Dynamic risk spillover in green financial markets: A wavelet frequency analysis from China

Abstract

This paper focuses on China's green stock market, green bond market, carbon trading market, and crude oil futures market, using return data from 2018 to 2024. By combing a time-varying parameter vector autoregression model (TVP-VAR) with the Diebold-Yilmaz spillover index, the research investigates the dynamic risk spillover effects in the green financial markets. The empirical results reveal the following findings: (i) There is cyclical risk transmission across green financial markets, significantly influenced by major risk events such as the pandemic and extreme climate occurrences. (ii) The green stock market exhibits the strongest risk contagion within the green financial system, with a greater capacity for transmitting risk than receiving it. (iii) From a multiscale frequency perspective, risk contagion pathways differ notably. Weekly data show heightened sensitivity, while monthly and quarterly data provide a more robust representation of long-term systemic risk. (iv) In the short term, market sentiment significantly impacts the overall spillover effect of risk in green financial markets while the long-term effect of the fear sentiment is more significant. This research makes several contributions: it expands the cross-market view of risk contagion in green finance; introduces wavelet analysis to capture the multiscale dynamics of financial risk spillovers; and assesses the influence of market sentiment and fear sentiment on total risk spillovers in green financial markets. The findings are highly relevant for improving the structure of green financial markets, advancing regulatory innovation in green finance, and mitigating systemic financial risks.