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Risks
Volume 13
Issue 12
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Risks, Volume 13, Issue 12 (December 2025) – 26 articles

Cover Story (view full-size image): Integrating Environmental, Social, and Governance (ESG) metrics into supply chain finance is critical for promoting sustainable development. However, the dynamic mechanisms through which real-time ESG performance influences credit allocation and, consequently, shapes credit risk and environmental risk exposures for financial institutions, remain poorly understood, especially when compared to traditional static and retrospective ESG evaluations. To address this, we developed an agent-based model that simulates interactions among green enterprises, a financial institution, and a regulator, featuring a dynamic credit algorithm that adjusts credit lines based on real-time ESG scores. View this paper
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14 pages, 977 KB  
Article
Maximizing Portfolio Diversification via Weighted Shannon Entropy: Application to the Cryptocurrency Market
by Florentin Șerban and Silvia Dedu
Risks 2025, 13(12), 253; https://doi.org/10.3390/risks13120253 - 18 Dec 2025
Viewed by 490
Abstract
This paper develops a robust portfolio optimization framework that integrates Weighted Shannon Entropy (WSE) into the classical mean–variance paradigm, offering a distribution-free approach to diversification suited for volatile and heavy-tailed markets. While traditional variance-based models are highly sensitive to estimation errors and instability [...] Read more.
This paper develops a robust portfolio optimization framework that integrates Weighted Shannon Entropy (WSE) into the classical mean–variance paradigm, offering a distribution-free approach to diversification suited for volatile and heavy-tailed markets. While traditional variance-based models are highly sensitive to estimation errors and instability in covariance structures—issues that are particularly acute in cryptocurrency markets—entropy provides a structural mechanism for mitigating concentration risk and enhancing resilience under uncertainty. By incorporating informational weights that reflect asset-specific characteristics such as volatility, market capitalization, and liquidity, the WSE model generalizes classical Shannon entropy and allows for more realistic, data-driven diversification profiles. Analytical solutions derived from the maximum entropy principle and Lagrange multipliers yield exponential-form portfolio weights that balance expected return, variance, and diversification. The empirical analysis examines two case studies: a four-asset cryptocurrency portfolio (BTC, ETH, SOL, and BNB) over January–March 2025, and an extended twelve-asset portfolio over April 2024–March 2025 with rolling rebalancing and proportional transaction costs. The results show that WSE portfolios achieve systematically higher entropy scores, more balanced allocations, and improved downside protection relative to both equal-weight and classical mean–variance portfolios. Risk-adjusted metrics confirm these improvements: WSE delivers higher Sharpe ratios and less negative Conditional Value-at-Risk (CVaR), together with reduced overexposure to highly volatile assets. Overall, the findings demonstrate that Weighted Shannon Entropy offers a transparent, flexible, and robust framework for portfolio construction in environments characterized by nonlinear dependencies, structural breaks, and parameter uncertainty. Beyond its empirical performance, the WSE model provides a theoretically grounded bridge between information theory and risk management, with strong potential for applications in algorithmic allocation, index construction, and regulatory settings where diversification and stability are essential. Moreover, the integration of informational weighting schemes highlights the capacity of WSE to incorporate both statistical properties and market microstructure signals, thereby enhancing its practical relevance for real-world investment decision-making. Full article
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21 pages, 591 KB  
Article
From Stochastic Orders to Volatility Surfaces: Revisiting the One-X Property
by Zeyu Cao, Siqiao Zhao and Shaosai Huang
Risks 2025, 13(12), 252; https://doi.org/10.3390/risks13120252 - 15 Dec 2025
Viewed by 307
Abstract
The One-X property, introduced by Zetocha in a 2023 paper, provides a novel stochastic order with direct implications for constructing arbitrage-free implied volatility surfaces. The current work revisits its theoretical foundations and explores its connections with classical stochastic orders, thereby offering a deeper [...] Read more.
The One-X property, introduced by Zetocha in a 2023 paper, provides a novel stochastic order with direct implications for constructing arbitrage-free implied volatility surfaces. The current work revisits its theoretical foundations and explores its connections with classical stochastic orders, thereby offering a deeper understanding of its mathematical structure and practical significance in calendar-arbitrage-free modeling. We first present an explicit counterexample to a conjecture raised in Zetocha’s previous paper, and then provide a natural and valid enhancement of this conjecture. After discussing the inherent relations between the One-X property and properties such as TP2, RP2, and unimodality of density ratio (introcuded by Glasserman and Pirjol in their 2024 papers), we further explore some sufficient conditions to achieve the One-X property for random variables of certain mixture types that are frequently seen in applications. Full article
(This article belongs to the Special Issue Stochastic Modelling in Financial Mathematics, 2nd Edition)
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33 pages, 6744 KB  
Article
Local Attention and ASEAN-5 Connectedness: A TVP-VAR and GARCH-MIDAS Analysis
by Faten Chibani and Jamel Eddine Henchiri
Risks 2025, 13(12), 251; https://doi.org/10.3390/risks13120251 - 15 Dec 2025
Viewed by 508
Abstract
We show that financial integration in emerging Asia is state-dependent in the sense that cross-market linkages vary systematically across regimes of global uncertainty and market stress. Focusing on Indonesia, Malaysia, Singapore, Thailand, and Vietnam, this study combines a time-varying parameter VAR (TVP–VAR) with [...] Read more.
