The COVID-19 crisis, which occurred in 2020, brought crisis events back to the attention of scholars. With the increasing frequency of crisis events, the influence of crisis events on stock markets has become more obvious. This paper focuses on the impact of the subprime crisis, the Chinese stock market crash crisis and the COVID-19 crisis on the volatility and risk of the world’s major stock markets. In this paper, we first fit the volatility using EGARCH model and detect asymmetry of volatility. After that, a VaR model is calculated on the basis of EGARCH to measure the impact of the crisis event on the risk of stock markets. This paper finds that the subprime crisis has a significant influence on the risk of the stock market in China, US, South Korea, and Japan. During the COVID-19 crisis, there was little change in the average risk of each country. But at the beginning of the COVID-19 crisis, there was a significant increase in the risk of each country’s stock market. The Chinese stock market crash crisis had a more pronounced effect on the Chinese and Japanese stock markets and a lesser effect on the US and Korean stock markets.

The continuous escalation of social risks has exacerbated the challenges faced by aging urban communities. In this context, resilience building emerges as a critical approach, offering new perspectives and innovative solutions to address these issues. This paper applies the theories of risk society and resilience governance to establish an analytical framework for resilience governance, specifically examining the current status of resilience construction within the Jin Guang Men community in Xi’an. The findings indicate that resilience building within these aging urban communities is hindered by issues such as weak grassroots governance, deficient repair mechanisms, inadequate infrastructure, and a slow pace of information technology adoption. To effectively manage social risks, it is imperative to strengthen party leadership in governance, enhance community self-repair capacities, upgrade infrastructure, and accelerate the application of information technology. These measures are essential for bolstering the risk management capabilities of aging urban communities.

The goal of this work was to create and assess machine-learning models for estimating the risk of budget overruns in developed projects. Finding the best model for risk forecasting required evaluating the performance of several models. Using a dataset of 177 projects took into account variables like environmental risks employee skill level safety incidents and project complexity. In our experiments, we analyzed the application of different machine learning models to analyze the risk for the management decision policies of developed organizations. The performance of the chosen model Neural Network (MLP) was improved after applying the tuning process which increased the Test R² from −0.37686 before tuning to 0.195637 after tuning. The Support Vector Machine (SVM), Ridge Regression, Lasso Regression, and Random Forest (Tuned) models did not improve, as seen when Test R² is compared to the experiments. No changes in Test R²’s were observed on GBM and XGBoost, which retained same Test R² across different tuning attempts. Stacking Regressor was used only during the hyperparameter tuning phase and brought a Test R² of 0. 022219.Decision Tree was again the worst model among all throughout the experiments, with no signs of improvement in its Test R²; it was −1.4669 for Decision Tree in all experiments arranged on the basis of Gender. These results indicate that although, models such as the Neural Network (MLP) sees improvements due to hyperparameter tuning, there are minimal improvements for most models. This works does highlight some of the weaknesses in specific types of models, as well as identifies areas where additional work can be expected to deliver incremental benefits to the structured applied process of risk assessment in organizational policies.

Family violence is the act that causes harm, suffering, or death to members of the family group, especially if they are in a situation of vulnerability due to characteristics associated to age or physical condition. Objective: The social characteristics of aggressors were associate in the risk level of victims of family violence in the city of Arequipa, Peru. Method: The study was descriptive, quantitative, and non-experimental. A total of 205 randomly selected judicial files of aggressors reported for domestic violence were evaluated. The data were secondary, and the chi-square test (association of categorical variables) was used for statistical analysis. Results: A moderate risk level (31.2%) was found, with a tendency to be severe and very severe (49.5%). Likewise, the most observed types of violence are physical and psychological violence (89.3%) and sexual abuse (10.7%). The female aggressor exerts mild violence, while the male aggressor exerts moderate to extreme severe violence, causing more harm to the victim. The profile of the aggressor with low or high education, with high or low incomes, and who occupies a house or only one room can be associated the level of violence that occurs. Conclusion: Men are more likely to attack women, and similarly, female aggressors tend to target men more frequently. Moreover, men exhibit a higher tendency to attack their partners, including wives, cohabitants, and ex-partners, whereas women tend to target a broader range of family members, including parents, children, grandparents, nephews, cousins, as well as in-laws such, brothers-in-law and other relatives.

In this paper, we examine a possible application of ordered weighted average (OWA for short) aggregation operators in the insurance industry. Aggregation operators are essential tools in decision-making when a single value is needed instead of a couple of features. Information aggregation necessarily leads to information loss, at least to a specific extent. Whether we concentrate on extreme values or middle terms, there can be cases when the most important piece of the puzzle is missing. Although the simple or weighted mean considers all the values there is a drawback: the values get the same weight regardless of their magnitude. One possible solution to this issue is the application of the so-called Ordered Weighted Averaging (OWA) operators. This is a broad class of aggregation methods, including the previously mentioned average as a special case. Moreover, using a proper parameter (the so-called orness) one can express the risk awareness of the decision-maker. Using real-life statistical data, we provide a simple model of the decision-making process of insurance companies. The model offers a decision-supporting tool for companies.

During the COVID-19 pandemic, individuals and their families faced various risk factors, which in some cases resulted in divorce. Adolescents in such families had to grapple with COVID-19 across the world, the risk factors faced by adolescents have largely been under-risk factors associated with COVID-19 and divorce. Despite the rise of divorce during studied, especially among adolescents in South Africa. This study aimed to explore the risk factors experienced by adolescents from divorced households during the COVID-19 pandemic and make recommendations for policy and development. This study employed a phenomenological research design in alignment with qualitative research. Purposive sampling was used to recruit five female adolescents in Johannesburg. Data was collected using semi-structured interviews and focus groups. Data was analyzed thematically using Braun and Clarke’s six steps of data analysis. The findings revealed that conflict at home, mental illness, physical and social isolation, a lack of paternal support, and diminished educational performance emerged as risk factors faced by the participants. These findings underscore the need for psychological interventions to help address the risk factors faced by adolescents whose parents divorced during the pandemic and those who face similar circumstances during future crises.