High-frequency return and volatility spillovers among cryptocurrencies

ABSTRACT

We examine the high-frequency return and volatility of major cryptocurrencies and reveal that spillovers among them exist. Our analysis shows that return and volatility clustering structures are distinct among different cryptocurrencies, suggesting that return and volatility might have different spillover patterns. Further investigation via minimal spanning trees points out that BTC, LTC and ETH are the most relevant cryptocurrencies in general, serving as connection hubs for linking many other cryptocurrencies. However, their role is challenged lately, potentially due to the increased usage of other cryptocurrencies in time.

KEYWORDS:

• Cryptocurrency
• bitcoin
• network
• spillover
• minimal spanning tree
• hierarchical clustering
• machine learning

Disclosure statement

No potential conflict of interest was reported by the authors.

Notes

¹ See Gandal et al. (2018) which identified the impact of suspicious trading activity on the Mt.Gox Bitcoin exchange theft.

² For recent studies that use Kaiko as the data provider, see Makarov and Schoar (2020); Akyildirim et al. (2020a); Aslan and Sensoy (2020); Mensi et al. (2019).

³ https://coinmarketcap.com/historical/20,180,623/

⁴ We follow Diebold and Yilmaz (2012), Diebold and Yilmaz (2014), and Barunik and Krehlik (2018). We use the $R$ packages written by Krehlik (2020), Nicholson, Matteson, and Bien (2019) and Csardi and Nepusz (2006).

⁵ We have $\frac{N^2-N}{2}$ net pairwise directional connectedness measures.:

⁶ We test our procedure for different lag specifications, using 1,3,5 and 10 lags, and results remain qualitatively the same (Kang and Lee (2019))

⁷ This is calculated as the total spillover from each asset divided by the total connectivity measure

Additional information

Funding

This work was supported by the Conselho Nacional de Desenvolvimento Científico e Tecnológico [425123/2018-9,308171/2019-5,310541/2018-2,408546/2018-2]; Türkiye Bilimler Akademisi.