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Cryptocurrencies and Tokens Lifetime Analysis from 2009 to 2021

Author

Listed:
  • Paul Gatabazi

    (Department of Mathematics and Applied Mathematics, Faculty of Sciences, University of Johannesburg, Johannesburg 2006, South Africa
    Department of Applied Statistics, School of Economics, College of Business and Economics, University of Rwanda, Huye 124, Rwanda)

  • Gaëtan Kabera

    (Department of Statistics, University of South Africa—UNISA, Pretoria 0003, South Africa)

  • Jules Clement Mba

    (School of Economics, College of Business and Economics, University of Johannesburg, Johannesburg 2006, South Africa)

  • Edson Pindza

    (Department of Mathematics and Statistics, Tshwane University of Technology, Pretoria 0001, South Africa
    Department of Mathematics and Applied Mathematics, University of Pretoria, Pretoria 0002, South Africa)

  • Sileshi Fanta Melesse

    (School of Mathematics, Statistics and Computer Science, University of KwaZulu Natal, Pietermaritzburg 3209, South Africa)

Abstract

The success of Bitcoin has spurred emergence of countless alternative coins with some of them shutting down only few weeks after their inception, thus disappearing with millions of dollars collected from enthusiast investors through initial coin offering (ICO) process. This has led investors from the general population to the institutional ones, to become skeptical in venturing in the cryptocurrency market, adding to its highly volatile characteristic. It is then of vital interest to investigate the life span of available coins and tokens, and to evaluate their level of survivability. This will make investors more knowledgeable and hence build their confidence in hazarding in the cryptocurrency market. Survival analysis approach is well suited to provide the needed information. In this study, we discuss the survival outcomes of coins and tokens from the first release of a cryptocurrency in 2009. Non-parametric methods of time-to-event analysis namely Aalen Additive Hazards Model (AAHM) trough counting and martingale processes, Cox Proportional Hazard Model (CPHM) are based on six covariates of interest. Proportional hazards assumption (PHA) is checked by assessing the Kaplan-Meier estimates of survival functions at the levels of each covariate. The results in different regression models display significant and non-significant covariates, relative risks and standard errors. Among the results, it was found that cryptocurrencies under standalone blockchain were at a relatively higher risk of collapsing. It was also found that the 2013–2017 cryptocurrencies release was at a high risk as compared to 2009–2013 release and that cryptocurrencies for which headquarters are known had the relatively better survival outcomes. This provides clear indicators to watch out for while selecting the coins or tokens in which to invest.

Suggested Citation

  • Paul Gatabazi & Gaëtan Kabera & Jules Clement Mba & Edson Pindza & Sileshi Fanta Melesse, 2022. "Cryptocurrencies and Tokens Lifetime Analysis from 2009 to 2021," Economies, MDPI, vol. 10(3), pages 1-14, March.
    • Handle: RePEc:gam:jecomi:v:10:y:2022:i:3:p:60-:d:767689
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    References listed on IDEAS

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    Cited by:

    1. Muneer M. Alshater & Mayank Joshipura & Rim El Khoury & Nohade Nasrallah, 2023. "Initial Coin Offerings: a Hybrid Empirical Review," Small Business Economics, Springer, vol. 61(3), pages 891-908, October.
    2. P. Gatabazi & J. C. Mba & E. Pindza, 2022. "Grey Verhulst model and its chaotic behaviour with application to Bitcoin adoption," Decisions in Economics and Finance, Springer;Associazione per la Matematica, vol. 45(1), pages 327-341, June.
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    5. Dean Fantazzini & Elena Korobova, 2025. "Stablecoins and credit risk: when do they stop being stable?," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 77, pages 46-73.

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