Artikel

Crypto exchanges and credit risk: Modeling and forecasting the probability of closure

While there is increasing interest in crypto assets, the credit risk of these exchanges is still relatively unexplored. To fill this gap, we considered a unique dataset of 144 exchanges, active from the first quarter of 2018 to the first quarter of 2021. We analyzed the determinants surrounding the decision to close an exchange using credit scoring and machine learning techniques. 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 analyzes confirm these findings. These results are robust in regard to the inclusion of additional variables, considering the country of registration of these exchanges and whether they are centralized or decentralized.

Language
Englisch

Bibliographic citation
Journal: Journal of Risk and Financial Management ; ISSN: 1911-8074 ; Volume: 14 ; Year: 2021 ; Issue: 11 ; Pages: 1-23 ; Basel: MDPI

Classification
Wirtschaft
Single Equation Models; Single Variables: Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions
Multiple or Simultaneous Equation Models: Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions
Model Construction and Estimation
Forecasting Models; Simulation Methods
Pension Funds; Non-bank Financial Institutions; Financial Instruments; Institutional Investors
Financing Policy; Financial Risk and Risk Management; Capital and Ownership Structure; Value of Firms; Goodwill
Bankruptcy; Liquidation
Subject
exchange
Bitcoin
crypto assets
cryptocurrencies
credit risk
bankruptcy
default probability

Event
Geistige Schöpfung
(who)
Fantazzini, Dean
Calabrese, Raffaella
Event
Veröffentlichung
(who)
MDPI
(where)
Basel
(when)
2021

DOI
doi:10.3390/jrfm14110516
Handle
Last update
10.03.2025, 11:42 AM CET

Data provider

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ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. If you have any questions about the object, please contact the data provider.

Object type

  • Artikel

Associated

  • Fantazzini, Dean
  • Calabrese, Raffaella
  • MDPI

Time of origin

  • 2021

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