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Cryptocurrency Trading and Downside Risk

Author

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  • Farhat Iqbal

    (Department of Mathematics, College of Science, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 31441, Saudi Arabia
    Basic and Applied Scientific Research Center, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 31441, Saudi Arabia)

  • Mamoona Zahid

    (Department of Statistics, University of Balochistan, Quetta 87300, Pakistan)

  • Dimitrios Koutmos

    (Department of Accounting, Finance, and Business Law, College of Business, Texas A&M University-Corpus Christi, Corpus Christi, TX 78412, USA)

Abstract

Since the debut of cryptocurrencies, particularly Bitcoin, in 2009, cryptocurrency trading has grown in popularity among investors. Relative to other conventional asset classes, cryptocurrencies exhibit high volatility and, consequently, downside risk. While the prospects of high returns are alluring for investors and speculators, the downside risks are important to consider and model. As a result, the profitability of crypto market operations depends on the predictability of price volatility. Predictive models that can successfully explain volatility help to reduce downside risk. In this paper, we investigate the value-at-risk (VaR) forecasts using a variety of volatility models, including conditional autoregressive VaR (CAViaR) and dynamic quantile range (DQR) models, as well as GARCH-type and generalized autoregressive score (GAS) models. We apply these models to five of some of the largest market capitalization cryptocurrencies (Bitcoin, Ethereum, Ripple, Litecoin, and Steller, respectively). The forecasts are evaluated using various backtesting and model confidence set (MCS) techniques. To create the best VaR forecast model, a weighted aggregative technique is used. The findings demonstrate that the quantile-based models using a weighted average method have the best ability to anticipate the negative risks of cryptocurrencies.

Suggested Citation

  • Farhat Iqbal & Mamoona Zahid & Dimitrios Koutmos, 2023. "Cryptocurrency Trading and Downside Risk," Risks, MDPI, vol. 11(7), pages 1-18, July.
    • Handle: RePEc:gam:jrisks:v:11:y:2023:i:7:p:122-:d:1188460
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    References listed on IDEAS

    as
    1. Stock, James H. & Watson, Mark W., 1999. "Forecasting inflation," Journal of Monetary Economics, Elsevier, vol. 44(2), pages 293-335, October.
    2. Timothy King & Dimitrios Koutmos & Francesco Saverio Stentella Lopes, 2021. "Cryptocurrency Mining Protocols: A Regulatory and Technological Overview," Palgrave Studies in Financial Services Technology, in: Timothy King & Francesco Saverio Stentella Lopes & Abhishek Srivastav & Jonathan Williams (ed.), Disruptive Technology in Banking and Finance, edition 1, chapter 0, pages 93-134, Palgrave Macmillan.
    3. Pesaran, M. Hashem & Schleicher, Christoph & Zaffaroni, Paolo, 2009. "Model averaging in risk management with an application to futures markets," Journal of Empirical Finance, Elsevier, vol. 16(2), pages 280-305, March.
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    Cited by:

    1. Chengying He & Yong Li & Tianqi Wang & Salman Ali Shah, 2024. "Is cryptocurrency a hedging tool during economic policy uncertainty? An empirical investigation," Palgrave Communications, Palgrave Macmillan, vol. 11(1), pages 1-10, December.

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