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Examining Strategies to Manage Climate Risks of PPP Infrastructure Projects
The Association of Financial Knowledge, Attitude, and Behavior with Investment Loss Tolerance: Evidence from Japan
Beyond the Rating: How Disagreement Among ESG Agencies Affects Bond Credit Spreads
Cryptocurrencies as a Tool for Money Laundering: Risk Assessment and Perception of Threats Based on Empirical Research

Journal Description

Risks

Risks is an international, scholarly, peer-reviewed, open access journal for research and studies on insurance and financial risk management. Risks is published monthly online by MDPI.

Open Access— free for readers, with article processing charges (APC) paid by authors or their institutions.
High visibility: indexed within Scopus, ESCI (Web of Science), EconLit, EconBiz, RePEc, and other databases.
Journal Rank: CiteScore - Q1 (Economics, Econometrics and Finance (miscellaneous))
Rapid Publication: manuscripts are peer-reviewed and a first decision is provided to authors approximately 20 days after submission; acceptance to publication is undertaken in 6.6 days (median values for papers published in this journal in the second half of 2025).
Recognition of Reviewers: reviewers who provide timely, thorough peer-review reports receive vouchers for a discount on the APC of their next publication in any MDPI journal, in appreciation of the work done

Impact Factor: 1.5 (2024); 5-Year Impact Factor: 1.7 (2024)

Imprint Information Journal Flyer Open Access ISSN: 2227-9091

Latest Articles

25 pages, 504 KB

Open AccessArticle

The Effect of Economic Policy Uncertainty on Banks: Distinguishing Short- and Long-Term Effects

by Badar Nadeem Ashraf and Ningyu Qian

Risks 2026, 14(1), 18; https://doi.org/10.3390/risks14010018 - 13 Jan 2026

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. [...] Read more.

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

(This article belongs to the Special Issue Empirical Corporate Finance and Banking: Risk Management in an Uncertain World)

26 pages, 656 KB

Open AccessArticle

Corporate Governance in Brazil and Opportunistic Behavior in the Use of Insider Information

by Ana Flávia Albuquerque Ventura, Roberto Frota Decourt and Clea Beatriz Macagnan

Risks 2026, 14(1), 17; https://doi.org/10.3390/risks14010017 - 13 Jan 2026

The opportunistic use of insider information generates adverse effects on capital markets, making its mitigation through robust corporate governance practices. This research analyzes the corporate governance mechanisms that reduce the signs of opportunistic insider trading, grounded in the assumptions of information asymmetry and [...] Read more.

The opportunistic use of insider information generates adverse effects on capital markets, making its mitigation through robust corporate governance practices. This research analyzes the corporate governance mechanisms that reduce the signs of opportunistic insider trading, grounded in the assumptions of information asymmetry and opportunistic behavior. The hypotheses posit that firms listed on the Novo Mercado or Level 2 of Corporate Governance, with more independent boards of directors and greater female representation, active fiscal councils, consolidated ESG practices, non-family ownership structures, robust audit committees, and audits not conducted by Big Four firms, are less prone to opportunistic conduct. The sample comprises 237 firms, representing 51% of companies listed on [B]3 between 2010 and 2021, resulting in a total of 2175 firm-year observations. Panel data analysis supports the proposed hypotheses. The findings indicate that higher levels of corporate governance practices are associated with a lower incidence of opportunistic insider trading in the Brazilian capital market. This study contributes to the literature by highlighting the specific features of the largest stock market in Latin America and emphasizing the role of transparency, formal monitoring, and informal mechanisms, such as social and reputational pressure on insiders, in shaping ethical behavior and curbing the misuse of privileged information. Full article

17 pages, 1573 KB

Open AccessArticle

From Risk to Returns: An Analysis of Asset Quality, Financial Ratios, and Market Valuation in Indian Banks

by Shireen Rosario and Sudha Mavuri

Risks 2026, 14(1), 16; https://doi.org/10.3390/risks14010016 - 13 Jan 2026

This study investigates the interplay between asset quality, financial ratios, and market valuation in Indian commercial banks over a twelve-year period (2014–2025). Using a hybrid approach combining Structural Equation Modeling, correlation analysis, and trend evaluation, the research examines whether Non-Performing Assets (NPAs) influence [...] Read more.

