A Theoretical Model of Price Volatility Transmission between Cryptocurrency Markets and Renewable Energy Stock Indices

This paper proposes a theoretical framework to model the price volatility transmission between cryptocurrency markets and the renewable energy stock sector. We develop a novel Factor-Augmented Dynamic Conditional Correlation GARCH (FA-DCC-GARCH) model, which extends the standard multivariate GARCH approach by incorporating observable, time-varying factors that represent core transmission mechanisms. This provides a structural blueprint for future empirical investigation. The model posits that volatility transmission is driven by three primary channels: (1) the Energy Consumption link from crypto mining; (2) a shared Investment Sentiment and Diversification channel reflecting investor risk appetite; and (3) a Policy and Regulatory channel for exogenous shocks. Our framework predicts asymmetric volatility transmission, with stronger spillovers from crypto to renewables during periods of high uncertainty.By deconstructing the spillover effects, the model offers a nuanced understanding beyond purely empirical studies and provides a robust set of testable hypotheses for assessing the time-varying risks and diversification benefits between these critical markets.