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GJR-GARCH Volatility Modeling under NIG and ANN for Predicting Top Cryptocurrencies

Author

Listed:
  • Fahad Mostafa

    (Department of Mathematics and Statistics, Texas Tech University, Lubbock, TX 79409, USA)

  • Pritam Saha

    (Rawls College of Business, Texas Tech University, Lubbock, TX 79409, USA)

  • Mohammad Rafiqul Islam

    (Department of Mathematics, Florida State University, Tallahassee, FL 32306, USA)

  • Nguyet Nguyen

    (Department of Mathematics and Statistics, Youngstown State University, Youngstown, OH 44555, USA)

Abstract

Cryptocurrencies are currently traded worldwide, with hundreds of different currencies in existence and even more on the way. This study implements some statistical and machine learning approaches for cryptocurrency investments. First, we implement GJR-GARCH over the GARCH model to estimate the volatility of ten popular cryptocurrencies based on market capitalization: Bitcoin, Bitcoin Cash, Bitcoin SV, Chainlink, EOS, Ethereum, Litecoin, TETHER, Tezos, and XRP. Then, we use Monte Carlo simulations to generate the conditional variance of the cryptocurrencies using the GJR-GARCH model, and calculate the value at risk (VaR) of the simulations. We also estimate the tail-risk using VaR backtesting. Finally, we use an artificial neural network ( ANN ) for predicting the prices of the ten cryptocurrencies. The graphical analysis and mean square errors ( MSEs ) from the ANN models confirmed that the predicted prices are close to the market prices. For some cryptocurrencies, the ANN models perform better than traditional ARIMA models.

Suggested Citation

  • Fahad Mostafa & Pritam Saha & Mohammad Rafiqul Islam & Nguyet Nguyen, 2021. "GJR-GARCH Volatility Modeling under NIG and ANN for Predicting Top Cryptocurrencies," JRFM, MDPI, vol. 14(9), pages 1-22, September.
    • Handle: RePEc:gam:jjrfmx:v:14:y:2021:i:9:p:421-:d:628582
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    Cited by:

    1. Umar, Zaghum & Usman, Muhammad & Choi, Sun-Yong & Rice, John, 2023. "Diversification benefits of NFTs for conventional asset investors: Evidence from CoVaR with higher moments and optimal hedge ratios," Research in International Business and Finance, Elsevier, vol. 65(C).
    2. Chou, Ke-Hsin & Day, Min-Yuh & Chiu, Chien-Liang, 2023. "Do bitcoin news information flow and return volatility fit the sequential information arrival hypothesis and the mixture of distribution hypothesis?," International Review of Economics & Finance, Elsevier, vol. 88(C), pages 365-385.
    3. Prof. Reepu & Prof.Bijesh Dhyani & Ms. Ayushi & Dr. Sudhi Sharma & Dr. Manish Kumar, 2022. "Predictive Modelling Of Select Cryptocurrencies And Identifying The Best Suitable Model - With Reference To Arima And Anns," Annals - Economy Series, Constantin Brancusi University, Faculty of Economics, vol. 6, pages 11-19, December.
    4. Rico-Peña, Juan Jesús & Arguedas-Sanz, Raquel & López-Martin, Carmen, 2023. "Models used to characterise blockchain features. A systematic literature review and bibliometric analysis," Technovation, Elsevier, vol. 123(C).
    5. Brini, Alessio & Lenz, Jimmie, 2022. "Assessing the resiliency of investors against cryptocurrency market crashes through the leverage effect," Economics Letters, Elsevier, vol. 220(C).

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