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A novel heavy tail distribution of logarithmic returns of cryptocurrencies

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  • Tran, Quang Van
  • Kukal, Jaromir

Abstract

We propose a novel distribution derived from the generalized gamma distribution by symmetrization and regularization around the mean. Besides location and scale parameters, the distribution has three shape parameters with many sub-models as special cases. Its parameters can be estimated by non-linear regression with parameter significance verification and sub-model testing. The applicability of this family of novel distributions is verified on returns of three cryptocurrencies and its suitability is tested by χ2 goodness of fit testing. The obtained results show that this novel distribution and its sub-models can be viable candidates for modeling the returns of cryptocurrencies.

Suggested Citation

  • Tran, Quang Van & Kukal, Jaromir, 2022. "A novel heavy tail distribution of logarithmic returns of cryptocurrencies," Finance Research Letters, Elsevier, vol. 47(PA).
    • Handle: RePEc:eee:finlet:v:47:y:2022:i:pa:s1544612321005250
    DOI: 10.1016/j.frl.2021.102574
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    References listed on IDEAS

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    More about this item

    Keywords

    Generalized gamma distribution; Regularization; Parameter estimation; Logarithmic returns; Cryptocurrencies;
    All these keywords.

    JEL classification:

    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics

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