Search Results (5,078)

This paper introduces a new framework for high-dimensional covariance matrix estimation, the Blockwise Exponential Covariance Model (BECM), which extends the traditional block-partitioned representation to the log-covariance domain. By exploiting the block-preserving properties of the matrix logarithm and exponential transformations, the proposed model guarantees strict positive definiteness while substantially reducing the number of parameters to be estimated through a blockwise log-covariance parameterization, without imposing any rank constraint. Within each block, intra- and inter-group dependencies are parameterized through interpretable coefficients and kernel-based similarity measures of factor loadings, enabling a data-driven representation of nonlinear groupwise associations. Using monthly stock return data from the U.S. stock market, we conduct extensive rolling-window tests to evaluate the empirical performance of the BECM in minimum-variance portfolio construction. The results reveal three main findings. First, the BECM consistently outperforms the Canonical Block Representation Model (CBRM) and the native 1/N benchmark in terms of out-of-sample Sharpe ratios and risk-adjusted returns. Second, adaptive determination of the number of clusters through cross-validation effectively balances structural flexibility and estimation stability. Third, the model maintains numerical robustness under fine-grained partitions, avoiding the loss of positive definiteness common in high-dimensional covariance estimators. Overall, the BECM offers a theoretically grounded and empirically effective approach to modeling complex covariance structures in high-dimensional financial applications. Full article

This study aimed to develop a credit risk index to classify coffee producers according to socioeconomic, agronomic, and financial performance variables, with the purpose of strengthening financial inclusion. We combined qualitative and quantitative methods to understand credit risk factors among smallholder coffee producers. The study followed a descriptive-analytical approach structured in consecutive methodological phases. The systematic review, conducted following the Kitchenham protocol, identified theoretical factors associated with credit risk, while fieldwork with 300 producers provided the socioeconomic and productive contexts of coffee-growing households. Producer income, cost of living, and farm management expenses were modeled using regression, statistical, and machine learning methods. Subsequently, these variables were integrated to construct a financial risk index, which was normalized using expert scoring. The index was validated using data from 100 additional producers, for whom annual repayment capacity and maximum loan amounts were estimated according to their risk level. The results indicated that incorporating municipal-level economic variables, such as estimated average prices, income, and expenses, enhanced predictive accuracy and improved the rational allocation of loan amounts. The study concludes that credit risk analysis based on variables related to human, productive, and economic capital constitutes an effective strategy for improving access to finance in rural areas. Full article

Financial crises increasingly exhibit complex, interconnected patterns that traditional risk models fail to capture. The 2008 global financial crisis, 2020 pandemic shock, and recent banking sector stress events demonstrate how systemic risks propagate through multiple channels simultaneously—e.g., network contagion, extreme co-movements, and information cascades—creating a multidimensional phenomenon that exceeds the capabilities of conventional actuarial or econometric approaches alone. This paper addresses the fundamental challenge of modeling this multidimensional systemic risk phenomenon by proposing a mathematically formalized three-tier integration framework that achieves 19.2% accuracy improvement over traditional models through the following: (1) dynamic network-copula coupling that captures 35% more tail dependencies than static approaches, (2) semantic-temporal alignment of textual signals with network evolution, and (3) economically optimized threshold calibration reducing false positives by 35% while maintaining 85% crisis detection sensitivity. Empirical validation on historical data (2000–2023) demonstrates significant improvements over traditional models: 19.2% increase in predictive accuracy (R² from 0.68 to 0.87), 2.7 months earlier crisis detection compared to Basel III credit-to-GDP indicators, and 35% reduction in false positive rates while maintaining 85% crisis detection sensitivity. Case studies of the 2008 crisis and 2020 market turbulence illustrate the model’s ability to identify subtle precursor signals through integrated analysis of network structure evolution and semantic changes in regulatory communications. These advances provide financial regulators and institutions with enhanced tools for macroprudential supervision and countercyclical capital buffer calibration, strengthening financial system resilience against multifaceted systemic risks. Full article