We show that financial integration in emerging Asia is state-dependent in the sense that cross-market linkages vary systematically across regimes of global uncertainty and market stress. Focusing on Indonesia, Malaysia, Singapore, Thailand, and Vietnam, this study combines a time-varying parameter VAR (TVP–VAR) with a GARCH–MIDAS volatility model to link short-run transmission to long-run behavioural effects. We construct a regional investor-sentiment (IS) index from Google search data on five macro-financial topics using principal component analysis and analyse it together with global benchmarks (MSCI EM, S&P 500), gold, clean-energy equities, and macro-uncertainty indicators. The TVP–VAR maps dynamic spillovers among the ASEAN-5 and external nodes, while the GARCH–MIDAS relates the slow component of variance to investor attention. The evidence indicates that connectedness tightens in stress regimes, with global benchmarks and policy uncertainty acting as transmitters and ASEAN equities absorbing incoming shocks. In the volatility block, the Google-based IS factor exerts a negative and economically meaningful influence on the long-run component over and above global uncertainty, supporting the view that attention and uncertainty function as complementary channels of risk propagation. The integrated framework is parsimonious and replicable, and it offers actionable insights for regime-aware risk management, policy communication, and the timing of green-finance issuance in emerging markets. Full article
(This article belongs to the Topic Advanced Techniques and Modeling in Business and Economics)
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16 pages, 802 KB  
Article
Policy Implications and Risk Mitigation of Greenhouse Gas Management in the Renewable Energy Sector
by Bogdan Firtescu, Laurentiu Droj, Adrian Florea and Bogdan-Florin Filip
Risks 2025, 13(12), 250; https://doi.org/10.3390/risks13120250 - 11 Dec 2025
Viewed by 395
Abstract
The transition toward renewable energy systems offers significant opportunities to reduce greenhouse gas (GHG) emissions, while also introducing new challenges in risk management and policy design. This study examines the long-term effects of renewable energy consumption, the risk factors associated with environmental taxation, [...] Read more.
The transition toward renewable energy systems offers significant opportunities to reduce greenhouse gas (GHG) emissions, while also introducing new challenges in risk management and policy design. This study examines the long-term effects of renewable energy consumption, the risk factors associated with environmental taxation, and public expenditure on greenhouse gas (GHG) emissions across 27 European Union countries over a period of 22 years. Using panel data techniques—specifically the Fully Modified Ordinary Least Squares (FMOLS) and Dynamic Ordinary Least Squares (DOLS) estimators—the analysis identifies robust cointegrating relationships among environmental, fiscal, and energy variables. The joint null hypothesis (H0) states that renewable energy consumption, environmental taxation, and public environmental expenditure do not exert a statistically significant negative long-run effect on greenhouse gas (GHG) emissions in the European Union (i.e., none of these variables contributes to reducing GHG emissions in the long run). The findings show that renewable energy consumption and environmental taxes significantly and negatively affect GHG emissions, confirming their effectiveness as instruments for emission risk mitigation. Pollution taxes display the strongest elasticity among fiscal measures, indicating their pivotal role in carbon reduction strategies. Furthermore, public expenditure, particularly in waste management, meaningfully contributes to long-term emission reductions. These results highlight that a cohesive policy framework combining renewable energy development, targeted taxation, and strategic public investment can effectively minimize the environmental and economic risks associated with decarbonization. The study provides valuable empirical evidence for policymakers and risk analysts, underscoring the importance of integrated fiscal and energy policies in achieving sustainable climate risk management across the European Union Full article
(This article belongs to the Special Issue Risks in Finance, Economy and Business on the Horizon in the 2030s)
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30 pages, 513 KB  
Article
From Placement to Integration: A Parametric Study of Cryptocurrency-Based Money Laundering Techniques
by Hugo Almeida, Pedro Pinto and Ana Fernández Vilas
Risks 2025, 13(12), 249; https://doi.org/10.3390/risks13120249 - 11 Dec 2025
Viewed by 509
Abstract
The widespread adoption of cryptocurrencies has transformed the financial landscape by enabling swift, decentralised transactions. However, the pseudonymous nature of digital currencies has also fuelled illicit activities, such as money laundering. Criminals perform money laundering to access illicitly acquired funds without detection and [...] Read more.
The widespread adoption of cryptocurrencies has transformed the financial landscape by enabling swift, decentralised transactions. However, the pseudonymous nature of digital currencies has also fuelled illicit activities, such as money laundering. Criminals perform money laundering to access illicitly acquired funds without detection and convert illegally obtained assets into untraceable commodities, seamlessly integrated into the financial system. Although new regulatory measures have been introduced, illicit actors continue to exploit various methods, from peer-to-peer exchanges to cryptocurrency mixing services, to obscure the origins of illegal funds. This study presents a parametric analysis of these methods, examining dimensions such as duration, number of actors, contextual requirements, operational difficulty, traceability, and costs across each stage of the money laundering process: placement, layering, and integration. The analysis indicates that, while more sophisticated techniques may provide a higher degree of anonymity, they simultaneously require specialised technical expertise and meticulous planning. Consequently, there is a trade-off between the level of privacy attainable and the operational complexity inherent to each method. By systematically comparing these strategies, this analysis aims to contribute to a deeper understanding of cryptocurrency-based money laundering techniques, providing insight for more effective prevention and mitigation measures for both regulatory authorities and the financial sector. Full article
(This article belongs to the Special Issue Cryptocurrency Pricing and Trading)
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23 pages, 790 KB  
Article
Assessing the Impact of Financial Risk and Ownership Structure on ESG Disclosure: Insights from the Energy Sector in Indonesia
by Aloysius Harry Mukti, Oda I. B. Hariyanto and Oswald Timothy Edward
Risks 2025, 13(12), 248; https://doi.org/10.3390/risks13120248 - 11 Dec 2025
Viewed by 709
Abstract
Environmental, social, and governance (ESG) disclosure has gained global prominence, yet its implementation in emerging markets particularly in environmentally intensive sectors remains fragmented. In Indonesia’s energy industry, ESG transparency still struggles to meet rising global expectations, especially amid increased foreign investment flows and [...] Read more.