This study investigates the interplay between asset quality, financial ratios, and market valuation in Indian commercial banks over a twelve-year period (2014–2025). Using a hybrid approach combining Structural Equation Modeling, correlation analysis, and trend evaluation, the research examines whether Non-Performing Assets (NPAs) influence market capitalization directly or through Return on Equity (ROE) as an intermediary. The findings reveal that NPAs exert a significant negative impact on both ROE and market value, while Net Interest Margin (NIM) emerges as a strong positive determinant of valuation. Conversely, Capital Adequacy Ratio (CAR), though vital for regulatory compliance, shows no direct effect on market prices. Mediation analysis challenges conventional assumptions, indicating that profitability alone does not fully explain valuation dynamics. These insights underscore the need for integrated strategies addressing asset quality and operational efficiency, offering practical implications for policymakers, investors, and bank management in strengthening resilience and optimizing shareholder value. Full article

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20 pages, 1369 KB

Open AccessArticle

The Relationship Between Psychological Factors and Retirement Financial Plan and Its Gender Difference

by Han Ren and Thien Sang Lim

Risks 2026, 14(1), 15; https://doi.org/10.3390/risks14010015 - 6 Jan 2026

As China’s population ages and the sustainability of the public pension system is at risk, personal savings become crucial. As such, the quality of financial planning for retirement (FPR) has been recognized as a key to safeguarding financial well-being during retirement. This study [...] Read more.

As China’s population ages and the sustainability of the public pension system is at risk, personal savings become crucial. As such, the quality of financial planning for retirement (FPR) has been recognized as a key to safeguarding financial well-being during retirement. This study examines the relationships of two predictors (future time perspective and risk tolerance) and a mediator (subjective financial literacy) in shaping financial planning for retirement, with particular attention to potential gender differences. Using survey data retrieved from respondents aged between 23 and 60 years old, overall sample and gender-based multigroup analysis were used to examine whether gender moderates these relationships. The results reveal that both future time perspective and subjective financial literacy positively influence financial planning for retirement across all gender groups. Notably, we found no significant gender gap in retirement planning behavior. Subjective financial literacy serves as a significant mediator linking both future time perspective and risk tolerance to retirement planning, though the indirect effect of risk tolerance through financial literacy differs significantly between genders. Academically, theoretical propositions related to retirement planning can be accounted for by both genders. Practically, standardized policy can be tailored to address retirement issues across genders. The study emphasizes that financial planning for retirement in China shows no gender gap, and this provides meaningful guidance to policymakers and financial institutions to develop measures to encourage individuals to take financial actions in retirement planning. Finally, the combined interpretation of a strong effect of subjective financial literacy and an insignificant effect of risk tolerance raises concern that adult income earners in China are affected by financial literacy bias when practicing financial retirement planning. Full article

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20 pages, 789 KB

Open AccessArticle

Deep Hybrid CNN-LSTM-GRU Model for a Financial Risk Early Warning System

by Muhammad Ali Chohan, Teng Li, Mohammad Abrar and Shamaila Butt

Risks 2026, 14(1), 14; https://doi.org/10.3390/risks14010014 - 5 Jan 2026

Financial risk early warning systems are essential for proactive risk management in volatile markets, particularly for emerging economies such as China. This study develops a hybrid deep learning model integrating Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM), and Gated Recurrent Units (GRUs) [...] Read more.

Financial risk early warning systems are essential for proactive risk management in volatile markets, particularly for emerging economies such as China. This study develops a hybrid deep learning model integrating Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM), and Gated Recurrent Units (GRUs) to enhance the accuracy and robustness of financial risk prediction. Using firm-level quarterly financial data from Chinese listed companies, the proposed model is benchmarked against standalone CNN, LSTM, and GRU architectures. Experimental results show that the hybrid CNN–LSTM–GRU model achieves superior performance across all evaluation metrics, with prediction accuracy reaching 93.5%, precision reaching 92.2%, recall reaching 91.8%, and F1-score reaching 92.0%, significantly outperforming individual models. Moreover, the hybrid approach demonstrates faster convergence than LSTM and improved class balance compared to CNN and GRU, reducing false negatives for high-risk firms—a critical aspect for early intervention. These findings highlight the hybrid model’s robustness and real-world applicability, offering regulators, investors, and policymakers a reliable tool for timely financial risk detection and informed decision-making. By combining high predictive power with computational efficiency, the proposed system provides a practical framework for strengthening financial stability in emerging and dynamic markets. Full article