A Security Operation Center (SOC) is a security control center for monitoring, detecting, analyzing, and responding to cybersecurity threats. PT (Perseroan Terbatas) Non-Bank Financial Company (NBFC) has implemented an SOC to secure its information systems, but challenges remain to be solved. These include the absence of impact analysis on financial and regulatory requirements, cost, and effort estimation for recovery; established Key Performance Indicators (KPIs) and Key Risk Indicators (KRIs) for monitoring security controls; and an official program for insider threats. This study evaluates SOC effectiveness at PT NBFC using the ISO 27005:2018 and NIST SP 800-30 frameworks. The research results in a proposed SOC assessment framework, integrating risk assessment, risk treatment, risk acceptance, and monitoring. Additionally, a maturity level assessment was conducted for ISO 27005:2018, NIST SP 800-30, and the proposed framework. The proposed framework achieves good maturity, with two domains meeting the target maturity value and one domain reaching level 4 (Managed and Measurable). By incorporating domains from both ISO 27005:2018 and NIST SP 800-30, the new framework offers a more comprehensive risk management approach, covering strategic, managerial, and technical aspects. Full article

Municipal solid waste management (MSWM) remains a critical environmental governance challenge in rural and peri-urban regions of the Global South, where service delivery gaps exacerbate illegal dumping and public health risks. Geographic Information Systems (GIS) are increasingly promoted as decision-support tools to improve waste collection efficiency and environmental monitoring; however, their adoption in resource-constrained municipalities remains limited. This study investigates the institutional and policy barriers shaping GIS readiness in four rural municipalities within South Africa’s Vhembe District. Using a qualitative case-study design, semi-structured interviews were conducted with 29 municipal officials across managerial and operational levels, complemented by 399 community responses to an open-ended survey question. Thematic analysis, guided by Institutional Theory and the Technology Acceptance Model (TAM), identified five interrelated themes: waste production and disposal behaviours, collection and infrastructure constraints, institutional and operational challenges, policy and standardisation gaps, and technology readiness. The findings reveal that weak service reliability, fragmented governance structures, limited human and financial capacity, and inconsistent policy enforcement collectively undermine GIS adoption, despite its high perceived usefulness among officials. The study demonstrates that the effectiveness of GIS as an environmental management tool is contingent on institutional readiness rather than technological availability alone and highlights the need for integrated reforms in service delivery, institutional capacity, and policy implementation to enable GIS-supported sustainable waste management. Full article

Sustainability has become a central concern globally, and efforts to enhance it are being made across various fields. In line with this trend, corporate sustainability reports have become more widely published. These reports provide both financial and non-financial information on a company’s sustainability. In this context, this study aims to, first, analyze the key keywords contained in CEO messages. Second, it examines whether the keywords emphasized by CEOs change in response to shifts in corporate risk under economic uncertainty. Finally, it identifies how the categories of words included in these messages are classified. To address these research questions, text analysis was selected as the methodology. Specifically, a qualitative research approach using text mining and CONCOR analysis was conducted on the text from sustainability report. According to the Term Frequency and Term Frequency-Inverse Document Frequency analyses, the most frequently occurring keywords were ESG, Sustainable, Society, Stakeholders, Growth, Environment, Effort, and Future. Centrality analysis identified the following keywords as having high centrality: Sustainable, ESG, Society, Environment, Growth, Effort, and Stakeholders. Finally, CONCOR analysis revealed four clusters: Eco-friendly Energy, ESG Management, Global Crisis, and Technological Competitiveness. This study is significant in that it analyzes the major keywords and their changes within unstructured text data using text mining and CONCOR analysis, and it suggests the possibility of future quantitative analysis of non-financial information using these keywords. Full article