Environmental, social, and governance (ESG) disclosure has gained global prominence, yet its implementation in emerging markets particularly in environmentally intensive sectors remains fragmented. In Indonesia’s energy industry, ESG transparency still struggles to meet rising global expectations, especially amid increased foreign investment flows and sustainability demands following the country’s G20 presidency. While prior research has separately examined financial performance and ownership structure, fewer studies have explored their combined impact on ESG disclosure within this institutional context. This study investigates how financial risk indicators and ownership composition influence ESG disclosure levels among publicly listed energy firms in Indonesia during the 2020–2024 period. Drawing on 98 firm-year observations, ESG performance is measured using the Nasdaq ESG Reporting Guide, and multiple linear regression is used to assess the influence of return on assets, liquidity, and various ownership types (managerial, institutional, and foreign), controlling for firm age and COVID-19 impact. The findings reveal that institutional ownership significantly enhances ESG disclosure, while other predictors such as return on assets, liquidity, managerial, and foreign ownership show no meaningful effect. The results underscore the role of institutional investors as key drivers of ESG adoption, offering insights into how ownership structures shape sustainability reporting in a high-impact sector of an emerging economy. Full article
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24 pages, 625 KB  
Article
The Regress of Uncertainty and the Forecasting Paradox
by Nassim Nicholas Taleb and Pasquale Cirillo
Risks 2025, 13(12), 247; https://doi.org/10.3390/risks13120247 - 10 Dec 2025
Viewed by 2660
Abstract
We show that epistemic uncertainty–our iterated ignorance about our own ignorance–inevitably thickens statistical tails, even under perceived thin-tailed environments from past realizations. Any claim of precise risk carries a margin of error, and that margin itself is uncertain, in an infinite regress of [...] Read more.
We show that epistemic uncertainty–our iterated ignorance about our own ignorance–inevitably thickens statistical tails, even under perceived thin-tailed environments from past realizations. Any claim of precise risk carries a margin of error, and that margin itself is uncertain, in an infinite regress of doubt. This “errors-on-errors” mechanism rules out thin-tailed certainty: predictive laws must be heavier-tailed than their in-sample counterparts. The result is the Forecasting Paradox: the future is structurally more extreme than the past. This insight collapses branching scenarios into a single heavy-tailed forecast, with direct implications for risk management, scientific modeling, and AI safety. Full article
(This article belongs to the Special Issue Innovative Quantitative Methods for Financial Risk Management)
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27 pages, 6209 KB  
Article
Asymmetric and Time-Varying Connectedness of FinTech with Equities, Bonds, and Cryptocurrencies: A Quantile-on-Quantile Perspective
by Mohammad Sharif Karimi, Omar Esqueda and Naveen Mahasen Weerasinghe
Risks 2025, 13(12), 246; https://doi.org/10.3390/risks13120246 - 10 Dec 2025
Viewed by 803
Abstract
This study employs a quantile-on-quantile connectedness approach to analyze the asymmetric, distribution-dependent, and time-varying spillovers between FinTech indices and traditional financial markets. The results show that spillovers are concentrated in the distribution tails, with FinTech indices exhibiting strong co-movements with equities and Bitcoin [...] Read more.
This study employs a quantile-on-quantile connectedness approach to analyze the asymmetric, distribution-dependent, and time-varying spillovers between FinTech indices and traditional financial markets. The results show that spillovers are concentrated in the distribution tails, with FinTech indices exhibiting strong co-movements with equities and Bitcoin under extreme conditions, while linkages with U.S. Treasury bonds are weaker and often inverse. Net connectedness analysis reveals that the S&P 500 and Bitcoin act as the primary transmitters of shocks into FinTech indices, whereas Treasuries generally serve as receivers, except during stress episodes when safe-haven flows or heightened credit risk reverse the direction of spillovers. The dynamic ∆TCI (Difference between the total direct connectedness and the reverse total connectedness) further demonstrates that FinTech indices serve as net transmitters in stable markets but become receivers during crises such as the COVID-19 pandemic, the Federal Reserve’s tightening cycle of 2022–2023, and the FTX-driven crypto collapse. Segmental heterogeneity is also evident: distributed ledger firms are highly sensitive to cryptocurrency dynamics, alternative finance providers respond strongly to both equity and bond markets, and digital payments firms are primarily influenced by equity spillovers. Overall, the findings underscore FinTech’s dual role—transmitting shocks during tranquil periods but amplifying systemic vulnerabilities during crises. For investors, diversification benefits are state-dependent and largely disappear under adverse conditions. For regulators and policymakers, the results highlight the systemic importance of FinTech–equity and crypto–ledger linkages and the need to integrate FinTech exposures into macroprudential surveillance to contain volatility spillovers and safeguard financial stability. Full article
(This article belongs to the Special Issue Critical Issues in Financial Markets: Risk-Return Tradeoff and Investment Strategies)
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23 pages, 1866 KB  
Article
The Sovereign Risk Amplifies ESG Market Extremes: A Quantile-Based Factor Analysis
by Oscar Walduin Orozco-Cerón, Orlando Joaqui-Barandica and Diego F. Manotas-Duque
Risks 2025, 13(12), 245; https://doi.org/10.3390/risks13120245 - 10 Dec 2025
Viewed by 462
Abstract
This study examines how sovereign risk shapes the financial performance of sustainable investments, using the MSCI Emerging Markets ESG Index as a reference. The analysis covers 24 emerging and frontier economies from Latin America, Asia, the Middle East, and Eastern Europe during 2016–2025, [...] Read more.
This study examines how sovereign risk shapes the financial performance of sustainable investments, using the MSCI Emerging Markets ESG Index as a reference. The analysis covers 24 emerging and frontier economies from Latin America, Asia, the Middle East, and Eastern Europe during 2016–2025, a period marked by major global disruptions such as the COVID-19 crisis and post-2022 financial tightening. Sovereign risk dimensions are extracted through Principal Component Analysis (PCA) applied to sovereign CDS spreads, identifying a systemic component linked to global shocks and a structural component associated with domestic fundamentals and governance quality. These factors are integrated into a quantile regression framework alongside control variables—oil prices, interest rates, and global equity indices—capturing key macro-financial transmission channels. Results show a nonlinear, quantile-dependent relationship: systemic risk intensifies ESG losses under adverse conditions, while structural improvements support gains in upper quantiles. Control variables behave as expected, confirming the macro-financial sensitivity of ESG performance. The findings reveal that ESG returns are state-dependent and strongly influenced by sovereign credit dynamics, especially in emerging markets where external shocks and institutional fragility intersect. Strengthening sovereign governance and integrating risk diagnostics into ESG assessments are essential steps to enhance resilience and credibility in sustainable finance. Full article
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28 pages, 1940 KB  
Article
Optimal Choice of Crop Insurance: The Case of Winter Barley in France
by Diana Dorobantu and Gia Hien Pham
Risks 2025, 13(12), 244; https://doi.org/10.3390/risks13120244 - 9 Dec 2025
Viewed by 224
Abstract
This paper analyzes how the agricultural insurance market is adapting to climate change, particularly as extreme weather events become more frequent and severe. We focus on the optimal decision faced by a risk-averse farmer who wants to insure their crop while making savings. [...] Read more.
