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An Empirical Study of Volatility in Cryptocurrency Market

Author

Listed:
  • Hemendra Gupta

    (Department of Finance, Jaipuria Institute of Management, Lucknow 226010, India)

  • Rashmi Chaudhary

    (Department of Finance, Jaipuria Institute of Management, Lucknow 226010, India)

Abstract

Cryptocurrencies have gained a lot of attraction across the globe. Most observers of the cryptocurrency market will agree that crypto volatility is in a different league altogether. There has been a growing need to understand the nature of volatility in cryptocurrency. This paper analyzes the performance of four mostly traded, different cryptocurrencies in terms of their risk and return. The relationship between the return and returns volatility among different currencies has been examined considering the daily closing prices from 1 January 2017 to 30 June 2022, using the family of the GARCH model. The study has explored the spillover and asymmetric effect of volatility by using the DCC GARCH model and EGARCH model, respectively. The causal behavior among different cryptocurrencies has also been examined using Granger causality. There has been a strong spillover effect among different cryptocurrencies, Bitcoin and Ether, which are the top two cryptocurrencies with the highest market capitalization which have exhibited an asymmetric impact in their volatility as compared to the other two currencies, which are Litecoin and XRP.

Suggested Citation

  • Hemendra Gupta & Rashmi Chaudhary, 2022. "An Empirical Study of Volatility in Cryptocurrency Market," JRFM, MDPI, vol. 15(11), pages 1-14, November.
    • Handle: RePEc:gam:jjrfmx:v:15:y:2022:i:11:p:513-:d:963868
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    References listed on IDEAS

    as
    1. Corbet, Shaen & Meegan, Andrew & Larkin, Charles & Lucey, Brian & Yarovaya, Larisa, 2018. "Exploring the dynamic relationships between cryptocurrencies and other financial assets," Economics Letters, Elsevier, vol. 165(C), pages 28-34.
    2. Robert Engle, 2001. "GARCH 101: The Use of ARCH/GARCH Models in Applied Econometrics," Journal of Economic Perspectives, American Economic Association, vol. 15(4), pages 157-168, Fall.
    3. Abakah, Emmanuel Joel Aikins & Gil-Alana, Luis Alberiko & Madigu, Godfrey & Romero-Rojo, Fatima, 2020. "Volatility persistence in cryptocurrency markets under structural breaks," International Review of Economics & Finance, Elsevier, vol. 69(C), pages 680-691.
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    Cited by:

    1. Vladyslav Koltun & Ivan P. Yamshchikov, 2023. "Pump It: Twitter Sentiment Analysis for Cryptocurrency Price Prediction," Risks, MDPI, vol. 11(9), pages 1-14, September.
    2. Mukhia, Kundan & Rai, Anish & Luwang, S.R. & Nurujjaman, Md & Majhi, Sushovan & Hens, Chittaranjan, 2024. "Complex network analysis of cryptocurrency market during crashes," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 653(C).
    3. Hemendra Gupta & Rashmi Chaudhary, 2023. "An Analysis of Volatility and Risk-Adjusted Returns of ESG Indices in Developed and Emerging Economies," Risks, MDPI, vol. 11(10), pages 1-18, October.
    4. Kundan Mukhia & Anish Rai & SR Luwang & Md Nurujjaman & Sushovan Majhi & Chittaranjan Hens, 2024. "Complex network analysis of cryptocurrency market during crashes," Papers 2405.05642, arXiv.org.

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