In modern financial markets, decision-makers increasingly rely on quantitative methods to navigate complex trade-offs among multiple, often conflicting objectives. This paper addresses constrained multi-objective optimization (MOO) with an application to portfolio optimization for minimizing risk and maximizing return. To this end, and to address existing gaps, we propose a novel reinforcement learning (RL)-guided non-dominated sorting genetic algorithm II (NSGA-II) enhanced with gray relational coefficients (GRC), termed RL-NSGA-II-GRC, which combines an RL agent controller and GRC-based selection to improve the convergence and diversity of the Pareto-optimal fronts. The agent adapts key evolutionary parameters online using population-level metrics of hypervolume, feasibility, and diversity, while the GRC-enhanced tournament operator ranks parents via a unified score simultaneously considering dominance rank, crowding distance, and geometric proximity to ideal reference. We evaluate the framework on the Kursawe and CONSTR benchmark problems and on a NASDAQ portfolio optimization application. On the benchmarks, RL-NSGA-II-GRC achieves convergence metric improvements of about 5.8% and 4.4% over the original NSGA-II, while preserving a well-distributed set of non-dominated solutions. In the portfolio application, the method produces a smooth and densely populated efficient frontier that supports the identification of the maximum Sharpe ratio portfolio (with annualized Sharpe ratio = 1.92), as well as utility-optimal portfolios for different risk-aversion levels. The main contributions of this work are three-fold: (1) we propose an RL-NSGA-II-GRC method that integrates an RL agent into the evolutionary framework to adaptively control key parameters using generational feedback; (2) we design a GRC-enhanced binary tournament selection operator that provides a comprehensive performance indicator to efficiently guide the search toward the Pareto-optimal front; (3) we demonstrate, on benchmark MOO problems and a NASDAQ portfolio case study, that the proposed method delivers improved convergence and well-populated efficient frontiers that support actionable investment insights. Full article

This article is aimed at assessing energy–economy models with a focus on their ability to capture the dynamic structural changes of economic systems and the related energy supply chains. A narrative literature review approach was employed, synthesizing relevant peer-reviewed research. The search yielded 229 publications spanning from 2015 to 2024. After applying screening criteria based on methodological transparency, quantitative modelling, and explicit energy–economy integration, 120 articles were retained, from which 23 representative modelling frameworks were selected. The review identifies five key dimensions shaping the realism and applicability of integrated models: geographical and temporal scope, technological detail, modelling approach, and the degree of micro- and macroeconomic realism. Results show a growing adoption of multi-scale modelling and a gradual shift toward hybrid structures combining technological and macroeconomic components. However, significant gaps remain: only 26% of the models move beyond equilibrium assumptions; 17% incorporate behavioural or heterogeneous agents; and almost half rely on exogenous technological change. Moreover, the representation of policy instruments—particularly performance standards, sectoral benchmarks, and public investment mechanisms—remains incomplete across most frameworks. Overall, this analysis highlights the need for more transparent coupling strategies, enhanced behavioural realism, and improved representation of financial and transition risks. These findings inform the methodological development of next-generation models and indicate priority areas for future research aimed at improving the robustness of policy-relevant transition assessments. Full article

This research aims to analyze strategic risks from financial digitalization, highlighting the disruptive role of Fintech firms and Neobanks, the associated challenges and opportunities, and how traditional banks can adapt to remain competitive and stable in a rapidly evolving financial ecosystem. A qualitative methodology was employed, involving semi-structured interviews with 10 executives and risk management experts from the financial sector. The study employed a concurrence analysis to identify semantic relationships among categories. The unit of analysis was the paragraph, and concurrence was computed based on the frequency with which two categories appeared within the same segment. Key findings indicate that the most significant risks are linked to technological competition, regulatory shifts, cybersecurity, and consumer trust. Conversely, notable opportunities exist in technological modernization, enhanced regulatory compliance, collaboration with digital players, and the development of user-centric products and services. This study introduces the concept of a cultural gap in strategic adaptation, distinct from resistance to change, by emphasizing misalignment between organizational culture and the pace of digital transformation. This gap poses a strategic risk by delaying execution, increasing exposure to regulatory and technological risks, and reducing competitiveness. Full article

