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Realized higher-order moments spillovers across cryptocurrencies

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  • Apergis, Nicholas

Abstract

Using 1-min data of nine cryptocurrency prices, spanning the period 2017 to 2021, the analysis extends Hasan et al. (2021) and Ahmed and Al Mafrachi (2021) papers that explore the dynamic spillovers connectedness of returns and realized moments, including realized volatility, realized skewness, and realized kurtosis, via a time-varying parameter vector autoregression (TVP-VAR) connectedness approach. Our study improves it by offering a larger set of cryptocurrencies, as well as new evidence on the mechanisms that can explain the presence of connectedness. Moreover, our new findings document that spillover effects intensify during shock periods, such as the ‘COVID-19′ pandemic, a fact that the above papers did not consider. Higher-order moment spillovers contain additional information that cannot be observed from return and realized volatility spillovers. Shocks from the cryptocurrency market identify different submitters and recipients across the cryptocurrencies under study. Furthermore, the analysis through quantile regressions illustrate that spillovers are generally affected by a number of factors within the cryptocurrency markets, as well as the COVID pandemic, depending on what tail point of the distribution we are. The findings are of significant importance for investors, portfolio managers, regulators and policymakers who should be aware of the impact of shocks within those markets on the dynamics of spillovers for the sake of investment decisions and financial stability.

Suggested Citation

  • Apergis, Nicholas, 2023. "Realized higher-order moments spillovers across cryptocurrencies," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 85(C).
  • Handle: RePEc:eee:intfin:v:85:y:2023:i:c:s1042443123000318
    DOI: 10.1016/j.intfin.2023.101763
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    More about this item

    Keywords

    Cryptocurrency returns; High-frequency data; TVP-VAR spillovers; Higher-order moments spillovers;
    All these keywords.

    JEL classification:

    • C10 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - General
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • G01 - Financial Economics - - General - - - Financial Crises
    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets

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