Autodeleveraging: Impossibilities and Optimization (arxiv.org)

A recent study has introduced a rigorous model of Autodeleveraging (ADL), a critical mechanism in the crypto derivatives market that serves as a last-resort method for managing losses, particularly when traditional liquidations fail. With over $60 trillion in trading volume in 2024, understanding ADL is paramount for the crypto community. The research uncovers a fundamental trilemma where no ADL policy can simultaneously ensure exchange solvency, maintain revenue, and provide fairness to traders. This came in light of empirical evidence from the Hyperliquid platform, where ADL was over-utilized, leading to significant unnecessary losses for traders. The implications of this research are substantial as it highlights the moral hazards and inefficiencies associated with existing ADL practices. By proposing three optimized ADL mechanisms that can effectively navigate this trilemma, the study aims to enhance fairness and profitability for traders while preserving the financial health of exchanges. The analysis showed that optimizing ADL could reduce potential losses by approximately $653 million, emphasizing the need for innovative strategies that balance the competing demands within the rapidly evolving landscape of crypto trading. This work not only advances the theoretical framework of ADL but also opens avenues for practical improvements in trading platforms.