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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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    References listed on IDEAS

    as
    1. Dharmaraja Selvamuthu & Vineet Kumar & Abhishek Mishra, 2019. "Indian stock market prediction using artificial neural networks on tick data," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 5(1), pages 1-12, December.
    2. Elie Bouri & Konstantinos Gkillas & Rangan Gupta & Christian Pierdzioch, 2021. "Forecasting Realized Volatility of Bitcoin: The Role of the Trade War," Computational Economics, Springer;Society for Computational Economics, vol. 57(1), pages 29-53, January.
    3. Moschini, GianCarlo & Myers, Robert J., 2002. "Testing for constant hedge ratios in commodity markets: a multivariate GARCH approach," Journal of Empirical Finance, Elsevier, vol. 9(5), pages 589-603, December.
    4. de Goeij, P. C. & Marquering, W., 2004. "Modeling the conditional covariance between stock and bond returns : A multivariate GARCH approach," Other publications TiSEM 94fe5ada-715a-4339-b94c-f, Tilburg University, School of Economics and Management.
    5. Glosten, Lawrence R & Jagannathan, Ravi & Runkle, David E, 1993. "On the Relation between the Expected Value and the Volatility of the Nominal Excess Return on Stocks," Journal of Finance, American Finance Association, vol. 48(5), pages 1779-1801, December.
    6. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    7. Khaled Mokni & Elie Bouri & Ahdi Noomen Ajmi & Xuan Vinh Vo, 2021. "Does Bitcoin Hedge Categorical Economic Uncertainty? A Quantile Analysis," SAGE Open, , vol. 11(2), pages 21582440211, May.
    8. Peter de Goeij, 2004. "Modeling the Conditional Covariance Between Stock and Bond Returns: A Multivariate GARCH Approach," Journal of Financial Econometrics, Oxford University Press, vol. 2(4), pages 531-564.
    9. Abdulnasser Hatemi-J & Mohamed A. Hajji & Elie Bouri & Rangan Gupta, 2022. "The Benefits of Diversification Between Bitcoin, Bonds, Equities and the US Dollar: A Matter of Portfolio Construction," Asia-Pacific Journal of Operational Research (APJOR), World Scientific Publishing Co. Pte. Ltd., vol. 39(04), pages 1-11, August.
    10. Tao Ma & R. A. Serota, 2013. "A Model for Stock Returns and Volatility," Papers 1305.4173, arXiv.org.
    11. Borri, Nicola, 2019. "Conditional tail-risk in cryptocurrency markets," Journal of Empirical Finance, Elsevier, vol. 50(C), pages 1-19.
    12. Engle, Robert F, 1982. "Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation," Econometrica, Econometric Society, vol. 50(4), pages 987-1007, July.
    13. Yukun Liu & Aleh Tsyvinski, 2018. "Risks and Returns of Cryptocurrency," NBER Working Papers 24877, National Bureau of Economic Research, Inc.
    14. Hedayati , Amin & Hedayati , Moein & Esfandyari, Morteza, 2016. "Stock market index prediction using artificial neural network," Journal of Economics, Finance and Administrative Science, Universidad ESAN, vol. 21(41), pages 89-93.
    15. Bouri, Elie & Gupta, Rangan, 2021. "Predicting Bitcoin returns: Comparing the roles of newspaper- and internet search-based measures of uncertainty," Finance Research Letters, Elsevier, vol. 38(C).
    16. Cheikh, Nidhaleddine Ben & Zaied, Younes Ben & Chevallier, Julien, 2020. "Asymmetric volatility in cryptocurrency markets: New evidence from smooth transition GARCH models," Finance Research Letters, Elsevier, vol. 35(C).
    17. Wenjun Feng & Yiming Wang & Zhengjun Zhang, 2018. "Can cryptocurrencies be a safe haven: a tail risk perspective analysis," Applied Economics, Taylor & Francis Journals, vol. 50(44), pages 4745-4762, September.
    18. Mokni, Khaled & Ajmi, Ahdi Noomen & Bouri, Elie & Vo, Xuan Vinh, 2020. "Economic policy uncertainty and the Bitcoin-US stock nexus," Journal of Multinational Financial Management, Elsevier, vol. 57.
    19. Unknown, 1986. "Letters," Choices: The Magazine of Food, Farm, and Resource Issues, Agricultural and Applied Economics Association, vol. 1(4), pages 1-9.
    20. Dima Alberg & Haim Shalit & Rami Yosef, 2008. "Estimating stock market volatility using asymmetric GARCH models," Applied Financial Economics, Taylor & Francis Journals, vol. 18(15), pages 1201-1208.
    21. Vasilios Plakandaras & Elie Bouri & Rangan Gupta, 2019. "Forecasting Bitcoin Returns: Is there a Role for the U.S. – China Trade War?," Working Papers 201980, University of Pretoria, Department of Economics.
    22. Leandro Maciel, 2013. "A Hybrid Fuzzy GJR-GARCH Modeling Approach for Stock Market Volatility Forecasting," Palgrave Macmillan Books, in: Jonathan A. Batten & Peter MacKay & Niklas Wagner (ed.), Advances in Financial Risk Management, chapter 11, pages 253-283, Palgrave Macmillan.
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    Cited by:

    1. Ayush Singh & Anshu K. Jha & Amit N. Kumar, 2024. "Prediction of Cryptocurrency Prices through a Path Dependent Monte Carlo Simulation," Papers 2405.12988, arXiv.org.
    2. 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.
    3. 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).
    4. Brini, Alessio & Lenz, Jimmie, 2022. "Assessing the resiliency of investors against cryptocurrency market crashes through the leverage effect," Economics Letters, Elsevier, vol. 220(C).
    5. 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).
    6. Alessio Brini & Jimmie Lenz, 2024. "A comparison of cryptocurrency volatility-benchmarking new and mature asset classes," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 10(1), pages 1-38, December.
    7. Alessio Brini & Jimmie Lenz, 2024. "A Comparison of Cryptocurrency Volatility-benchmarking New and Mature Asset Classes," Papers 2404.04962, arXiv.org.
    8. 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.

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