Cryptocurrency Trading and Downside Risk

– 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.

– Investigates value-at-risk (VaR) forecasts using various volatility models
– Demonstrates quantile-based models have best ability to anticipate negative risks of cryptocurrencies

The paper discusses the downside risk of cryptocurrency trading and the importance of choosing precise models to estimate this risk. It does not provide specific details about crypto trading strategies or techniques.

– Volatility models including CAViaR, DQR, GARCH-type, and GAS models
– Weighted aggregative technique for creating the best VaR forecast model

– 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.