In recent years, frequent shocks associated with global uncertainty have increased market instability across economies and altered patterns of risk transmission in financial markets. This study investigates the transmission of volatility among market risk-aversion indices across countries and regions. We collected constituent stock data for representative stock indices in 18 major national and regional financial markets. Building on the flight-to-safety (FTS) methodology proposed by Ahmed (2023), we constructed an FTS risk-aversion index for each country or region. The indices distinguish between cyclical and defensive sectors and account for sectoral heterogeneity over the financial risk cycle to quantify risk-aversion sentiment in each market. Using the NETS (network estimation for time series) approach and quantile regression, we then examined how cross-market transmission of risk-aversion sentiment affects the volatility of each market’s risk-aversion index. The findings show that risk-aversion sentiment in most markets is positively affected by fluctuations in sentiment in other global markets, with stronger effects at higher quantiles. Dynamic analysis further indicates that sudden events influence the extent to which individual markets respond to the global transmission of risk-aversion sentiment. These findings can help market regulators implement sector-specific supervision and regulation and respond promptly to sudden shocks and extreme risks, thereby supporting market stability during periods of global geopolitical uncertainty.
Global public health emergencies, particularly, infectious disease outbreaks, are occurring with increasing frequency and, imposing multifaceted challenges on healthcare systems, including staff-scheduling imbalances, elevated infection risks, and substantial uncertainty in patient demand. To address these challenges, a multi-objective robust optimization model for cross-regional allocation of healthcare staff is developed in this study. The model explicitly accounts for demand uncertainty and infection-risk constraints while optimizing rescue efficiency, fairness, and penalties for unmet demand. Our results demonstrate that (1) introducing cross-regional coordination markedly improves resource-allocation efficiency; (2) incorporating infection-risk constraints prevents overestimation of workforce availability and provides practical benefits; (3) scheduling policies must adapt dynamically to evolving epidemic phases and policy objectives; and (4) under high demand uncertainty, the robust model substantially improves schedule stability and reliability. By incorporating infection risk as a key decision constraint, our framework provides quantitative and actionable guidance for the adaptive scheduling of healthcare staff during complex, rapidly evolving infectious disease outbreaks.
The Baltic Capesize Index (BCI) reflects freight rate fluctuations of major dry bulk commodities and is highly sensitive to emergency-induced shocks. These emergencies differ markedly in impact intensity and duration, and even emergencies within the same category can generate heterogeneous BCI responses that cannot be adequately captured by category-based classification or frequency-based analysis alone. In this paper, emergencies are further classified into significant events and important events. We develop an Ensemble Empirical Mode Decomposition (EEMD)-based event-oriented framework that extends decomposition beyond frequency-based dynamics, complementing existing studies by explicitly integrating event-response characterization. By applying Fine-to-coarse Reconstruction, symbolic sequence procedures, and definite integrals to quantify cumulative effects, the approach enables the measurement of feedback direction, feedback time, individual fluctuation period, and fluctuation amplitude based on the classification of significant events and important events, and it reveals how route-level freight rate responses are aggregated into index-level volatility. The study demonstrates the following findings: (1) The fluctuation of the BCI is primarily composed of short-term market fluctuations, which result from supply-demand imbalances and the impact of important events, significant events, and the intrinsic long-term trend. (2) The BCI exhibits shorter feedback times to significant events and negative feedback events. (3) The individual fluctuation periods of important events are shorter, averaging 34 trading days. (4) The BCI fluctuates more in low-frequency modes under significant events. These findings assist firms and government departments in managing the risks arising from BCI volatility while enhancing the BCI’s predictive and early-warning capacity, thereby contributing to economic security.
