Abstract Investments in assets related to positive environmental, social, and governance (ESG) have grown, attracting interest from both academics and investment fund practitioners. In this work, we explore various methods for integrating ESG factors into portfolio construction, assessing their feasibility. We address three key questions. First, what are potential benefits and challenges of incorporating ESG factors into portfolio strategies? We compare our results to an equally weighted benchmark to assess the impact of ESG integration on portfolio performance. Second, how does performance vary across different portfolio optimization techniques? By exploring various optimization approaches, we identify the most effective strategies for constructing climate-resilient portfolios. Finally, does performance differ across countries and/or regions? Our study focuses its analysis on North American (S&P 500 Top 50) and European Union (EU STOXX 50) markets. Through this investigation, we examine the portfolio integration of ESG factors and the impact of country/region-specific characteristics on climate portfolio performance. This study contributes by introducing an ensemble portfolio methodology to ESG integration and providing a comparative cross-regional analysis. Our findings show that the effectiveness of ESG integration depend on the portfolio optimization framework, the portfolio sustainability choice, and the underlying market. Although no single strategy dominates across all settings, the proposed ensemble approach provides the best performance by balancing sustainability objectives with diversification and risk-adjusted returns across regions.
Abstract This study examines length of stay (LOS) in Australian residential aged care using a comprehensive dataset derived from the Aged Care Funding Instrument (ACFI). In addition to demographic characteristics and core care-need measures, the analysis incorporates depression-related appraisal variables and grouped mental, behavioral, and medical diagnosis indicators. To account for right-censoring, non-proportional hazards, nonlinear covariate effects, and strong dependence among predictors, we employ the accelerated oblique random survival forest to model LOS. The results show that admission cohort, age at admission, and functional dependency are the main predictors of LOS at the aggregate level, while richer assessment and diagnostic variables provide predictive value. In particular, the Cornell Scale for Depression emerges as an important predictor beyond the standard ACFI domains. Subsample analyses reveal substantial heterogeneity across care-need profiles, admission cohorts, and age groups. Depression-related and mental and behavioral variables become especially important in higher-need groups, and the relative importance of predictors also changes over time. These findings extend existing Australian evidence on residential aged care use and suggest that more detailed assessment and diagnosis information can improve understanding of LOS patterns, support more accurate planning and resource allocation, and strengthen actuarial assessment of long-term care duration risk and related liabilities.
Climate change is likely to increase the frequency, severity, and duration of heat waves in many countries. To plan mitigation, adaptation, and resilience strategies, it is necessary to quantify heat wave risk at both the local level and the country level. A new, more granular methodology is proposed in order to integrate the impact of heat waves in hexagonal France on mortality with a short-term stress scenario. Based on open data and reproducible methodology, the approach can be used as a starting point to investigate other effects, such as urban heat islands. The present application is based on in situ observational weather data and environmental vulnerability data to construct adapted geographical clusters without relying on the administrative division of the territory. Excess mortality is modeled as a function of weather using machine learning. Using recent knowledge of climatology, we construct extreme weather scenarios to calculate a shock to mortality. Short-term shocks are compared, and their respective merits are discussed. The methodology has been shown to generate mortality shocks up to five times greater than those estimated by the French regulatory authority.
Gaussian Process (GP) modeling is a probabilistic, non-parametric framework for describing spatio-temporal dependence that is well-suited for fitting risk-related surfaces. I summarize the main emerging actuarial use cases of GPs, including their applications in longevity modeling, insurance contract valuation, and loss development. The editorial also discusses further contexts with potential for GP-based approaches.
Accurate and internally coherent crop-yield forecasts are important for agricultural risk management, crop-insurance ratemaking, and regional risk assessment under climate variability. However, crop yields are influenced by high-dimensional and strongly correlated weather conditions, while forecasts produced at different spatial levels often violate aggregation constraints. Existing studies focus on yield prediction within individual regions and pay limited attention to weather-informed forecasting, hierarchical coherence, and insurance-oriented risk measurement. This paper develops an integrated framework for hierarchical crop-yield forecasting and risk assessment by combining dimensionality reduction for high-dimensional weather variables, probabilistic forecasting, and forecast reconciliation. Using county- and state-level spring and winter wheat yields in Montana from 1982 to 2022, we compare alternative base forecasting models and reconciliation methods under scenarios with and without weather information. Forecast performance is evaluated using point and probabilistic scoring rules, and the reconciled predictive distributions are used to construct scenario-based measures of downside yield risk. The results show that incorporating weather information and hierarchical reconciliation improves the quality and coherence of hierarchical yield forecasts. The resulting probabilistic forecasts provide a basis for loss-rate estimation, cross-county risk comparison, and spatial risk mapping and also support crop-insurance ratemaking under a retain-cede game between private insurers and the government.
