Therefore, it follows that the differences between credit and market risk for crypto-currencies are of quantitative and temporal nature, not qualitative because, if the financial losses and the technical problems are small, then we have a market event whereas, if the financial losses are too big and the technical problems cannot be solved, then we have a credit event and the crypto-currency “dies” ( Fantazzini and Zimin ( 2020)). In addition, a large amount of literature showed that market and credit risk are driven by the same economic factors see the special issue on the interaction of market and credit risk in the Journal of Banking and Finance in 2010 for more details. However, the Basel Committee on Banking Supervision ( 2009) highlighted that “ the securitization trend in the last decade has diminished the scope for differences in measuring market and credit risk, as securitization transforms the latter into the former” ( Basel Committee on Banking Supervision ( 2009), p. In traditional finance, credit risk is defined as the gains and losses on a position or portfolio associated with the fulfillment (or not) of contractual obligations, while market risk is the gains and losses on the value of a position or portfolio that can take place due to the movements in market prices (such as exchange rates, commodity prices, interest rates, etc.), see Basel Committee on Banking Supervision ( 2009), Hartmann ( 2010) and references therein for more details. Even though there is still some marginal trading for the coins defined as dead according to these rules, this is not a problem but an advantage, because we can analyze them before they go into permanent (digital) oblivion.
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( 2018) and Schmitz and Hoffmann ( 2020) to detect dead coins, or the simple professional rule that defines a coin as dead if its value drops below 1 cent. Therefore, it makes more sense to employ the methods proposed by Feder et al. It is for this reason that Grobys and Sapkota ( 2020) and Fantazzini and Zimin ( 2020) were forced to use small coin datasets in their analyses and to employ a limited set of variables to forecast these dead coins. In simple terms, when a coin name is inserted in these repositories, it is too late to gain any valuable information for credit risk modelling and forecasting. Unfortunately, the information set for the vast majority of these coins does not exist anymore because their technical information and historical market data are no longer available. The past literature and professional practice highlighted that the dead coins collected in well-known online repositories such as or are indeed dead, but this fact represents (paradoxically) a problem. These results also held after a set of robustness checks that considered different time samples and the coins’ market capitalization. In general, we found that the cauchit and the zero-price-probability (ZPP) based on the random walk or the Markov Switching-GARCH(1,1) were the best models for newly established coins, whereas credit-scoring models and machine-learning methods using lagged trading volumes and online searches were better choices for older coins. However, this choice was not critical, and the best models turned out to be the same in most cases. We found that the choice of the coin-death definition affected the set of the best forecasting models to compute the probability of death.
We employed different definitions of dead coins, ranging from academic literature to professional practice alternative forecasting models, ranging from credit scoring models to machine learning and time-series-based models and different forecasting horizons. This paper examined a set of over two thousand crypto-coins observed between 20 to estimate their credit risk by computing their probability of death.
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