A New Cluster-Based Universal Model for Cryptocurrency Volatility Forecasting Using Deep Learning Regressors

Abstract

The cryptocurrency evolution series has piqued the interest of many researchers seeking to understand the underlying factors that drive evolution and predict future values, yielding useful insights across social and economic fields. The paper introduces a new methodology implementing a cluster-based universal framework to forecast daily cryptocurrency realized volatility from exogenous inputs. The mathematical models include internal and external determinants, and, in the proposed universal model, the input set is further augmented with time-series data on realized cryptocurrency volatility based on the hierarchical Dynamic Time Warping (DTW) clustering dendrogram. The forecasting stage is performed by the regressors in the Deep Learning (DL) class. The performance of the models is assessed using Mean Accuracy Percentage Error (MAPE), Root Mean Squared Error (RMSE), and Mean Directional Accuracy (MDA). The final stage of the proposed methodology is the model interpretation carried out by the Gradient Explainer technique. The comprehensive experimental analysis in our work shows that the universal model trained on a time series from the same class reliably outperforms the other models. In addition, the results obtained are better than those reported for other universal approaches in the literature.