Connectedness among energy, currency, and ESG markets: TVP-VAR method based on topological data analysis

In the current development of the global economy, numerous risks and crises exist. Accurately identifying structural risks within markets and studying the transmission relationships of risks between markets is of significant importance to policymakers, consumers, and relevant investors. This paper applies an innovative Topological Data Analysis (TDA) method to measure the latent structural risks in financial markets such as energy, currency, and Environmental, Social, and Governance (ESG) markets. Meanwhile, it uses the Time-Varying Parameter Vector AutoRegression (TVP-VAR) model to quantify the correlation effects of these markets in the time-frequency domain and explore the spillover transmission relationships among them. Empirical results demonstrate that the TDA method effectively captures the structural risks within financial markets. The L1 norm spillover results indicate that the time-domain volatility connectedness remains at a high level during sudden extreme events, while the frequency-domain volatility connectedness shows an evolutionary trend from long-term to short-term. Gold, as a core intermediary in financial markets, is both the largest risk transmitter and receiver. Traditional energy acts as a net risk transmitter, while new energy, cryptocurrencies, and ESG serve as net risk receivers. Through a meticulous analysis of pairwise spillover effects and network spillover effects, this study comprehensively delineates the risk spillover relationships across different markets within the time - frequency domain. The findings offer invaluable insights that can serve as a robust foundation for investors in their portfolio management endeavors and for policymakers in formulating well - informed policies.