Financialisation has an increasing influence on the functioning of non-financial enterprises. It is therefore important to examine whether and to what extent food sector enterprises are subject to the process of financialisation. The research objective was to determine the level of financialisation of food industry enterprises in Poland in relation to the whole industry sector. To achieve this objective, the following research hypothesis was formulated: the process of financialisation of food industry enterprises proceeds similarly to the analogous process undergoing in industrial enterprises but varies across different sectors of the food industry. The research was conducted on the basis of statistical data from Statistics Poland (SP) published in various statistical studies. Financial data from 2010 to 2023 were analysed. For this purpose, research tools used in the paper are referred to in the literature as measures of the level of financialisation, so-called balance sheet indicators. The main limitation of the research is that the results can only be applied to countries with similar economic conditions, especially post-communist countries, and that balance sheet indicators are used to measure financialisation, which, although widely used, are limited in their effectiveness because they focus only on balance sheet data. The results support the research hypothesis. The companies in the analysed industries are characterised by a low level of financialisation. The process of financialisation of food industry companies is similar to the one in industrial companies and is more intense in beverage production than in other food industry sectors. There is room for a sustainable financing policy. The results indicate that there is room for higher financing of food industry enterprises in Poland, but excessive financing may lead to excessive concentration and monopolisation of enterprises and even to speculation on agricultural markets. To maintain financial stability, it will be important to pursue a stable monetary policy, limit the risk of food price volatility, improve communication and coordination in international monetary policy, and increase national food self-sufficiency. This study fills a research gap in understanding the process of financialisation, assessing its degree of advancement and diversity in the main sectors of food processing enterprises. Full article

The construction sector is central to achieving the objectives of the European Green Deal (EGD). While existing research on transition risks predominantly focuses on project- or firm-level challenges, less is known about the transition risks implied by high-level EU policy documents. This study addresses this gap by systematically analysing 101 EGD-related policy and guidance documents published between 2019 and February 2025. A mixed human–AI content analysis approach was applied, combining human expert manual coding with automated validation using large language models (Kimi K2 and GLM 4.6). The final dataset contains 2752 coded risk references organised into eight main categories and twenty-six subcategories. Results show that transition risks are most frequently associated with environmental, economic, and legislative domains, with Climate Change Impact, Cost of Transition, Pollution, Investment Risks, and Implementation Variability emerging as the most prominent risks across the corpus. Technological and social risks appear less frequently but highlight important systemic and contextual vulnerabilities. Overall, analysis of the EGD policy texts reveals the green transition as being constrained not only by environmental pressures but also by financial feasibility and execution capacity. The study provides a structured, policy-level risk profile of the EGD and demonstrates the value of hybrid human–LLM analysis for large-scale policy content analysis and interpretation. These insights support policymakers and industry stakeholders to anticipate structural uncertainties that may affect the construction sector’s transition toward a low-carbon, circular economy. Full article

Low-income individuals are unlikely to save relatively large sums on a regular basis; however, many still fall short of even the modest threshold required for long-term financial security. This study examines the determinants of benchmark-defined undersaving among retail e-payment agents (REAs) operating in the urban slums of Lagos, Nigeria. We use a contingent valuation survey, descriptive analysis, and logistic regression to examine how selected behavioural and demographic factors, alongside a 60-day experimental intervention—the Programmed Microsaving Scheme (PMSS), a hard daily commitment savings device—affect the likelihood of undersaving, defined as saving less than 12% of each REA’s average daily income. While the PMSS appears to have contributed to improvements in post-treatment saving participation and performance among REAs, it did not significantly increase the likelihood of reaching or exceeding the benchmark savings threshold. Consistent with this, average daily income, age, gender, marital status, education, and religion are statistically insignificant predictors of benchmark-defined undersaving. In contrast, self-control, measured using a literature-validated instrument, exhibits a statistically significant negative association with benchmark-defined undersaving, indicating that higher self-control reduces the likelihood of failing to meet the benchmark. Measured risk aversion similarly shows no significant association. Notably, this study introduces a novel 60-day PMSS, co-designed with REAs and neobanks to accommodate daily income savings—a characteristic of the informal sector largely overlooked in the literature on commitment savings devices. From a policy perspective, the findings suggest that while short-horizon commitment devices (such as the 60-day PMSS) and financial literacy are associated with improvements in microsavings among low-income daily earners, achieving benchmark-level saving might require longer-term and more adaptive mechanisms that address income volatility and mitigate other inherent risks. Full article

