Crypto-exchanges and Credit Risk: Modelling and Forecasting the Probability of Closure

While there is an increasing interest in crypto-assets, the credit risk of these exchanges is still relatively unexplored. To fill this gap, we consider a unique data set on 144 exchanges active from the first quarter of 2018 to the first quarter of 2021. We analyze the determinants of the decision of closing an exchange using credit scoring and machine learning techniques. The cybersecurity grades, having a public developer team, the age of the exchange, and the number of available traded cryptocurrencies are the main significant covariates across different model specifications. Both in-sample and out-of-sample analyses confirm these findings. These results are robust to the inclusion of additional variables considering the country of registration of these exchanges and whether they are centralized or decentralized.