Learning from Terra-Luna: A Simulation-Based Study on Stabilizing Algorithmic Stablecoins

Abstract

This paper investigates the dynamics behind the catastrophic collapse of the Terra-Luna ecosystem in May 2022, where the UST stablecoin de-pegged and lost nearly all its value. Using a custom-built simulation environment, we reproduce the free-market interactions and protocol mechanisms that triggered the crash, offering a new perspective on the vulnerabilities of algorithmic stablecoins based on the dual-token seigniorage model. Building on this analysis, we propose four stabilization strategies: two purely algorithmic mechanisms, designed to prevent de-pegging and mitigate token hyperinflation, and two hybrid approaches, introducing partial collateralization through USDT and BTC reserves. Our simulations show that these strategies significantly reduce collapse events under extreme market conditions, with the best improvements ranging from 60.6% to 95.8%. Additionally, we gained better control over LUNA’s circulating supply. Our simulation environment showcases how each proposal effectively mitigates systemic risks and enhances stability, thus providing valuable insights for designing future decentralized algorithmic stablecoins that can withstand market crises