Cryptocurrency Trading and Downside Risk

DOI: 10.3390/risks11070122

ABSTRACT: Since the debut of cryptocurrencies, particularly Bitcoin, in 2009, cryptocurrency 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 market operations depends on the predictability of price volatility. Predictive 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 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.

– Quantile-based models have the best ability to anticipate negative risks of cryptocurrencies.
– Weighted average method is used to create the best VaR forecast model.

– Quantile-based models have the best ability to anticipate negative risks of cryptocurrencies.
– Weighted average method is used to create the best VaR forecast model.

– Quantile-based models have the best ability to anticipate negative risks of cryptocurrencies.
– Weighted average method is used to create the best VaR forecast model.

Methods used:

– The paper investigates value-at-risk (VaR) forecasts for cryptocurrencies.
– Quantile-based models using a weighted average method have the best ability to anticipate negative risks.

– Quantile models outperform GARCH, EGARCH, GJR, and GAS models.
– GARCH, EGARCH, and GJR models pass the LR uc and LR cc tests.

– Cryptocurrency trading has grown in popularity among investors.
– Volatility models help reduce downside risk.

– Cryptocurrency trading has grown in popularity among investors.
– Cryptocurrencies exhibit high volatility and downside risk.

In this paper , the authors 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 autoregression score (GAS) models.

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