This paper analyzes how the agricultural insurance market is adapting to climate change, particularly as extreme weather events become more frequent and severe. We focus on the optimal decision faced by a risk-averse farmer who wants to insure their crop while making savings. They can choose between a traditional loss-based insurance, index-based insurance or a mix of both. By maximizing the farmer’s CARA utility function, we show that in some cases, a mixed insurance strategy is more advantageous than a single contract. In our model, the farmer insures only part of the crop when the market interest rate is strictly positive. Demand for traditional and index insurance depends on their respective prices. Highly risk-averse farmers prefer traditional insurance. A numerical application to the French agriculture sector indicates that mean spring temperature primarily affects winter barley yield and could therefore be the main indicator for index-based insurance design. Insurance simulations using the theoretical model and the estimated results further illustrate these findings. Full article
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28 pages, 559 KB  
Article
Institutional Investor Diversity, Herding Behavior, and Systemic Financial Risk: Evidence from China
by Siyu Zhang, Wenlong Miao and Yuqing Zhang
Risks 2025, 13(12), 243; https://doi.org/10.3390/risks13120243 - 8 Dec 2025
Viewed by 821
Abstract
Institutional investors exert significant influence on the operations and development of financial institutions, with different categories of investors playing distinct roles. We contend that institutional investor diversity may affect systemic financial risk. This study proposes novel measures of institutional investor diversity across 84 [...] Read more.
Institutional investors exert significant influence on the operations and development of financial institutions, with different categories of investors playing distinct roles. We contend that institutional investor diversity may affect systemic financial risk. This study proposes novel measures of institutional investor diversity across 84 China’s financial institutions and employs Extreme Value Theory (EVT) to estimate systemic financial risk. Based on this, we empirically examine the relationship and underlying mechanisms. Baseline regression indicates that greater institutional investor diversity plays an effective role in controlling systemic financial risk. We further find that institutional investor diversity significantly suppresses herding behavior, thereby indirectly reducing systemic risk. Moreover, this effect is more pronounced in financial institutions operating in more developed market environments, under stronger external supervision, and with higher levels of technological advancement, as well as in securities firms. These findings not only contribute to the literature on the economic impact of institutional investors but also provide valuable insights for strengthening systemic financial risk control. Full article
(This article belongs to the Special Issue Systemic Risk in the Financial System: New Developments and Challenges)
21 pages, 437 KB  
Article
The Impact of Environmental, Social, and Governance Disclosure on the Firm Value of Non-Financial Firms Listed in South Africa
by Thabiso Sthembiso Msomi, Michael Akinola Aruwaji and Dipakiso Clara Msiza
Risks 2025, 13(12), 242; https://doi.org/10.3390/risks13120242 - 8 Dec 2025
Viewed by 912
Abstract
This study examines the impact of Environmental, Social, and Governance (ESG) disclosures on the firm valuation of non-financial firms listed in South Africa, using Tobin’s Q as a firm value proxy. Using a panel data approach of 642 firm-year observations from 2017 to [...] Read more.
This study examines the impact of Environmental, Social, and Governance (ESG) disclosures on the firm valuation of non-financial firms listed in South Africa, using Tobin’s Q as a firm value proxy. Using a panel data approach of 642 firm-year observations from 2017 to 2022, the study applies Fixed Effects, Random Effects, and Generalized Method of Moments (GMM) estimators to address possible endogeneity concerns. The results consistently show that, for the whole sample, ESG disclosures are positively and significantly related to firm value, thus supporting the view that markets reward transparency and sustainability initiatives. Firm size and liquidity also have positive impacts, while financial leverage has an inverse relationship with firm value. Subgroup regression analysis shows significant sectoral differences: ESG disclosure in non-manufacturing companies has a positive and significant relationship with firm value, in line with stakeholder and signaling theories, emphasizing the premium for intangible assets like reputation and trust. However, in manufacturing companies, ESG disclosure is negatively and significantly associated with firm value, implying concerns among investors regarding compliance costs, strategic misalignment, or possible “greenwashing.” The study contributes to the emerging-market literature by (i) introducing a PCA-based ESG index specific to JSE-listed non-financials, (ii) triangulating results across static and dynamic specifications to ensure robustness, and (iii) uncovering sectoral heterogeneity that has been largely overlooked. The research also has practical implications for corporate managers, policymakers, and investors on the alignment of ESG practices to industry attributes for long-term value optimization. Full article
22 pages, 476 KB  
Article
Economic Analysis of Global Catastrophic Risks Under Uncertainty
by Wei-Chun Tseng, Chi-Chung Chen and Tsung-Ling Hwang
Risks 2025, 13(12), 241; https://doi.org/10.3390/risks13120241 - 5 Dec 2025
Viewed by 862
Abstract
Background: Despite the apparent importance of global catastrophe risks (GCRs), human society has invested relatively little to reduce them. One possible reason is that we do not understand the significance of reducing GCRs, especially when measured in the monetary terms that we typically [...] Read more.
