Modelling Asymmetric Volatility and Sentiment Effects: Forecasting Accuracy in the Crypto Market

This study examines the ability of asymmetric GARCH-family models, specifically EGARCH and GJR-GARCH, to capture and forecast the volatility of major decentralized cryptocurrencies. We analyzed the returns of seven leading assets (BTC, ETH, ADA, XRP, LTC, XLM, DASH). We used the Crypto Fear & Greed Index (CFGI) as a dummy variable, covering a period when all cryptocurrencies were active simultaneously. Notably, the Student-t distribution provided the best in-sample results with the lowest AIC and BIC for both models. When comparing the models directly, EGARCH consistently outperforms GJR-GARCH across in-sample metrics. The use of the CFGI dummy variable marginally improves in-sample results for only three of the seven cryptocurrencies, suggesting it may be adding noise to the models for some coins. Additionally, there is no clear rule of asymmetry across all cryptocurrencies, suggesting a fundamental structural difference from the traditional stock market. Out-of-sample metrics and performance vary more than in-sample metrics, with normal and GJR-GARCH models yielding better performance and lower QLIKE values for specific cryptocurrencies. This study contributes to the growing literature on volatility modeling and forecasting in cryptocurrencies, highlighting the importance of asset-specific valuation in the cryptocurrency market. It also provides a framework for integrating specific market indicators into the modeling framework.