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Volatility Spillovers among the Cryptocurrency Time Series

Author

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  • Zouheir Mighri

    (Department of Finance and Insurance, College of Business, University of Jeddah, Saudi Arabia)

  • Majid Ibrahim Alsaggaf

    (Department of Finance and Insurance, College of Business, University of Jeddah, Saudi Arabia)

Abstract

This paper uses different multivariate GARCH models to model conditional correlations and analyze the volatility spillovers between cryptocurrency time series. The dynamic conditional correlation GARCH model is found to fit the data the best. Our empirical results are fourfold. First, on average, a $1 long position in BitShares (BTS) can be hedged for 15% with a short position in MonaCoin (MONA), while a $1 long position in MONA can be hedged for 14% with a short position in Ripple (XRP). Second, the average weight for the BTS/MONA portfolio is 0.48, indicating that for a $1 portfolio, 48% should be invested in BTS and 52% invested in MONA. Third, the average weight for the BTS/XRP portfolio indicates that 27% should be invested in BTS and 73 % invested in XRP. Finally, the average weight for the MONA/XRP portfolio indicates that 33% should be invested in MONA and 67% invested in XRP.

Suggested Citation

  • Zouheir Mighri & Majid Ibrahim Alsaggaf, 2019. "Volatility Spillovers among the Cryptocurrency Time Series," International Journal of Economics and Financial Issues, Econjournals, vol. 9(3), pages 81-90.
    • Handle: RePEc:eco:journ1:2019-03-7
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    Cited by:

    1. Balcilar, Mehmet & Ozdemir, Huseyin & Agan, Busra, 2022. "Effects of COVID-19 on cryptocurrency and emerging market connectedness: Empirical evidence from quantile, frequency, and lasso networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 604(C).
    2. Cong Gu & Benfu Lv & Ying Liu & Geng Peng, 2021. "The Impact of Quantitative Easing on Cryptocurrency," International Journal of Economics and Financial Issues, Econjournals, vol. 11(4), pages 27-34.

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    More about this item

    Keywords

    Cryptocurrencies; Multivariate GARCH; Volatility spillover; Hedging; Portfolio designs.;
    All these keywords.

    JEL classification:

    • C5 - Mathematical and Quantitative Methods - - Econometric Modeling
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • 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
    • G1 - Financial Economics - - General Financial Markets

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