Background: Despite the apparent importance of global catastrophe risks (GCRs), human society has invested relatively little to reduce them. One possible reason is that we do not understand the significance of reducing GCRs, especially when measured in the monetary terms that we typically use to make decisions. Consequently, we cannot compare them to other issues that influence our decision making and well-being. Purpose: In this study, we quantified the benefits of reducing all non-natural GCRs to highlight their importance. Method: We used a probabilistic model for simulation. Due to limited information, we introduced concepts and assumptions to aid the calculations, such as steady-state economics and sensitivity analyses. In addition, we converted expert opinions to help us focus on a narrower range of risk levels. Results: Within a considerably plausible range of the GCR, we found the following: 1. The benefits of halving the overall non-natural GCR over the next 100 years are substantial. 2. The expected human survival years are sensitive to the mitigation effort but robust to the horizon length. 3. The higher the population growth rate, the larger the expected life years saved. 4. The expected monetary benefits are positively related to the GWP per capita growth rate, mitigation period, and magnitude of natural GCRs but are negatively related to the discounting rate. Significance: The human species is actually facing multiple GCRs simultaneously. In the literature, there is still a gap in quantifying the benefits of reducing all non-natural GCRs/ERs in the coming century while accounting for the very long run on a million-year scale. This article fills such a gap, and the results may serve as a reference for global policymaking to handle this global public issue. Full article
(This article belongs to the Special Issue Tail Risk Analysis and Management)
34 pages, 2785 KB  
Article
Machine Learning Analysis of Financial Risk Dynamics in Micro-, Small, and Medium Enterprises
by Dražen Božović, Nataša Perović, Marinko Aleksić, Ivana Rašović and Oto Iker
Risks 2025, 13(12), 240; https://doi.org/10.3390/risks13120240 - 5 Dec 2025
Viewed by 493
Abstract
This study examines the use of artificial neural networks (ANNs) to classify financial risks in micro-, small-, and medium-sized enterprises (MSMEs) in Montenegro and the wider Western Balkan region. The economies in this region share structural similarities, such as a high concentration of [...] Read more.
This study examines the use of artificial neural networks (ANNs) to classify financial risks in micro-, small-, and medium-sized enterprises (MSMEs) in Montenegro and the wider Western Balkan region. The economies in this region share structural similarities, such as a high concentration of MSMEs, limited access to finance, and vulnerability to macroeconomic volatility, which make financial risk assessment particularly challenging. Traditional statistical and econometric methods often fail to capture the complex, nonlinear interdependencies among financial and operational indicators, resulting in the inaccurate classification of high-risk MSMEs. By applying advanced machine learning (ML) techniques, neural networks (NNs) can identify intricate patterns in multidimensional financial data, significantly improving the accuracy and reliability of risk classification. In this research, a predictive model was developed using key financial and operational variables of MSMEs, enabling the accurate classification of MSMEs in terms of financial instability and insolvency. Empirical validation shows that NNs outperform conventional methods in accuracy, sensitivity, and generalisation. This approach offers tangible benefits for investors, credit institutions, and MSME managers, supporting improvements in early warning systems, optimisation of credit decision-making, and strengthening MSMEs’ financial resilience and sustainability. The methodology also advances risk quantification tools, providing robust indicators for strategic planning and resource management. By focusing the analysis on Montenegro and the Western Balkans, this study demonstrates that regional economic and structural similarities support the adaptation of NN models for precise financial risk classification, offering actionable insights to enhance MSME performance and regional economic stability. Full article
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15 pages, 438 KB  
Article
Gender as a Risk Factor: A Test of Gender-Neutral Pricing in Lithuania’s P2P Market
by Mindaugas Jasas and Aiste Lastauskaite
Risks 2025, 13(12), 239; https://doi.org/10.3390/risks13120239 - 5 Dec 2025
Viewed by 379
Abstract
European Union legislation, particularly Council Directive 2004/113/EC, mandates gender neutrality in credit scoring to prevent discrimination. However, this creates a regulatory paradox if gender is a statistically relevant predictor of default risk. This study investigates this “fairness-through-unawareness” approach by empirically testing for systematic [...] Read more.
European Union legislation, particularly Council Directive 2004/113/EC, mandates gender neutrality in credit scoring to prevent discrimination. However, this creates a regulatory paradox if gender is a statistically relevant predictor of default risk. This study investigates this “fairness-through-unawareness” approach by empirically testing for systematic mispricing. We employ a twofold econometric analysis on a dataset of consumer loans from a Lithuanian peer-to-peer platform. After data preparation for the regression, the sample consists of 9707 loans. First, logistic regression is used to model actual default risk, controlling for credit rating, age, loan amount, and education. Second, Ordinary Least Squares (OLS) regression is used to model the interest rate set by the platform. The Logit model finds that gender is a highly significant predictor of default (p < 0.001), with male borrowers associated with a higher probability of default. Conversely, the OLS model finds that gender is not a statistically significant factor in loan pricing (p = 0.263), confirming the platform’s compliance with EU law. The findings empirically demonstrate the regulatory paradox: the legally compliant, gender-blind pricing model fails to account for a significant risk differential. This leads to systematic risk mispricing and an implicit cross-subsidy from lower-risk female borrowers to higher-risk male counterparts, highlighting a critical tension between regulatory intent and outcome fairness. The analysis is limited to observed loan-level characteristics; it does not incorporate household composition or the internal structure of the platform’s proprietary scoring model. Full article
16 pages, 2090 KB  
Article
SHAP Stability in Credit Risk Management: A Case Study in Credit Card Default Model
by Luyun Lin and Yiqing Wang
Risks 2025, 13(12), 238; https://doi.org/10.3390/risks13120238 - 3 Dec 2025
Viewed by 1632
Abstract
The rapid growth of the consumer credit card market has introduced substantial regulatory and risk management challenges. To address these challenges, financial institutions increasingly adopt advanced machine learning models to improve default prediction and portfolio monitoring. However, the use of such models raises [...] Read more.
