The increasing institutional participation and deepening integration of physical trading and financial operations in commodity markets have elevated the interconnectedness of energy futures and equity markets to prominence in both scholarly discourse and industry analysis. Employing the Nelson-Siegel framework and Fama-French factor model, this study examines the dynamic relationships between energy futures holding cost variations and equity returns across coal and oil sectors. Our analysis yields three principal findings: First, the Fama-French three-factor model exhibits robust explanatory power in China's energy sector equity market, revealing significant statistical relationships between holding cost curve parameters—level, slope, and curvature—and industry excess returns. Second, holding cost variations manifest substantial heterogeneity in their impact on stock returns across coal and oil sectors. Third, carrying cost components demonstrate dominance over shock transmission effects in explaining industry stock return volatility, indicating complex, asymmetric interaction mechanisms between futures and equity markets. Drawing from these empirical results, we advance targeted policy prescriptions addressing futures market architecture and financial stability.

This study examines how global turbulence factors shape volatility spillovers among emerging stock markets through the lens of risk cycles. We find that emerging market connectedness exhibits clear regime heterogeneity across risk cycles, while also preserving several persistent structural patterns. Specifically, trade policy uncertainty (TPU) and economic policy uncertainty (EPU) serve the dominant drivers during risk outbreak and risk accumulation periods, respectively. Meanwhile, sustainability uncertainty (ESGUI) consistently plays a leading driver role in both regimes, while physical climate risk plays a comparatively limited role. Furthermore, the effects of these core turbulence factors are nonlinear and threshold-dependent, highlighting the importance of accounting for risk cycle heterogeneity and nonlinear dynamics when assessing emerging market risk transmission.

This paper examines biodiversity risk transmission to US agricultural futures markets. We find: (1) all futures exhibit moderate-to-high biodiversity sensitivity, with coffee showing highest response through transparent price transmission mechanisms; (2) wavelet analysis reveals time-frequency heterogeneity, where tropical crops maintain strong long-term synchronization with biodiversity risk, intensified during COVID-19; (3) frequency-dependent asymmetric correlations emerge, with grains shifting from positive long-cycle to negative short-cycle correlations; (4) systemic spillover analysis indicates moderate interdependence, with soybeans as primary risk receiver and sugar as dominant transmitter, revealing differentiated transmission roles.

We employ a time-varying parameter vector autoregression (TVP-VAR) joint connectedness approach to study the dynamic risk spillover effects between cryptocurrencies and China’s financial market, further exploring the impact of cryptocurrencies on China’s financial market. Our results show that there is asymmetric risk transmission between cryptocurrencies and China’s financial market, and the risk spillover effect is very weak. Specifically, the spillover of cryptocurrencies to China’s financial market is significantly stronger than the spillover of China’s financial market to cryptocurrencies. Cryptocurrencies have a stronger spillover effect to China’s exchange rate and gold. The net spillover effect of cryptocurrencies is weakening over time. Overall, the return spillover impact of cryptocurrencies on China’s financial market is greater than the volatility spillover impact, and the degree of impact of different cryptocurrencies is heterogeneous. This study provides some reference and guidance for cross-market investment portfolios and the regulation of China’s financial market.

As the world’s two largest economies, China and the US are currently experiencing political and economic friction. This conflict brings high uncertainty to financial markets. Assessing risk spillover effects in a sector level will help us to characterize international risk contagions. We construct a multilayer network to examine tail risk spillovers between China and the US and find that (1) the value of total connectedness rises amidst tensions but declines during reconciliations; (2) interlayer spillovers mainly manifest as extreme pulses instead of steady outflows, which implies a significant increase in the frequency and magnitude of interlayer spillovers requires vigilant monitoring; and (3) compared with the in-strength, the out-strength is more concentrated, which represents that some sectors may play the role of major interlayer transmitter in tail risk spillovers. Monitoring interlayer spillovers helps policymakers and investors respond to emerging systemic threats.

We investigate the dynamic extremal connectedness of sectors within the Chinese stock market conditional on exogenous extreme risk through multivariate extreme value regression. To proxy the exogenous extreme risk, we independently consider market volatility-based measures and policy uncertainty-based measures. We discover that market volatility-based measures have a stronger influence than policy uncertainty-based measures on the extremal connectedness of sectors. The oil volatility index is the most influential on extremal connectedness, and the energy sector plays a direct role in transmitting exogenous extreme risk. Our findings provide new insights into understanding the drivers of systematic and idiosyncratic contagion.

Based on the connectedness time-frequency domain decomposition method are adopted in this paper. With the help of network topology for visualization, the characteristics and transmission path of financial risks in the time-frequency domain under major emergencies are studied. The results show that after the occurrence of MPEs, the level of risk spillover in China's financial market usually decreases in the short term, medium term and long term. When the policy has a long time lag or the market reaction is not timely, the medium term risk spillover will be higher than the short term risk spillover.

This paper examines return spillovers within and between different DeFi, cryptocurrency, stock and safe-haven assets. The results show that DeFi and cryptocurrency asset markets exhibit strong within-market and between-market return spillovers, that stock and safe-haven markets show weak connectedness, and that safe-haven assets are minor receivers and transmitters of between-market spillover effects. The connectedness between markets is time varying and reveals structural changes in early 2020. Furthermore, we document that financial conditions shape the dynamics of return spillover effects between markets.

In 2013, the Shanghai Futures Exchange (SHFE) introduced a night session in Chinese metal futures markets. Using high-frequency data of gold, silver, and copper futures, we investigate the impact of night trading on intraday return predictability in Chinese metal futures markets. Firstly, we find the intraday return predictability has changed after introducing night trading: before the launch of night trading, the first half-hour daytime returns show significant predictability, whereas the first half-hour night returns exhibit forecasting power after that. Such changes can be explained by the immediate reactions of domestic investors to international news released in the evening. Secondly, the market timing strategy outperforms the always-long and buy-and-hold benchmark strategies. Thirdly, the predictability of night return is stronger on days with higher volatility and volume. Furthermore, stronger intraday predictability is associated with global news releases and positive news sentiment, suggesting enhanced connectedness of Chinese and international metal futures markets after the launch of night trading.

This paper investigates the impact of the COVID-19 pandemic on the volatility spillover and dependence structure among the major developed and emerging stock markets. The TVP-VAR connectedness decomposition approach and R-vine copula are implemented in this research. The results of the TVP-VAR connectedness decomposition approach reveal that the volatility spillover among the major developed and emerging stock markets has been significantly strengthened by the outbreak of the COVID-19 pandemic, although it has gradually faded over time. In addition, during the pandemic, the UK, German, French and Canadian stock markets are the spillover transmitters, while the Japanese, Chinese Hong Kong, Chinese and Indian stock markets are the receivers. It is also found that the US and Brazilian stock markets have undergone role shifts after the outbreak of the COVID-19 pandemic. The results of the R-vine copula model indicate that during the pandemic, the Canadian, French, and Chinese Hong Kong stock markets are the most important financial centre in the American, European, and Asian stock markets, respectively. Furthermore, the effect of the extreme risk contagion has been strengthened by the pandemic, particularly the downside risk contagion.