Search Results (115)

This study explores the transformative role of Cyber-Physical Power System (CPPS) Digital Twins (DTs) in enhancing the operational resilience, flexibility, and intelligence of modern power grids. By integrating physical system models with real-time cyber elements, CPPS DTs provide a synchronized framework for real-time monitoring, predictive maintenance, energy management, and cybersecurity. A structured literature review was conducted using the ProKnow-C methodology, yielding a curated portfolio of 74 publications from 2017 to 2025. This corpus was analyzed to identify key application areas, enabling technologies, simulation methods, and conceptual maturity levels of CPPS DTs. The study highlights seven primary application domains, including real-time decision support and cybersecurity, while emphasizing essential enablers such as data acquisition systems, cloud/edge computing, and advanced simulation techniques like co-simulation and hardware-in-the-loop testing. Despite significant academic interest, real-world implementations remain limited due to interoperability and integration challenges. The paper identifies gaps in standard definitions, maturity models, and simulation frameworks, underscoring the need for scalable, secure, and interoperable architectures and highlighting key areas for scientific development and real-life application of CPPS DTs, such as grid predictive maintenance, forecasting, fault handling, and power system cybersecurity. Full article

A design science framework integrates sustainability into business development across sectors. The framework embeds sustainability, reflected in environmental, social, and governance (ESG) dimensions, within a structured process that links drivers, evaluation components, and outcome indicators. Six principles guide its structure: clarity, integration, adaptability, stakeholder engagement, performance feedback, and scoring consistency. Researchers applied the framework in energy, engineering, and agribusiness cases. Case results show how the framework improves opportunity selection, identifies capability gaps, strengthens prioritization, and structures stakeholder input without adding complexity. Findings confirm that incorporating sustainability factors during the initial stage of business development changes decision patterns, aligns projects with long-term goals, and increases transparency in portfolio planning. This design science approach moves sustainability and its ESG dimensions from a reporting concern to a central element of strategic evaluation and growth planning. Organizations gain a practical structure to align opportunity development with resilience, learning capacity, and sustainability outcomes. In addition, the framework provides a foundation for adaptation, digital tool development, and longitudinal feedback cycles as firms integrate sustainability and ESG dimensions within uncertain policy, market, and stakeholder environments. Full article

Most reinforcement learning (RL) methods for portfolio optimization remain limited to single markets and a single algorithmic paradigm, which restricts their adaptability to regime shifts and heterogeneous conditions. This paper introduces a generalized version of the Modular Portfolio Learning System (MPLS), extending beyond its initial PPO backbone to integrate four RL algorithms: Proximal Policy Optimization (PPO), Deep Q-Network (DQN), Deep Deterministic Policy Gradient (DDPG), and Soft Actor-Critic (SAC). Building on its modular design, MPLS leverages specialized components for sentiment analysis, volatility forecasting, and structural dependency modeling, whose signals are fused within an attention-based decision framework. Unlike prior approaches, MPLS is evaluated independently on three major equity indices (S&P 500, DAX 30, and FTSE 100) across diverse regimes including stable, crisis, recovery, and sideways phases. Experimental results show that MPLS consistently achieved higher Sharpe ratios—typically +40–70% over Minimum Variance Portfolio (MVP) and Risk Parity (RP)—while limiting drawdowns and Conditional Value-at-Risk (CVaR) during stress periods such as the COVID-19 crash. Turnover levels remained moderate, confirming cost-awareness. Ablation and variance analyses highlight the distinct contribution of each module and the robustness of the framework. Overall, MPLS represents a modular, resilient, and practically relevant framework for risk-aware portfolio optimization. Full article

This study offers a comprehensive analysis of the performance and systemic dynamics of green finance investments in environmentally sustainable technologies from 2000 to 2025, complemented by scenario-based projections extending to 2050. Empirical results indicate a consistent increase in portfolio returns—from 5.2% in 2000 to 11.8% in 2025—accompanied by a significant reduction in annualized volatility, declining from 8.1% to 3.0%. Concurrently, the portfolio’s sustainability score improved from 0.45 to a full alignment score of 1.00, reflecting a strategic shift towards high-impact green assets. Building on these observed trends, this study introduces the Eco-Financial Resonance Theory (EFRT), an original conceptual framework that interprets sustainable transitions as emergent phenomena arising from resonant interactions among four interdependent domains: financial flows, technological innovation, policy and regulation, and environmental outcomes. Scenario analyses highlight the pivotal roles of policy ambition and innovation pathways in shaping long-term risk-return profiles, with optimistic forecasts projecting returns exceeding 40% by 2050, alongside markedly reduced risks. Regional analysis reveals persistent disparities, underscoring the necessity for context-specific strategies to enhance systemic coherence. Beyond its theoretical contributions, EFRT offers actionable insights for investors and policymakers aiming to align profitability with ecological sustainability. Collectively, these findings position green finance not merely as a niche or ancillary activity but as a transformative mechanism for enabling scalable and resilient sustainability transitions amid accelerating global environmental challenges. Full article

