Detecting Stablecoin Failure with Simple Thresholds and Panel Binary Models: The Pivotal Role of Lagged Market Capitalization and Volatility

In this study, we extend research on stablecoin credit risk by introducing a novel rule-of-thumb approach to determine whether a stablecoin is ``dead" or ``alive" based on a simple price threshold. Using a comprehensive dataset of 98 stablecoins, we classify a coin as failed if its price falls below a predefined threshold (e.g., \$0.80), validated through sensitivity analysis against established benchmarks such as CoinMarketCap delistings and \cite{feder2018rise} methodology. We employ a wide range of panel binary models to forecast stablecoins' probabilities of default (PDs), incorporating stablecoin-specific regressors. Our findings indicate that panel Cauchit models with fixed effects outperform other models across different definitions of stablecoin failure, while lagged average monthly market capitalization and lagged stablecoin volatility emerge as the most significant predictors—outweighing macroeconomic and policy-related variables. Random forest models complement our analysis, confirming the robustness of these key drivers. This approach not only enhances the predictive accuracy of stablecoin PDs but also provides a practical, interpretable framework for regulators and investors to assess stablecoin stability based on credit risk dynamics.