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Crypto Exchanges and Credit Risk: Modeling and Forecasting the Probability of Closure

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  • Dean Fantazzini

    (Moscow School of Economics, Moscow State University, Leninskie Gory, 1, Building 61, 119992 Moscow, Russia)

  • Raffaella Calabrese

    (Business School, University of Edinburgh, 29 Buccleuch Place, Edinburgh EH8 9JS, UK)

Abstract

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.

Suggested Citation

  • Dean Fantazzini & Raffaella Calabrese, 2021. "Crypto Exchanges and Credit Risk: Modeling and Forecasting the Probability of Closure," JRFM, MDPI, vol. 14(11), pages 1-23, October.
    • Handle: RePEc:gam:jjrfmx:v:14:y:2021:i:11:p:516-:d:666046
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    Citations

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    Cited by:

    1. Dean Fantazzini, 2022. "Crypto-Coins and Credit Risk: Modelling and Forecasting Their Probability of Death," JRFM, MDPI, vol. 15(7), pages 1-34, July.
    2. Fatih Ecer & Tolga Murat & Hasan Dinçer & Serhat Yüksel, 2024. "A fuzzy BWM and MARCOS integrated framework with Heronian function for evaluating cryptocurrency exchanges: a case study of Türkiye," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 10(1), pages 1-29, December.
    3. Fantazzini, Dean, 2023. "Assessing the Credit Risk of Crypto-Assets Using Daily Range Volatility Models," MPRA Paper 117141, University Library of Munich, Germany.
    4. Vittorio Astarita, 2023. "Risks and opportunities in arbitrage and market-making in blockchain-based currency markets. Part 1 : Risks," Papers 2304.08590, arXiv.org.
    5. Yang, Zixiu & Fantazzini, Dean, 2022. "Using crypto assets pricing methods to build technical oscillators for short-term bitcoin trading," MPRA Paper 115508, University Library of Munich, Germany.
    6. Korobova, Elena & Fantazzini, Dean, 2024. "Stablecoins and credit risk: when do they stop being stable?," MPRA Paper 122951, University Library of Munich, Germany.

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    More about this item

    Keywords

    exchange; Bitcoin; crypto assets; cryptocurrencies; credit risk; bankruptcy; default probability;
    All these keywords.

    JEL classification:

    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C35 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • G23 - Financial Economics - - Financial Institutions and Services - - - Non-bank Financial Institutions; Financial Instruments; Institutional Investors
    • G32 - Financial Economics - - Corporate Finance and Governance - - - Financing Policy; Financial Risk and Risk Management; Capital and Ownership Structure; Value of Firms; Goodwill
    • G33 - Financial Economics - - Corporate Finance and Governance - - - Bankruptcy; Liquidation

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