We develop a robust continuous time portfolio optimization framework that incorporates time-varying ESG risk through dynamically evolving drift ambiguity. Building on the equivalence between linear ESG penalties in mean-variance optimization and robust formulations under drift uncertainty, we extend the analysis to a dynamic setting with time-dependent, ESG-weighted ellipsoidal ambiguity sets. The model admits a tractable solution under CRRA preferences: the worst-case drift is obtained in closed form, and the optimal portfolio strategy is characterized as the unique maximizer of an ESG-adjusted Markowitz-type objective at each point in time. Economically, the framework provides a rigorous justification for penalty-based ESG portfolio models, while offering time-consistent robustness, forward-looking risk management, and dynamic hedging against sustainability-related model risk. Full article

Electricity expansion planning is inherently subject to uncertainty, shaped by climatic, regulatory, and economic risks. In Brazil, this challenge is compounded by recurrent crises that have repeatedly reduced electricity demand. This study proposes a complementary decision-support approach to make planning more resilient to such crises. Using Brazil’s official optimization models (NEWAVE), we introduce two analytical elements: (i) a regret-minimization screen for choosing between conservative and optimistic demand trajectories and (ii) a flexibility stress test that evaluates the cost impact of compulsory-dispatch shares in generation portfolios. Key findings show that conservative demand projections systematically minimize consumer-cost regret when crises occur, while portfolios with lower compulsory-dispatch shares reduce total system cost and improve adaptability across 2000 hydro inflow scenarios. These results highlight that crisis-robust planning requires combining cautious demand assumptions with flexible supply portfolios. Although grounded in the Brazilian context, the methodological contributions are generalizable and provide practical guidance for other electricity markets facing deep and recurrent uncertainty. Full article

This paper develops a nonlinear shrinkage estimation method for higher-order moment matrices within a multifactor model framework and establishes its asymptotic consistency under high-dimensional settings. The approach extends the nonlinear shrinkage methodology from covariance to higher-order moments, thereby mitigating the “curse of dimensionality” and alleviating estimation uncertainty in high-dimensional settings. Monte Carlo simulations demonstrate that, compared with linear shrinkage estimation, the proposed method substantially reduces mean squared errors (MSEs) and achieves greater Percentage Relative Improvement in Average Loss (PRIAL) for covariance and cokurtosis estimates; relative to sample estimation, it delivers significant gains in mitigating uncertainty for covariance, coskewness, and cokurtosis. An empirical portfolio analysis incorporating higher-order moments shows that, when the asset universe is large, portfolios based on the nonlinear shrinkage estimator outperform those constructed using linear shrinkage and sample estimators, achieving higher annualized return and Sharpe ratio with lower kurtosis and maximum drawdown, thus providing stronger resilience against uncertainty in complex financial systems. In smaller asset universes, nonlinear shrinkage portfolios perform on par with their linear shrinkage counterparts. These findings highlight the potential of nonlinear shrinkage techniques to reduce uncertainty in higher-order moment estimation and to improve portfolio performance across diverse and complex investment environments. Full article

