Intraday spillovers in high-order moments among main cryptocurrency markets: the role of uncertainty indexes

Walid Mensi (), Anoop S. Kumar (), Hee-Un Ko () and Sang Hoon Kang ()
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Walid Mensi: Sultan Qaboos University
Anoop S. Kumar: Gulati Institute of Finance and Taxation
Hee-Un Ko: Jeonbuk State Institute
Sang Hoon Kang: Pusan National University

Eurasian Economic Review, 2024, vol. 14, issue 2, No 11, 507-538

Abstract: Abstract This study examines hourly realized volatility and high-order moments (realized kurtosis, realized skewness, and Jumps) spillovers among leading cryptocurrency markets (Bitcoin [BTC], Ethreum [ETH], Litecoin [LTC], Ripple [XRP], Bitcoin Cash [BCH]) using the time-varying parameter vector autoregression (TVP-VAR)-based connectedness method of (Antonakakis, N., & Gabauer, D., (2017). Refined Measures of Dynamic Connectedness Based On TVP-VAR. Technical Report. Munich: University Library of Munich.). Further, we investigate the impacts of uncertainty indices of stocks, gold, and oil on spillover size by employing a quantile regression framework. The results show that cryptocurrency connectedness increased during COVID-19 and returned to pre-pandemic levels once the stock markets recovered. BTC and XRP are net receivers of realized spillovers, whereas the remaining markets are net transmitters in the system. Under high-order moments, BTC is a net receiver of spillovers in Kurtosis and Jumps and shifts to a net contributor in kurtosis. ETH (XRP) is a net transmitter (receiver) of spillovers at high moments, except for jumps. LTC (BCH) is a net transmitter (receiver) of spillovers in the system, irrespective of high-order moments. From the hedging analysis, we document the hedging ability of the XRP against price fluctuations in BTC and ETH assets. Furthermore, quantile regression analysis reveals that cryptocurrency markets react asymmetrically to uncertainties during bullish and bearish regimes and exhibit potential hedge and safe haven properties.

Keywords: Cryptocurrencies; Spillovers in high moments; High frequency; Hedging (search for similar items in EconPapers)
JEL-codes: G14 G15 (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s40822-024-00263-1

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