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Modeling cryptocurrencies volatility using GARCH models: a comparison based on Normal and Student's T-Error distribution

Author

Listed:
  • Shazia Salamat

    (Liaoning Technical University, China)

  • Niu Lixia

    (Liaoning Technical University, China)

  • Sobia Naseem

    (Liaoning Technical University, China)

  • Muhammad Mohsin

    (Liaoning Technical University, China)

  • Muhammad Zia-ur-Rehman

    (National Textile University, Pakistan)

  • Sajjad Ahmad Baig

    (National Textile University, Pakistan)

Abstract

This study measures the volatility of cryptocurrency by utilizing the symmetric (GARCH 1,1) and asymmetric (EGARCH, TGARCH, PGARCH) model of GARCH family using a daily database designated in different digital monetary standards. The results for an explicit set of currencies for entire period provide evidence of volatile nature of cryptocurrency and in most of the cases, the PGARCH is a better-fitted model with student’s t distribution. The findings show positive shocks heavily affected conditional volatility as a contrast with negative stuns. Those additional analyses can be provided further support their findings and worthwhile information for economic thespians who are engrossed in adding cryptocurrency to their equity portfolios or are snooping about the capabilities of cryptocurrency as a financial asset.

Suggested Citation

  • Shazia Salamat & Niu Lixia & Sobia Naseem & Muhammad Mohsin & Muhammad Zia-ur-Rehman & Sajjad Ahmad Baig, 2020. "Modeling cryptocurrencies volatility using GARCH models: a comparison based on Normal and Student's T-Error distribution," Entrepreneurship and Sustainability Issues, VsI Entrepreneurship and Sustainability Center, vol. 7(3), pages 1580-1596, March.
    • Handle: RePEc:ssi:jouesi:v:7:y:2020:i:3:p:1580-1596
    DOI: 10.9770/jesi.2020.7.3(11)
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    References listed on IDEAS

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    1. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    2. Kovačić, Zlatko, 2007. "Forecasting volatility: Evidence from the Macedonian stock exchange," MPRA Paper 5319, University Library of Munich, Germany.
    3. Shaw, Charles, 2018. "Conditional heteroskedasticity in crypto-asset returns," MPRA Paper 90437, University Library of Munich, Germany.
    4. Charles Shaw, 2018. "Conditional heteroskedasticity in crypto-asset returns," Papers 1804.07978, arXiv.org, revised Dec 2018.
    5. Nelson, Daniel B, 1991. "Conditional Heteroskedasticity in Asset Returns: A New Approach," Econometrica, Econometric Society, vol. 59(2), pages 347-370, March.
    6. Baur, Dirk G. & Dimpfl, Thomas, 2018. "Asymmetric volatility in cryptocurrencies," Economics Letters, Elsevier, vol. 173(C), pages 148-151.
    7. Tully, Edel & Lucey, Brian M., 2007. "A power GARCH examination of the gold market," Research in International Business and Finance, Elsevier, vol. 21(2), pages 316-325, June.
    8. Bouoiyour, Jamal & Selmi, Refk, 2015. "Bitcoin Price: Is it really that New Round of Volatility can be on way?," MPRA Paper 65580, University Library of Munich, Germany.
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    Cited by:

    1. Mohammad Arashi & Mohammad Mahdi Rounaghi, 2022. "Analysis of market efficiency and fractal feature of NASDAQ stock exchange: Time series modeling and forecasting of stock index using ARMA-GARCH model," Future Business Journal, Springer, vol. 8(1), pages 1-12, December.
    2. Zdravka Aljinović & Branka Marasović & Tea Šestanović, 2021. "Cryptocurrency Portfolio Selection—A Multicriteria Approach," Mathematics, MDPI, vol. 9(14), pages 1-21, July.
    3. Muhammad MOHSIN & Sobia NASEEM & Larisa IVAȘCU & Lucian-Ionel CIOCA & Muddassar SARFRAZ & Nicolae Cristian STĂNICĂ, 2021. "Gauging the Effect of Investor Sentiment on Cryptocurrency Market: An Analysis of Bitcoin Currency," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(4), pages 87-102, December.

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    More about this item

    Keywords

    cryptocurrency; GARCH models; normal distribution; student's T distribution;
    All these keywords.

    JEL classification:

    • B26 - Schools of Economic Thought and Methodology - - History of Economic Thought since 1925 - - - Financial Economics
    • C01 - Mathematical and Quantitative Methods - - General - - - Econometrics
    • C19 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Other

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