Portfolio optimization is a cornerstone of modern financial decision-making, traditionally based on the mean–variance model introduced by Markowitz. However, this framework relies on restrictive assumptions—such as normally distributed returns and symmetric risk preferences—that often fail in real-world markets, particularly in volatile and non-Gaussian environments such as cryptocurrencies. To address these limitations, this paper proposes a novel multi-objective model that combines expected return maximization, mean absolute deviation (MAD) minimization, and entropy-based diversification into a unified optimization structure: the Mean–Deviation–Entropy (MDE) model. The MAD metric offers a robust alternative to variance by capturing the average magnitude of deviations from the mean without inflating extreme values, while entropy serves as an information-theoretic proxy for portfolio diversification and uncertainty. Three entropy formulations are considered—Shannon entropy, Tsallis entropy, and cumulative residual Sharma–Taneja–Mittal entropy (CR-STME)—to explore different notions of uncertainty and structural diversity. The MDE model is formulated as a tri-objective optimization problem and solved via scalarization techniques, enabling flexible trade-offs between return, deviation, and entropy. The framework is empirically tested on a cryptocurrency portfolio composed of Bitcoin (BTC), Ethereum (ETH), Solana (SOL), and Binance Coin (BNB), using daily data over a 12-month period. The empirical setting reflects a high-volatility, high-skewness regime, ideal for testing entropy-driven diversification. Comparative outcomes reveal that entropy-integrated models yield more robust weightings, particularly when tail risk and regime shifts are present. Comparative results against classical mean–variance and mean–MAD models indicate that the MDE model achieves improved diversification, enhanced allocation stability, and greater resilience to volatility clustering and tail risk. This study contributes to the literature on robust portfolio optimization by integrating entropy as a formal objective within a scalarized multi-criteria framework. The proposed approach offers promising applications in sustainable investing, algorithmic asset allocation, and decentralized finance, especially under high-uncertainty market conditions. Full article

Portfolio optimization is a multifaceted process aimed at achieving a balance between investors’ risk tolerance and expected returns. However, the inherent uncertainty and unpredictability of financial markets significantly hinder the attainment of this balance. Therefore, there is an increasing need for models capable of representing these uncertainties in a more realistic manner. In this study, novel intuitionistic fuzzy mathematical models are proposed to provide alternative portfolio options that align with diverse investor expectations and risk perceptions. By utilizing mathematical programming formulations incorporating intuitionistic fuzzy parameters, the study contributes to the theoretical framework and enables the analysis of portfolio structures that vary in response to imprecisely defined return levels. The intuitionistic fuzzy parameters are modeled using appropriate membership and non-membership functions, and mean absolute deviation is employed as the risk measure within the proposed models. Various alternative solutions are generated by considering different lower and upper bound constraints, thereby allowing for the construction of flexible investment strategies suitable for different investor profiles. The practical applicability of the proposed models is demonstrated using real-world stock data obtained from Borsa Istanbul. The empirical results reveal that the models provide solutions that are sensitive to individual risk preferences and adaptable to changing market conditions. Accordingly, the developed intuitionistic fuzzy models serve as effective tools for determining optimal portfolio allocations and developing resilient investment strategies. Full article

Sectoral vulnerability to distinct crisis types in small, open, and geopolitically exposed markets—such as Jordan—remains insufficiently quantified, constraining targeted policy design and portfolio allocation. This study’s primary purpose is to establish a transparent, comparable metric of sector-level market resilience that reveals how crisis typology reorders vulnerabilities and shapes recovery speed. Applying this framework, we assess Jordan’s equity market across three archetypal episodes—the Global Financial Crisis, the Arab Spring, and COVID-19—to clarify how shock channels reconfigure sectoral risk. Using daily Amman Stock Exchange sector indices (2001–2025), we estimate $GARCH\left(\mathrm{1,1}\right)$ models for each sector–crisis window and summarize volatility dynamics by persistence $(\alpha +\beta )$, interpreted as an inverse proxy for resilience; complementary diagnostics include maximum drawdown and days-to-recovery, with nonparametric (Kruskal–Wallis) and rank-based (Spearman, Friedman) tests to evaluate within-crisis differences and cross-crisis reordering. Results show pronounced heterogeneity in every crisis and shifting sectoral rankings: financials—especially banking—display the highest persistence during the GFC; tourism and transportation dominate during COVID-19; and tourism/electric-related industries are most persistent around the Arab Spring. Meanwhile, food & beverages, pharmaceuticals/medical, and education recurrently exhibit lower persistence. Higher persistence aligns with slower post-shock normalization. We conclude that resilience is sector-specific and contingent on crisis characteristics, implying targeted policy and portfolio responses; regulators should prioritize liquidity backstops, timely disclosure, and contingency planning for fragile sectors, while investors can mitigate crisis risk via dynamic sector allocation and volatility-aware risk management in emerging markets. Full article

