Assessing the Credit Risk of Crypto-Assets Using Daily Range Volatility Models

In this paper, we analyzed a dataset of over 2000 crypto-assets to assess their credit risk by computing probability death using the daily range. Unlike conventional low-frequency volatility models that only utilize close-to-close prices, range incorporates all information provided in traditional datasets, including open-high-low-close (OHLC) prices for each asset. We evaluated accuracy estimated with against various forecasting models, scoring machine learning and time-series-based models. Our study considered different definitions “dead coins” horizons. results indicate methods incorporating lagged trading volumes online searches were best short-term horizons up 30 days. Conversely, time-series more appropriate longer term forecasts, one year. Additionally, our analysis revealed signaled, far advance, weakened position crypto derivatives platform FTX, which filed Chapter 11 bankruptcy protection United States on November 2022.