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Volatility in the Cryptocurrency Market

Author

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  • Jinan Liu

    (University of Calgary)

  • Apostolos Serletis

    (University of Calgary)

Abstract

How do cryptocurrency prices evolve? Is there any interdependence among cryptocurrency returns and/or volatilities? Are there any return spillovers and volatility spillovers between the cryptocurrency market and other financial markets? To answer these questions, we use GARCH-in-mean models to examine the relationship between volatility and returns of leading cryptocurrencies, to investigate spillovers within the cryptocurrency market, and also from the cryptocurrency market to other financial markets. Overall, we find statistically significant transmission of shocks and volatilities among the leading cryptocurrencies. We also find statistically significant spillover effects from the cryptocurrency market to other financial markets in the United States, as well as in other leading economies (Germany, the United Kingdom, and Japan).

Suggested Citation

  • Jinan Liu & Apostolos Serletis, 2019. "Volatility in the Cryptocurrency Market," Open Economies Review, Springer, vol. 30(4), pages 779-811, September.
    • Handle: RePEc:kap:openec:v:30:y:2019:i:4:d:10.1007_s11079-019-09547-5
    DOI: 10.1007/s11079-019-09547-5
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    Cited by:

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    2. De Pace, Pierangelo & Rao, Jayant, 2023. "Comovement and instability in cryptocurrency markets," International Review of Economics & Finance, Elsevier, vol. 83(C), pages 173-200.
    3. Larisa V. Sannikova, 2022. "Risks of Using Cryptoassets in Russia and the Potential for Mitigation," Finansovyj žhurnal — Financial Journal, Financial Research Institute, Moscow 125375, Russia, issue 6, pages 124-138, December.
    4. Bouri, Elie & Vo, Xuan Vinh & Saeed, Tareq, 2021. "Return equicorrelation in the cryptocurrency market: Analysis and determinants," Finance Research Letters, Elsevier, vol. 38(C).
    5. Jens Klose, 2022. "Comparing cryptocurrencies and gold - a system-GARCH-approach," Eurasian Economic Review, Springer;Eurasia Business and Economics Society, vol. 12(4), pages 653-679, December.
    6. Darko Vukovic & Moinak Maiti & Zoran Grubisic & Elena M. Grigorieva & Michael Frömmel, 2021. "COVID-19 Pandemic: Is the Crypto Market a Safe Haven? The Impact of the First Wave," Sustainability, MDPI, vol. 13(15), pages 1-17, July.
    7. B M, Lithin & chakraborty, Suman & iyer, Vishwanathan & M N, Nikhil & ledwani, Sanket, 2022. "Modeling asymmetric sovereign bond yield volatility with univariate GARCH models: Evidence from India," MPRA Paper 117067, University Library of Munich, Germany, revised 05 Jan 2023.
    8. Qiao, Xingzhi & Zhu, Huiming & Hau, Liya, 2020. "Time-frequency co-movement of cryptocurrency return and volatility: Evidence from wavelet coherence analysis," International Review of Financial Analysis, Elsevier, vol. 71(C).
    9. Ramit Sawhney & Shivam Agarwal & Vivek Mittal & Paolo Rosso & Vikram Nanda & Sudheer Chava, 2022. "Cryptocurrency Bubble Detection: A New Stock Market Dataset, Financial Task & Hyperbolic Models," Papers 2206.06320, arXiv.org.
    10. Ameen Omar Shareef & K.P. Prabheesh, 2022. "Does International Monetary Policy Influence The Bank Risk? Evidence From India," Bulletin of Monetary Economics and Banking, Bank Indonesia, vol. 25(2), pages 135-154, August.
    11. D’Amato, Valeria & Levantesi, Susanna & Piscopo, Gabriella, 2022. "Deep learning in predicting cryptocurrency volatility," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 596(C).
    12. Bojaj, Martin M. & Muhadinovic, Milica & Bracanovic, Andrej & Mihailovic, Andrej & Radulovic, Mladen & Jolicic, Ivan & Milosevic, Igor & Milacic, Veselin, 2022. "Forecasting macroeconomic effects of stablecoin adoption: A Bayesian approach," Economic Modelling, Elsevier, vol. 109(C).
    13. Vasu Kalariya & Pushpendra Parmar & Patel Jay & Sudeep Tanwar & Maria Simona Raboaca & Fayez Alqahtani & Amr Tolba & Bogdan-Constantin Neagu, 2022. "Stochastic Neural Networks-Based Algorithmic Trading for the Cryptocurrency Market," Mathematics, MDPI, vol. 10(9), pages 1-15, April.
    14. Nicolás Magner & Nicolás Hardy, 2022. "Cryptocurrency Forecasting: More Evidence of the Meese-Rogoff Puzzle," Mathematics, MDPI, vol. 10(13), pages 1-27, July.
    15. Fung, Kennard & Jeong, Jiin & Pereira, Javier, 2022. "More to cryptos than bitcoin: A GARCH modelling of heterogeneous cryptocurrencies," Finance Research Letters, Elsevier, vol. 47(PA).
    16. Jinan Liu & Sajjadur Rahman & Apostolos Serletis, 2021. "Cryptocurrency shocks," Manchester School, University of Manchester, vol. 89(2), pages 190-202, March.
    17. Prashant Joshi & Jinghua Wang & Michael Busler, 2022. "A Study of the Machine Learning Approach and the MGARCH-BEKK Model in Volatility Transmission," JRFM, MDPI, vol. 15(3), pages 1-9, March.
    18. Urom, Christian & Abid, Ilyes & Guesmi, Khaled & Chevallier, Julien, 2020. "Quantile spillovers and dependence between Bitcoin, equities and strategic commodities," Economic Modelling, Elsevier, vol. 93(C), pages 230-258.
    19. Alessio Brini & Jimmie Lenz, 2024. "A Comparison of Cryptocurrency Volatility-benchmarking New and Mature Asset Classes," Papers 2404.04962, arXiv.org.
    20. Apostolos Ampountolas, 2022. "Cryptocurrencies Intraday High-Frequency Volatility Spillover Effects Using Univariate and Multivariate GARCH Models," IJFS, MDPI, vol. 10(3), pages 1-22, July.
    21. Ivanovski, Kris & Hailemariam, Abebe, 2023. "Forecasting the stock-cryptocurrency relationship: Evidence from a dynamic GAS model," International Review of Economics & Finance, Elsevier, vol. 86(C), pages 97-111.
    22. Onur Özdemir, 2022. "Cue the volatility spillover in the cryptocurrency markets during the COVID-19 pandemic: evidence from DCC-GARCH and wavelet analysis," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 8(1), pages 1-38, December.
    23. Novi Maryaningsih & Suahasil Nazara & Febrio N. Kacaribu & Solikin M. Juhro, 2022. "Central Bank Digital Currency: What Factors Determine Its Adoption?," Bulletin of Monetary Economics and Banking, Bank Indonesia, vol. 25(1), pages 1-24, June.

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    More about this item

    Keywords

    Cryptocurrency; Financial markets; Spillover effects; GARCH-in-mean model; Asymmetric BEKK model; Volatility transmission;
    All these keywords.

    JEL classification:

    • 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
    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets
    • G32 - Financial Economics - - Corporate Finance and Governance - - - Financing Policy; Financial Risk and Risk Management; Capital and Ownership Structure; Value of Firms; Goodwill

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