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Quantifying the spillover effect in the cryptocurrency market

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  • Moratis, George

Abstract

The study quantifies the spillover effects in the cryptocurrency market using a rolling-window Bayesian Vector Autoregressive Model. The present study offers a better understanding of the interconnectedness and the shock transmission in the cryptocurrency market, as it quantifies spillover risk at the pairwise directional level, offering a dynamic understanding of the shock fluctuation within the market which in turn uncovers periods of risk integration. In addition, the study investigates the determinants of the spillover shocks in the cryptocurrency market, revealing the increasing connections to external drivers over time.

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  • Moratis, George, 2021. "Quantifying the spillover effect in the cryptocurrency market," Finance Research Letters, Elsevier, vol. 38(C).
    • Handle: RePEc:eee:finlet:v:38:y:2021:i:c:s1544612319304787
    DOI: 10.1016/j.frl.2020.101534
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    Cited by:

    1. Lee A. Smales, 2021. "Volatility Spillovers among Cryptocurrencies," JRFM, MDPI, vol. 14(10), pages 1-12, October.
    2. Giannellis, Nikolaos, 2022. "Cryptocurrency market connectedness in Covid-19 days and the role of Twitter: Evidence from a smooth transition regression model," Research in International Business and Finance, Elsevier, vol. 63(C).
    3. Shan Wu, 2021. "Co-movement and return spillover: evidence from Bitcoin and traditional assets," SN Business & Economics, Springer, vol. 1(10), pages 1-16, October.
    4. Mensi, Walid & El Khoury, Rim & Ali, Syed Riaz Mahmood & Vo, Xuan Vinh & Kang, Sang Hoon, 2023. "Quantile dependencies and connectedness between the gold and cryptocurrency markets: Effects of the COVID-19 crisis," Research in International Business and Finance, Elsevier, vol. 65(C).
    5. Hasan, Mudassar & Naeem, Muhammad Abubakr & Arif, Muhammad & Yarovaya, Larisa, 2021. "Higher moment connectedness in cryptocurrency market," Journal of Behavioral and Experimental Finance, Elsevier, vol. 32(C).
    6. 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).
    7. Kumar, Ashish & Iqbal, Najaf & Mitra, Subrata Kumar & Kristoufek, Ladislav & Bouri, Elie, 2022. "Connectedness among major cryptocurrencies in standard times and during the COVID-19 outbreak," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 77(C).
    8. Li, Shi, 2022. "Spillovers between Bitcoin and Meme stocks," Finance Research Letters, Elsevier, vol. 50(C).
    9. Al-Shboul, Mohammad & Assaf, Ata & Mokni, Khaled, 2023. "Does economic policy uncertainty drive the dynamic spillover among traditional currencies and cryptocurrencies? The role of the COVID-19 pandemic," Research in International Business and Finance, Elsevier, vol. 64(C).
    10. Riccardo Blasis & Luca Galati & Alexander Webb & Robert I. Webb, 2023. "Intelligent design: stablecoins (in)stability and collateral during market turbulence," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 9(1), pages 1-23, December.
    11. Li, Xingyi & Gan, Kai & Zhou, Qi, 2023. "Dynamic volatility connectedness among cryptocurrencies and China's financial assets in standard times and during the COVID-19 pandemic," Finance Research Letters, Elsevier, vol. 51(C).
    12. Lee, Seungju & Lee, Jaewook & Lee, Yunyoung, 2023. "Dissecting the Terra-LUNA crash: Evidence from the spillover effect and information flow," Finance Research Letters, Elsevier, vol. 53(C).
    13. Jinxin Cui & Aktham Maghyereh, 2022. "Time–frequency co-movement and risk connectedness among cryptocurrencies: new evidence from the higher-order moments before and during the COVID-19 pandemic," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 8(1), pages 1-56, December.
    14. Dowling, Michael, 2022. "Is non-fungible token pricing driven by cryptocurrencies?," Finance Research Letters, Elsevier, vol. 44(C).
    15. Timoth'ee Fabre & Ioane Muni Toke, 2024. "Neural Hawkes: Non-Parametric Estimation in High Dimension and Causality Analysis in Cryptocurrency Markets," Papers 2401.09361, arXiv.org, revised Jan 2024.
    16. Lennart Ante, 2022. "The Non-Fungible Token (NFT) Market and Its Relationship with Bitcoin and Ethereum," FinTech, MDPI, vol. 1(3), pages 1-9, June.
    17. Binh Nguyen Thanh & Thai Nguyen Vu Hong & Huy Pham & Thanh Nguyen Cong & Thu Pham Thi Anh, 2023. "Are the stabilities of stablecoins connected?," Economia e Politica Industriale: Journal of Industrial and Business Economics, Springer;Associazione Amici di Economia e Politica Industriale, vol. 50(3), pages 515-525, September.
    18. Cao, Guangxi & Xie, Wenhao, 2022. "Asymmetric dynamic spillover effect between cryptocurrency and China's financial market: Evidence from TVP-VAR based connectedness approach," Finance Research Letters, Elsevier, vol. 49(C).
    19. Abubakr Naeem, Muhammad & Iqbal, Najaf & Lucey, Brian M. & Karim, Sitara, 2022. "Good versus bad information transmission in the cryptocurrency market: Evidence from high-frequency data," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 81(C).
    20. Umar, Zaghum & Gubareva, Mariya & Teplova, Tamara & Tran, Dang K., 2022. "Covid-19 impact on NFTs and major asset classes interrelations: Insights from the wavelet coherence analysis," Finance Research Letters, Elsevier, vol. 47(PB).
    21. Umar, Zaghum & Polat, Onur & Choi, Sun-Yong & Teplova, Tamara, 2022. "Dynamic connectedness between non-fungible tokens, decentralized finance, and conventional financial assets in a time-frequency framework," Pacific-Basin Finance Journal, Elsevier, vol. 76(C).
    22. Kanis Saengchote, 2022. "Cryptocurrency bubbles, the wealth effect, and non-fungible token prices: Evidence from metaverse LAND," Papers 2209.04385, arXiv.org.

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    More about this item

    Keywords

    Cryptocurrencies; Bitcoin; Spillover Risk; Connectedness; Bayesian VAR;
    All these keywords.

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • E6 - Macroeconomics and Monetary Economics - - Macroeconomic Policy, Macroeconomic Aspects of Public Finance, and General Outlook
    • F3 - International Economics - - International Finance
    • G1 - Financial Economics - - General Financial Markets

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