The rapid growth of the consumer credit card market has introduced substantial regulatory and risk management challenges. To address these challenges, financial institutions increasingly adopt advanced machine learning models to improve default prediction and portfolio monitoring. However, the use of such models raises additional concerns regarding transparency and fairness for both institutions and regulators. In this study, we investigate the consistency of Shapley Additive Explanations (SHAPs), a widely used Explainable Artificial Intelligence (XAI) technique, through a case study on credit card probability-of-default modeling. Using the Default of Credit Card dataset containing 30,000 consumer credit accounts information, we train 100 Extreme Gradient Boosting (XGBoost) models with different random seeds to quantify the consistency of SHAP-based feature attributions. The results show that the feature SHAP stability is strongly associated with feature importance level. Features with high predictive power tend to yield consistent SHAP rankings (Kendall’s W = 0.93 for the top five features), while features with moderate contributions exhibit greater variability (Kendall’s W = 0.34 for six mid-importance features). Based on these findings, we recommend incorporating SHAP stability analysis into model validation procedures and avoiding the use of unstable features in regulatory or customer-facing explanations. We believe these recommendations can help enhance the reliability and accountability of explainable machine learning framework in credit risk management. Full article
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18 pages, 422 KB  
Article
The Impact of ESG Performance and Corporate Governance on Dividend Policies: Empirical Analysis for European Companies
by Hichem Saidi, Soufiene Tabessi and Abdelaziz Hakimi
Risks 2025, 13(12), 237; https://doi.org/10.3390/risks13120237 - 3 Dec 2025
Viewed by 863
Abstract
Understanding how ESG performance and corporate governance practices influence financial policies has become increasingly critical for investors, regulators, and other stakeholders. This study specifically examines the simultaneous independent effects of corporate social responsibility (CSR) performance and board characteristics on the dividend payouts (DIV) [...] Read more.
Understanding how ESG performance and corporate governance practices influence financial policies has become increasingly critical for investors, regulators, and other stakeholders. This study specifically examines the simultaneous independent effects of corporate social responsibility (CSR) performance and board characteristics on the dividend payouts (DIV) of European companies. To control for unobserved heterogeneity within firms, we initially used fixed and random effects models (FE and RE). Additionally, to address potential endogeneity issues and capture the dynamic nature of dividend behavior, the System Generalized Method of Moments (SGMM) approach was performed as a robustness check. The analysis is based on a comprehensive panel dataset covering 1376 firms across 23 European countries over the period 2014–2023. Empirical results from both FE and RE models and SGMM indicate that CSR performance, gender diversity, cultural diversity, and financial expertise on the board positively influence dividend payouts, while larger board size, greater board independence, and CEO duality are associated with lower dividend payouts. These findings highlight the critical role of ESG and governance factors in shaping corporate financial policies and provide valuable insights for policymakers, investors, and corporate managers. Full article
20 pages, 2521 KB  
Article
A Risk-Aware Dynamic Credit Allocation Mechanism in Green Supply Chains: An Agent-Based Model with ESG Metrics
by Yuansheng Zhang, Ping Song and Qifeng Yang
Risks 2025, 13(12), 236; https://doi.org/10.3390/risks13120236 - 1 Dec 2025
Viewed by 590
Abstract
Integrating Environmental, Social, and Governance (ESG) metrics into supply chain finance is critical for promoting sustainable development. However, the dynamic mechanisms through which real-time ESG performance influences credit allocation and, consequently, shapes credit risk and environmental risk exposures for financial institutions, remain poorly [...] Read more.
Integrating Environmental, Social, and Governance (ESG) metrics into supply chain finance is critical for promoting sustainable development. However, the dynamic mechanisms through which real-time ESG performance influences credit allocation and, consequently, shapes credit risk and environmental risk exposures for financial institutions, remain poorly understood, especially when compared to traditional static and retrospective ESG evaluations. To address this, we developed an agent-based model that simulates interactions among green enterprises, a financial institution, and a regulator, featuring a dynamic credit algorithm that adjusts credit lines based on real-time ESG scores. Our simulations demonstrate that ESG-driven credit policies significantly boost green technology adoption among SMEs, raising adoption rates from 20% to over 85% under strong incentives, which in turn drives a substantial reduction of the supply chain’s carbon footprint by more than 50%. Notably, this environmental benefit is achieved without a commensurate surge in credit risk, as the non-performing loan ratio only experienced a moderate increase. Additionally, sensitivity analysis reveals a non-linear relationship between the ESG weighting in credit decisions and environmental outcomes, identifying a critical threshold for policy effectiveness. Our findings offer risk managers and policymakers evidence-backed strategies for designing dynamic incentives that effectively promote supply chain decarbonization while managing associated financial risks. Full article
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29 pages, 1065 KB  
Article
Emission Performance, Environmental Disclosure, and Firm Value: Evidence from Southeast Asia
by Alya Rahma Munir and Arie Pratama
Risks 2025, 13(12), 235; https://doi.org/10.3390/risks13120235 - 1 Dec 2025
Viewed by 1208
Abstract
This study investigates the relationship between emission performance, environmental disclosure, and firm value in Southeast Asia, where climate-related risks are increasingly shaping corporate strategies and investor decisions. Using a sample of 206 listed firms from Indonesia, Malaysia, Singapore, and Thailand over 2022–2023, the [...] Read more.
This study investigates the relationship between emission performance, environmental disclosure, and firm value in Southeast Asia, where climate-related risks are increasingly shaping corporate strategies and investor decisions. Using a sample of 206 listed firms from Indonesia, Malaysia, Singapore, and Thailand over 2022–2023, the analysis applies a 12-item environmental disclosure index and emission scores from Refinitiv LSEG, with firm value measured by the price-to-book ratio. Structural Equation Modeling (SEM) is employed to test causal pathways, complemented by ANOVA to explore cross-country and cross-industry differences. The results show that emission performance significantly enhances environmental disclosure, consistent with signaling theory and the resource-based view, where superior performance motivates firms to communicate credibility and differentiate themselves. However, environmental disclosure does not exert a significant direct effect on firm value, highlighting a disclosure–value gap in emerging markets where reporting remains heterogeneous and less valued by investors. Country-level differences suggest stronger performance in Indonesia, Singapore, and Thailand compared to Malaysia, while industry-level analysis shows that health care, energy, and financial firms lead in both emission management and disclosure. The findings provide implications for regulators, firms, and investors by underscoring the need for stronger ESG reporting frameworks and more credible disclosure practices to strengthen value relevance. Full article
(This article belongs to the Special Issue Environmental, Social, and Governance (ESG) and Corporate Risk-Taking Capability)
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18 pages, 693 KB  
Article
Employee Stock Ownership Plans and Market Stability: A Longitudinal Analysis of Stock Price Crash Risk in China
by Mengfei Liu, Xiyuan Jiang and Xuyan Tong
Risks 2025, 13(12), 234; https://doi.org/10.3390/risks13120234 - 1 Dec 2025
Viewed by 728
Abstract
Reducing stock price crash risk is vital for capital market stability, particularly in emerging economies such as China. This study investigates whether Employee Stock Ownership Plans (ESOPs) can mitigate crash risk by analyzing panel data from A-share listed firms between 2014 and 2022. [...] Read more.