The interplay between government economic policy uncertainty (EPU) and bank risk remains a key concern in the financial stability literature. This study advances the field by examining the dynamic, time-varying impact of EPU on bank risk, explicitly differentiating between short- and long-term effects. We posit a dual hypothesis: heightened EPU increases short-run bank risk by raising borrower default probabilities while decreasing long-run risk as banks adopt more conservative lending strategies, given the option value of waiting under high uncertainty. Analyzing bank-level data across 22 countries from 1998 to 2017, we find robust empirical support: EPU exerts an immediate positive effect on bank risk and a significant negative effect with a lag of two to four years. These findings are robust to endogeneity and multiple sensitivity checks. Our results explicitly demonstrate the dual role of policy uncertainty in shaping bank risk-taking and offer timely guidance for the design of regulatory and macroprudential frameworks. Full article

Accurate volatility forecasting is essential for risk management in increasingly interconnected financial markets. Traditional econometric models capture volatility clustering but struggle to model nonlinear cross-market spillovers. This study proposes a Temporal Graph Attention Network (TemporalGAT) for multi-horizon volatility forecasting, integrating LSTM-based temporal encoding with graph convolutional and attention layers to jointly model volatility persistence and inter-market dependencies. Market linkages are constructed using the Diebold–Yilmaz volatility spillover index, providing an economically interpretable representation of directional shock transmission. Using daily data from major global equity indices, the model is evaluated against econometric, machine learning, and graph-based benchmarks across multiple forecast horizons. Performance is assessed using MSE, $R^2$, MAFE, and MAPE, with statistical significance validated via Diebold–Mariano tests and bootstrap confidence intervals. The study further conducts a strict expanding-window robustness test, comparing fixed and dynamically re-estimated spillover graphs in a fully out-of-sample setting. Sensitivity and scenario analyses confirm robustness across hyperparameter configurations and market regimes, while results show no systematic gains from dynamic graph updating over a fixed spillover network. Full article

Decentralized Finance (DeFi) has become a major component of digital asset markets, yet accurately valuing protocol performance remains difficult due to high volatility, nonlinear pricing dynamics, and persistent disclosure gaps that amplify valuation risk. This study develops an Optuna-tuned Super Learner stacked ensemble to improve risk-aware DeFi valuation, combining Extremely Randomized Trees (ETs), Support Vector Regression (SVR), and Categorical Boosting (CAT) as heterogeneous base learners, with a K-Nearest Neighbors (KNNs) meta-learner integrating their forecasts. Using an expanding-window panel time-series cross-validation design, the framework achieves significantly higher predictive accuracy than individual models, benchmark ensembles, and econometric baselines, obtaining RMSE = 0.085, MAE = 0.065, and R² = 0.97—representing a 25–36% reduction in valuation error. Wilcoxon tests confirm that these gains are statistically significant (p < 0.01). SHAP-based interpretability analysis identifies Gross Merchandise Volume (GMV) as the primary valuation determinant, followed by Total Value Locked (TVL) and key protocol design features such as Decentralized Exchange (DEX) classification, while revenue variables and inflation contribute secondary effects. The findings demonstrate how explainable ensemble learning can strengthen valuation accuracy, reduce information-driven uncertainty, and support risk-informed decision-making for investors, analysts, developers, and policymakers operating within rapidly evolving blockchain-based digital asset environments. Full article