Enhancing cryptocurrency market volatility forecasting with daily dynamic tuning strategy

Abstract

This study proposes a novel parameter tuning strategy, daily dynamic tuning, and applies it to forecast volatility in the cryptocurrency market. Comparative analysis with HAR-RV and machine learning models based on fixed parameters demonstrates the superior predictive performance of the dynamic tuning-based model. These findings are corroborated in robustness tests. Using interpretable tools for machine learning model explanations, this study delves further into the driving factors behind the predictive performance of the dynamic tuning-based model. The results indicate that hash rate and the effective federal funds rate play significant roles among various forecasting factors. Further categorization of the forecasting factors reveals that blockchain indicators and macroeconomic indicators are more useful for predictions, while market indicators are relatively less informative. Moreover, a time-varying analysis of variable importance scores suggests a significant increase in the importance score of blockchain indicators from October 2022 onwards, coinciding with the EU passing landmark legislation on digital assets in October 2022. Finally, an economic value analysis indicates that the dynamic tuning-based model can achieve higher economic returns in practice compared to traditional fixed parameter models. These findings are significant for enhancing cryptocurrency market risk prediction and management and exploring the potential applications of machine learning models in volatility forecasting.