Reducing stock price crash risk is vital for capital market stability, particularly in emerging economies such as China. This study investigates whether Employee Stock Ownership Plans (ESOPs) can mitigate crash risk by analyzing panel data from A-share listed firms between 2014 and 2022. In contrast to prior research that has largely centered on managers and controlling shareholders, we highlight employees as active participants in corporate governance. Employing firm, year, and industry fixed effects, together with propensity score matching and instrumental variable techniques, we find robust evidence that ESOPs significantly reduce crash risk. Mediation analyses indicate that this effect operates through reduced agency costs both between managers and shareholders and between controlling and minority shareholders, as well as enhanced corporate productivity. Moderation tests further show that ESOPs are most effective when investor attention is high and when exit threats from non-controlling major shareholders are stronger. Heterogeneity analyses reveal that ESOPs exert greater influence in non-state-owned enterprises, in eastern regions, in firms with higher employee participation, and when shares are sourced from the secondary market. By extending the observation window to nearly a decade and deploying multiple robustness checks, this study provides one of the most comprehensive examinations of ESOPs and crash risk to date. It contributes to the literature by reframing employees as central actors in market stability and offers actionable insights for managers, investors, and regulators seeking to enhance corporate governance and reduce systemic risk. Full article
(This article belongs to the Special Issue Quantitative Analysis and Mathematical Modeling in Economics and Financial Decision-Making)
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23 pages, 1113 KB  
Article
Optimal Investment Considerations for a Single Cohort Life Insurance Portfolio
by Sari Cahyaningtias, Petar Jevtić, Carl Gardner and Traian A. Pirvu
Risks 2025, 13(12), 233; https://doi.org/10.3390/risks13120233 - 1 Dec 2025
Viewed by 438
Abstract
This study examines the portfolio optimization problem of an insurance company that issues an annuity, receives the associated premiums as a lump sum, and invests in a financial market. The insurer’s objective is to determine an investment strategy that minimizes the likelihood of [...] Read more.
This study examines the portfolio optimization problem of an insurance company that issues an annuity, receives the associated premiums as a lump sum, and invests in a financial market. The insurer’s objective is to determine an investment strategy that minimizes the likelihood of defaulting on annuity payments before ceasing operations, where default occurs if the portfolio value, net of the annuity liability, becomes negative. Unlike the previous work, here the mortality intensity is stochastic and follows a Cox–Ingersoll–Ross (CIR) process. Dynamic programming is employed, and the value function is characterized by a Hamilton–Jacobi–Bellman (HJB) equation, and the former is linearized through the Legendre transform. Numerical results show that default probability declines with higher initial wealth and mortality intensity, while stochastic mortality volatility has little impact—though slightly higher volatility marginally reduces default risk. Optimal stock investment falls with increasing wealth and mortality intensity, and is nearly constant for low wealth levels. Mortality volatility has minimal influence, but a higher Sharpe ratio raises optimal investment, underscoring the role of risk-adjusted returns. Full article
(This article belongs to the Special Issue Advancements in Actuarial Mathematics and Insurance Risk Management)
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28 pages, 735 KB  
Article
Bridging Transparency and Risk Nexus: Does ESG Performance, Financial Reporting Quality, and Corporate Risk-Taking Matter? Evidence from Indonesia
by Yanuar Bachtiar, Mujennah and Nirza Marzuki Husien
Risks 2025, 13(12), 232; https://doi.org/10.3390/risks13120232 - 30 Nov 2025
Viewed by 1348
Abstract
This study investigates the impact of environmental, social, and governance (ESG) performance on the link between financial reporting quality (FRQ) and corporate risk-taking (CRT). Building upon agency and stakeholder theories, we contend that ESG practices represent a transparency mechanism that is distinct from [...] Read more.
This study investigates the impact of environmental, social, and governance (ESG) performance on the link between financial reporting quality (FRQ) and corporate risk-taking (CRT). Building upon agency and stakeholder theories, we contend that ESG practices represent a transparency mechanism that is distinct from the mainstream and addresses information asymmetry in environments susceptible to earnings management. Operationalizing the framework with panel data, we estimated panel regression models and generalized structural equation modeling (GSEM) to examine the hypothesized framework. The findings show that ESG performance mediates the relationship between FRQ and CRT. In particular, we found that in weaker institutional environments, higher FRQ is associated with greater ESG engagement, which leads to relatively prudent risk-taking behavior. These results demonstrate the significance of ESG as a governance mechanism and underscore the significant role of ESG in encouraging responsible corporate conduct and curbing excessive risk. This research contributes to the existing literature on integrated reporting and sustainable finance by demonstrating how effective ESG governance can bolster corporate resilience and support long-term value creation, especially within emerging markets. Full article
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47 pages, 1120 KB  
Article
Model Misspecification and Data-Driven Model Ranking Approach for Insurance Loss and Claims Data
by Suparna Basu and Hon Keung Tony Ng
Risks 2025, 13(12), 231; https://doi.org/10.3390/risks13120231 - 28 Nov 2025
Viewed by 291
Abstract
Statistical models are crucial in analyzing insurance loss and claims data, offering insights into various risk elements. The prevailing statistical notion that “all models are wrong, but some are useful” can wield significant influence, particularly when multiple competing statistical models are considered. This [...] Read more.
