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Higher moment connectedness of cryptocurrencies: a time-frequency approach

Author

Listed:
  • Kingstone Nyakurukwa

    (University of the Witwatersrand)

  • Yudhvir Seetharam

    (University of the Witwatersrand)

Abstract

The purpose of the study is to examine higher moment connectedness among 12 cryptocurrencies using data sampled at the 1-minute high-frequency interval. We use methods that demonstrate the heterogeneity of agents from their distinct investing horizons. This includes wavelet multiple cross-correlations, CEEMDAN-based Diebold-Yilmaz (DY) connectedness index and the Barunik-Krehlik (BK) frequency connectedness index. First, our results show that higher moment multiple correlations among the sampled cryptocurrencies are higher at all time scales and the relationship strengthens at lower frequencies. Second, the wavelet cross-correlations show different cryptocurrencies with the potential to lead and lag in the transmission of higher moment shocks to the whole system at different frequencies. Again, the multiple wavelet cross-correlations increase with increasing time scales. The results from the CEEMDAN-based DY connectedness index as well as the BK framework also reveal cyclical connectedness and differences in connectedness across different frequencies. The results show more connectedness of higher moments than the connectedness empirically reported for returns and volatility. Cryptocurrency connectedness has mostly been examined using the first two moments. We extend this line of literature by examining the third and fourth moments, which might be more useful for risk management purposes.

Suggested Citation

  • Kingstone Nyakurukwa & Yudhvir Seetharam, 2023. "Higher moment connectedness of cryptocurrencies: a time-frequency approach," Journal of Economics and Finance, Springer;Academy of Economics and Finance, vol. 47(3), pages 793-814, September.
  • Handle: RePEc:spr:jecfin:v:47:y:2023:i:3:d:10.1007_s12197-023-09627-w
    DOI: 10.1007/s12197-023-09627-w
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    More about this item

    Keywords

    Realised skewness; Realised kurtosis; High-frequency data; Risk spillovers;
    All these keywords.

    JEL classification:

    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)
    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets

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