Cryptocurrency Trading and Downside Risk

Farhat Iqbal, Mamoona Zahid and Dimitrios Koutmos ()
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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
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

Risks, 2023, vol. 11, issue 7, 1-18

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.

Keywords: cryptocurrencies; downside risk; VaR models; weighted aggregative approach (search for similar items in EconPapers)
JEL-codes: C G0 G1 G2 G3 K2 M2 M4 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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