Statistical models are crucial in analyzing insurance loss and claims data, offering insights into various risk elements. The prevailing statistical notion that “all models are wrong, but some are useful” can wield significant influence, particularly when multiple competing statistical models are considered. This becomes particularly pertinent when all models portray similar characteristics within specific subsets of the support of the random variable under scrutiny. Since the actual model is unknown in practical scenarios, the challenge of model selection becomes daunting, complicating the study of associated characteristics of the actual data generation process. To address these challenges, the concept of model averaging is embraced. Often, averaging over multiple models helps alleviate the risk of model misspecification, as different models may capture distinct aspects of the data or modeling assumptions. This enhances the robustness of the estimation process, yielding a more accurate and reasonable estimate compared to relying solely on a single model. This paper introduces two novel data-based model selection methods—one using the likelihood function and the other using the density power divergence measure. The study focuses on estimating the Value-at-Risk (VaR) for non-life insurance claim size data, providing comprehensive insights into potential losses for insurers. The performance of the proposed procedures is evaluated through Monte Carlo simulations under both uncontaminated conditions and in the presence of data contamination. Additionally, the applicability of the methods is illustrated using two real non-life insurance datasets, with the VaR values estimated at different confidence levels. Full article
(This article belongs to the Special Issue Financial Risk, Actuarial Science, and Applications of AI Techniques)
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20 pages, 1449 KB  
Article
Low Financial Risk of Default and Productive Use of Assets Through Hidden Markov Models
by Alexander Haro, Genaro Sandoval, María Rodríguez, Victor Armijo, Ivonne Arana, William Vasquez, Elizabeth Proaño and Amanda Martínez
Risks 2025, 13(12), 230; https://doi.org/10.3390/risks13120230 - 27 Nov 2025
Viewed by 843
Abstract
This paper analyzes solvency dynamics in Ecuador’s mutualist segment by modeling the joint behavior of the productive-assets-to-total-assets ratio (PATR) and portfolio-specific delinquency rates. Using monthly supervisory data from the Superintendencia de Economía Popular y Solidaria (SEPS) for the full universe of four mutualist [...] Read more.
This paper analyzes solvency dynamics in Ecuador’s mutualist segment by modeling the joint behavior of the productive-assets-to-total-assets ratio (PATR) and portfolio-specific delinquency rates. Using monthly supervisory data from the Superintendencia de Economía Popular y Solidaria (SEPS) for the full universe of four mutualist institutions (2022–2025), we estimate a multivariate Gaussian Hidden Markov Model on system-level aggregates. The model identifies latent regimes that summarize configurations of asset productivity and segmented credit risk, distinguishing relatively sound conditions from episodes of heightened vulnerability. Model selection is based on information criteria, complemented by convergence checks, distributional diagnostics, and alternative covariance specifications to assess robustness. The approach is explicitly framed as diagnostic rather than causal or prescriptive: it does not replace simple thresholds nor calibrate capital buffers, but organizes supervisory information into interpretable solvency states with associated frequencies and expected durations. The framework is transparent and reproducible and provides a baseline for future extensions with longer samples and richer covariates. Full article
(This article belongs to the Special Issue Stochastic Modelling in Financial Mathematics, 2nd Edition)
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16 pages, 404 KB  
Article
Socio-Demographic Predictors of Financial Security Perception: Evidence from the OECD Financial Literacy Survey in Hungary
by Erzsébet Németh, Szilárd Malatyinszki and Botond Géza Kálmán
Risks 2025, 13(12), 229; https://doi.org/10.3390/risks13120229 - 27 Nov 2025
Viewed by 574
Abstract
Purpose of the article: The study aims to explore how demographic characteristics—including gender, age, education, employment type, household composition, and place of residence—affect perceived financial security among Hungarian adults. It seeks to identify which population segments feel most or least financially secure and [...] Read more.
Purpose of the article: The study aims to explore how demographic characteristics—including gender, age, education, employment type, household composition, and place of residence—affect perceived financial security among Hungarian adults. It seeks to identify which population segments feel most or least financially secure and to assess the relationship between socio-demographic factors and subjective financial well-being. Methods: The analysis is based on the OECD Financial Culture Survey conducted in Hungary on a representative sample of 1000 adults. Perceived financial security was measured using four questionnaire items related to financial satisfaction, concerns about expenses, and income sufficiency. Independent t-tests, one-way ANOVA, and Welch’s ANOVA were applied to test group differences. Findings & value added: Results indicate no significant gender differences in perceived financial security, while education and employment status show strong effects: higher educational attainment and self-employment or retirement are associated with greater financial security, whereas lack of formal education and disability predict lower security perceptions. Urban residents, particularly in large cities, report significantly higher perceived security than those in smaller towns. The study contributes to the literature by integrating OECD-level data with demographic analysis, highlighting the role of education and labor market position in shaping subjective financial well-being in Hungary. Full article
14 pages, 743 KB  
Article
Dynamic Connectedness Among Key Financial Markets and the Role of Policy Uncertainty: A Quantile-Based Approach
by Lumengo Bonga-Bonga
Risks 2025, 13(12), 228; https://doi.org/10.3390/risks13120228 - 24 Nov 2025
Viewed by 1274
Abstract
This paper investigates the return spillover dynamics among carry trade, stock, foreign exchange, and commodity markets to identify their roles as net transmitters or receivers of shocks under varying market conditions. Employing a Quantile Vector Autoregressive (QVAR) framework within the network-connectedness approach, the [...] Read more.
This paper investigates the return spillover dynamics among carry trade, stock, foreign exchange, and commodity markets to identify their roles as net transmitters or receivers of shocks under varying market conditions. Employing a Quantile Vector Autoregressive (QVAR) framework within the network-connectedness approach, the analysis captures asymmetric and state-dependent relationships across these markets. In addition, the study examines the influence of U.S. monetary and economic policy uncertainties on the total return spillovers among these markets across different market regimes, using the quantile-in-causality technique. The empirical results reveal that market influence shifts with changing conditions, while total interconnectedness intensifies during periods of elevated uncertainty, particularly throughout the bear market with the total connectedness index reaching 69.97%. Moreover, U.S. Monetary Policy Uncertainty (MPU) exerts varying effects on total connectedness depending on the prevailing market regime, with a pronounced effect at 0.50 quantile, representing stable market regime with no effect at extreme market conditions. These findings offer valuable insights for policymakers and investors, especially regarding the timing of asset allocation and investment decisions under different states of market uncertainty. Full article
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