Risk spillovers among China's conventional energy, new energy and technology stock markets: A novel mixed distorted Copula method

This study proposes a novel framework for mixed distorted Copulas, developing four types of mixed distorted Copula models (MDC-PHDP, MDC-DPPH, MDC-PHPH, and MDC-DPDP) based on the PH distortion function and its dual form. The proposed model incorporates heterogeneous distortion parameters and time-varying parameter evolution equations, thereby addressing the limitations of traditional Copula models from the dual perspectives of distortion mapping and dynamic mixing. Utilizing CoVaR and delta CoVaR metrics, this research examines the dynamic risk spillover relationships among China's new energy, conventional energy, and technology markets. Empirical results demonstrate that the time-varying mixed distorted Copula model (TVP-MDC) exhibits superior fitting performance. The TVP-MDC model effectively captures the nonlinear, asymmetric, and time-varying risk spillover effects between the three markets during both upward and downward market phases. Furthermore, the model identifies the distinct characteristics of risk spillovers across the pre-, during-, and post- COVID-19 pandemic periods. The findings of this study reveal significant systemic risks among the three markets, with the most pronounced increase in risk spillovers between the technology and new energy markets during the pandemic. This study provides a novel dynamic risk assessment tool for investors and policymakers, emphasizing the importance of supporting the new energy and technology industries during crises.