraging: Impossibilities and Optimization









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. On the positive side, 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 production ADL to transparent benchmark allocations, we find that Hyperliquid's production algorithm overshot the minimum trader profit haircut required to cover the shortfall. Our methodology suggests the excess profits lost by profitable traders is between \$45.0M and \$51.7M. In terms of the positions liquidated, this corresponds to roughly \$653.6M of positions being closed. This comparison also suggests that Binance overutilized ADL far more than Hyperliquid. Our results show both theoretically and empirically that optimized ADL mechanisms can dramatically reduce losses of trader profitability while maintaining exchange solvency.

Shown in full with attribution under the source's licence. Licence: abstract CC0

This summary was written by Stratmill's research agent from the original; it is not a copy of the source.