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Correlations among cryptocurrencies: Evidence from multivariate factor stochastic volatility model

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  • Shi, Yongjing
  • Tiwari, Aviral Kumar
  • Gozgor, Giray
  • Lu, Zhou

Abstract

This paper is the first study to apply the multivariate factor stochastic volatility model (MFSVM) for analyzing the correlations among six cryptocurrencies. We use MFSVM with the Bayesian estimation procedure for the period from August 8, 2015, to January 1, 2020. According to the findings, there is a significant positive correlation between price volatility values of Bitcoin and Litecoin. Besides, the volatility values of Ethereum have a positive correlation with both Ripple and Stellar. There is also a positive correlation between the volatility values of Ripple and Dash. These findings are robust to consider different correlation networks. The evidence implies that Bitcoin is mainly related to Litecoin, but Ethereum is associated with other cryptocurrencies.

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  • Shi, Yongjing & Tiwari, Aviral Kumar & Gozgor, Giray & Lu, Zhou, 2020. "Correlations among cryptocurrencies: Evidence from multivariate factor stochastic volatility model," Research in International Business and Finance, Elsevier, vol. 53(C).
    • Handle: RePEc:eee:riibaf:v:53:y:2020:i:c:s0275531919311419
    DOI: 10.1016/j.ribaf.2020.101231
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    More about this item

    Keywords

    Price volatility of cryptocurrencies; Bitcoin; Ethereum; Factor stochastic volatility model; Bayesian estimations; Multivariate time-varying approach;
    All these keywords.

    JEL classification:

    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C38 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Classification Methdos; Cluster Analysis; Principal Components; Factor Analysis

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