This paper takes Zhejiang's carbon financial market as its research subject

employing a combined VAR and Copula model methodology to conduct an in-depth study on the theoretical framework and practical pathways for market risk measurement and management. Using daily transaction data from Zhejiang's carbon trading market between 2015 and 2022

the study first models carbon price return series with ARMA-GARCH models to capture volatility characteristics and standard residual sequences. Subsequently

the R Vine Copula model is applied to capture dependencies between Zhejiang's carbon market and other related markets. Bai-Perron tests identify structural break points

enabling phase-specific analysis of the data. To evaluate model validity

backtesting was conducted comparing model results with actual data at a uniform confidence level. Findings indicate the VAR-Copula model effectively captures the time-varying risk characteristics and dependencies within the carbon financial market. For risk management

this study integrates VAR and ES indicators from the model to provide early warnings and management recommendations for future risks in Zhejiang's carbon market. The study reveals significant high-risk volatility in Zhejiang's carbon financial market

which exhibits strong interconnections with domestic energy markets and macroeconomic conditions. Finally

by identifying key influencing factors

this paper proposes risk prevention strategies and policy recommendations for Zhejiang's carbon financial market

aiming to promote its healthy and sustainable development. This research provides a systematic risk assessment and management framework for Zhejiang's carbon market while offering reference points for risk studies in other regional carbon financial markets.