The fundamental objective of this research is to learn how trade liberalization, male employment, urbanization, and foreign direct investment (FDI) affect women’s participation in the labor force. To continue, this study aims to determine the effects of trade and other factors on women’s employment in three distinct sectors (i.e., agriculture, industry, and service). From 1991 to 2021, we analyzed data from eight SAARC countries. The study’s theoretical foundation was the Cobb–Douglas production function. To better understand the connections between trade liberalization and the SAARC labor market, this paper used panel quantile regression (QR) and generalized method of moments (GMM) to empirically explore the key determinants of female employment in total and three sub-sectors. The QR method was used in the study because it looks at how variables affect each other beyond the data mean. Additionally, our data set does not follow a normal distribution, and the connection between the explained and explanatory factors is non-linear. Trade openness has a beneficial effect on total female employment throughout system GMM and all quartiles. Total female employment also benefits from an increase in GDP and FDI. However, women’s access to the workforce is hampered by urbanization. Many strategies for increasing women’s participation in the workforce across three sectors are addressed in this article. The major finding of this study is the rate of change in female employment across three industries. Women’s participation in the service and manufacturing sectors increases, whereas their participation in agriculture decreases, as a result of increased trade openness. Although these studies can assist policymakers in choosing the best feasible trade adjustments, they will also add to diverse academic and policy discussions on trade liberalization and its gender consequences. Since trade has become more accessible, more and more women are entering the workforce. Therefore, workers should acquire industrial and service-sector-related competencies.

The escalating frequency and severity of natural disasters present significant challenges to the stability and sustainability of global financial systems, with the US stock market being especially vulnerable. This study examines sector-level exposure and contagion dynamics during climate-related disaster events, providing insights essential for sustainable investing and resilient financial planning. Using an advanced econometric framework—dynamic conditional correlation GARCH (DCC-GARCH) augmented with Generalized Hyperbolic Processes (GHPs) and an asymmetric specification (ADCC-GARCH)—we model daily stock returns for 20 publicly traded US companies across five sectors (insurance, energy, automotive, retail, and industrial) between 2017 and 2022. The results reveal considerable sectoral heterogeneity: insurance and energy sectors exhibit the highest vulnerability, with heavy-tailed return distributions and persistent volatility, whereas retail and selected industrial firms demonstrate resilience, including counter-cyclical behavior during crises. GHP-based models improve tail risk estimation by capturing return asymmetries, skewness, and leptokurtosis beyond Gaussian specifications. Moreover, the ADCC-GHP-GARCH framework shows that negative shocks induce more persistent correlation shifts than positive ones, highlighting asymmetric contagion effects during stress periods. The results present the insurance and energy sectors as the most exposed to extreme events, backed by the heavy-tailed return distributions and persistent volatility. In contrast, the retail and select industrial firms exhibit resilience and show stable, and in some cases, counter-cyclical, behavior in crises. The results from using a GHP indicate a slight improvement in model specification fit, capturing return asymmetries, skewness, and leptokurtosis indications, in comparison to standard Gaussian models. It was also shown with an ADCC-GHP-GARCH model that negative shocks result in a greater and more durable change in correlations than positive shocks, reinforcing the consideration of asymmetry contagion in times of stress. By integrating sector-specific financial responses into a climate-disaster framework, this research supports the design of targeted climate risk mitigation strategies, sustainable investment portfolios, and regulatory stress-testing approaches that account for volatility clustering and tail dependencies. The findings contribute to the literature on financial resilience by providing a robust statistical basis for assessing how extreme climate events impact asset values, thereby informing both policy and practice in advancing sustainable economic development.