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

Abstract

Highlights: What are the main findings? A simple price threshold of 0.80 is introduced as a novel and robust real-time indicator for stablecoin failure, validated against benchmarks like CoinMarketCap delistings and volume-based methods. Lagged monthly market capitalization and stablecoin volatility are identified as the most significant predictors of default. These coin-specific drivers consistently outperform macroeconomic factors, and the panel Cauchit model with fixed effects (Cauchit FE) delivers the best out-of-sample forecasting performance. What is the implication of the main findings? Investors and risk managers gain a practical, interpretable framework to assess stability. The 0.80 threshold can be used as a real-time signal to reassess risk or exit positions, while low volatility and high market capitalization serve as key indicators of a stablecoin’s resilience. Regulators can use the proposed threshold and forecasting models to monitor stablecoin stability and potential systemic risks in the DeFi market. The findings also imply that oversight should prioritize stablecoin-specific factors like reserve quality and transparency over broader macroeconomic controls. 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 Feder et al. (2018) 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.