As states within the United States respond to future grid development goals, there is a growing demand for reliable and resilient nighttime generation that can be addressed by low-cost, long-duration energy storage solutions. This report studies the potential of including concentrating solar power (CSP) in the technology mix to support California’s goals as defined in Senate Bill 100. A joint agency report study that determined potential pathways to achieve the renewable portfolio standard set by the bill did not include CSP, and our work provides information that could be used as a follow-up. This study uses a capacity expansion model configured to have nodal spatial fidelity in California and balancing-area fidelity in the Western Interconnection outside of California. The authors discovered that by applying current technology cost projections CSP fulfills nearly 15% of the annual load while representing just 6% of total installed capacity in 2045, replacing approximately 30 GWe of wind, solar PV, and standalone batteries compared to a scenario without CSP included. The deployment of CSP in the results is sensitive to the technology’s cost, which highlights the importance of meeting cost targets in 2030 and beyond to enable the technology’s potential contribution to California’s carbon reduction goals. Full article

This study examines the dynamics of equity market volatility and economic policy uncertainty (EPU) in South Africa during the COVID-19 pandemic. Using daily return data for sectoral indices and the JSE All Share Index (ALSI) from 1 January 2020 to 31 March 2022, the analysis explores both market-wide and sector-specific volatility responses. Univariate GARCH-family models (GARCH (1,1), E-GARCH, and T-GARCH) are employed to capture volatility clustering, persistence, and asymmetry across sectors. The results show that volatility was highly persistent during the pandemic, with sectoral differences in sensitivity to shocks: Consumer Staples and Financials were particularly reactive to recent news, while Health Care and Basic Materials were more stable. Asymmetric models confirm that market sentiment was predominantly driven by negative news, except in the Energy sector, where positive recovery signals played a stronger role. Correlation analysis further indicates that most sectors were moderately correlated with the ALSI, while Energy and Health Care behaved more independently. In contrast, both the ALSI and sector returns exhibited weak and negative correlations with the South African EPU index, suggesting that uncertainty did not translate directly into equity market declines. Overall, the findings highlight the importance of sectoral heterogeneity in volatility dynamics and suggest that during extreme market events, investors can mitigate downside risk by reallocating portfolios toward more resilient sectors. Full article

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. Full article

In multi-project environments characterized by resource constraints and high uncertainty, traditional scheduling approaches often fail to respond effectively to dynamic project conditions. Fixed activity durations and rigid resource allocations limit adaptability, leading to inefficiencies and delays. To address this, the paper proposes a novel heuristic-based scheduling method that compresses and decompresses activity durations dynamically within the context of multi-project scheduling under uncertainty and resource flexibility—while preserving resource and precedence feasibility. The technique integrates Critical Path Method (CPM) calculations with heuristic rules to identify candidate activities whose durations can be reduced or extended based on slack availability and resource effort profiles. The objective is to enhance scheduling flexibility, improve resource utilization, and better align project execution with organizational priorities and sustainability goals. Validated through a case study at an automotive company in Portugal, the method demonstrates its practical effectiveness in recalibrating schedules and balancing resource loads. This contribution offers a timely and necessary innovation for companies aiming to enhance responsiveness and competitiveness in increasingly complex project landscapes. It provides an actionable framework for dynamic schedule adjustment in multi-project environments, helping companies to respond more effectively to uncertainty and resource fluctuations. Importantly, the proposed approach also supports sustainability objectives in new product development and supply chain operations. For practitioners, the method offers a responsive and sustainable planning tool that supports real-time adjustments in project portfolios, enhancing resource visibility and execution resilience. For researchers, the study contributes a reproducible, Python-based implementation grounded in Design Science Research (DSR), addressing gaps in stochastic multi-project scheduling and sustainability-aware planning. Full article

This study develops an integrated risk modeling framework to assess capital adequacy and optimize portfolio performance for Thai life and non-life insurers. Leveraging ARMA–GJR–GARCH models with skewed Student-t innovations, extreme value theory, and dynamic R-vine copulas, the framework effectively captures volatility, tail risks, and evolving asset interdependencies. Utilizing daily data from 2014 to 2024, the models generate value-at-risk forecasts consistent with international standards such as Basel III’s 10-day 99% VaR and rolling Sharpe ratios for portfolios integrating green bonds compared to traditional asset allocations. The results demonstrate that green bonds, fixedincome instruments funding renewable energy and other environmental projects, significantly improve risk-adjusted returns and have the potential to reduce capital requirements, particularly for life insurers with long-term sustainability mandates. These findings underscore the importance of portfolio-level capital assessment and support the proactive integration of ESG considerations into supervisory investment guidelines to enhance financial resilience and align the insurance sector with Thailand’s sustainable finance agenda. Full article