(This article belongs to the Special Issue Advances in Volatility Modeling and Risk in Markets)

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31 pages, 2487 KB

Open AccessArticle

Enhancing Predictive Performance of LSTM–Attention Models for Investment Risk Forecasting

by Amina Ladhari and Heni Boubaker

Risks 2026, 14(1), 13; https://doi.org/10.3390/risks14010013 - 5 Jan 2026

For many decades, time-series forecasting has been applied to different problems by scientists and industries. Many models have been introduced for the purpose of forecasting. These advancements have significantly improved the accuracy and reliability of predictions, especially in complex scenarios where traditional methods [...] Read more.

For many decades, time-series forecasting has been applied to different problems by scientists and industries. Many models have been introduced for the purpose of forecasting. These advancements have significantly improved the accuracy and reliability of predictions, especially in complex scenarios where traditional methods struggled. As data availability continues to expand, the integration of machine learning techniques is likely to further enhance forecasting capabilities across various fields. Today, hybrid techniques are gaining popularity, as they combine the advantages of different approaches to deliver improved predictive performance and more advanced visualization analytics for decision support. These hybrid approaches can provide better prediction, and at the same time, they can develop a more sophisticated set of visualization analytics for decision support. Recently, the integration of cross-entropy, fuzzy logic, and attention mechanisms in hybrid forecasting models has enhanced their ability to capture complex and uncertain patterns in financial and energy markets. In this study, we propose a hybrid ANN–LSTM deep learning model optimized with cross-entropy, fuzzy logic, and an attention mechanism to enhance the forecasting of financial and energy time series, specifically Ethereum and natural gas prices. Our models combine the feature extraction strength of ANN with the temporal learning of LSTM, while cross-entropy improves convergence, fuzzy logic handles uncertainty, and attention refines feature weighting. Since inaccurate forecasts can lead to greater estimation uncertainty and increased financial and operational risk, improving predictive reliability is essential for effective risk mitigation. These techniques prove effective not only in improving estimation accuracy but also in minimizing financial risks and supporting more informed investment decisions. Full article

(This article belongs to the Special Issue Artificial Intelligence Risk Management)

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27 pages, 2446 KB

Open AccessArticle

Machine Learning & Artificial Intelligence Powered Credit Scoring Models for Islamic Microfinance Institutions: A Blockchain Approach

by Mohammad Mushfiqul Haque Mukit, Fakhrul Hasan, Tonmoy Choudhury, Amer Al Fadli and Abubaker Fadul

Risks 2026, 14(1), 12; https://doi.org/10.3390/risks14010012 - 5 Jan 2026

Islamic Microfinance Institutions (IMFIs) encounter distinct difficulties with credit scoring because they need to follow Shariah principles that combine riba bans with fair financial dealings regulations. Conventional credit scoring models exhibit two shortcomings: a poor capability to incorporate non-financial behavioral data and inadequate [...] Read more.

Islamic Microfinance Institutions (IMFIs) encounter distinct difficulties with credit scoring because they need to follow Shariah principles that combine riba bans with fair financial dealings regulations. Conventional credit scoring models exhibit two shortcomings: a poor capability to incorporate non-financial behavioral data and inadequate support for Islamic Microfinance Institutions’ requirements. Researchers use machine learning coupled with blockchain technology to create an adaptive Shariah-compliant credit scoring method that solves problems found in standard evaluation systems. Using a dataset of 1275 farmers with 52 weeks of transaction data, we implemented and compared three ML models: Linear Regression, Random Forest, and Gradient Boosting. Data preparation involved addressing 53% missing transaction data, followed by summing weekly financial activity to prepare it for predictive evaluations. Our analysis shows that the Random Forest model produced the best results with an R-squared value of 0.87 and a Mean Squared Error (MSE) of 12.4. In creditworthiness binary classification tasks, Gradient Boosting delivered an F1 score of 0.91 while maintaining precision at 0.89 and recall at 0.93. Blockchain integration exists to protect data through secure mechanisms that also conserve Islamic financial integrity and promote transparency. The research shows how ML and Blockchain technology enable fundamental changes in IMFIs by delivering elevated predictive accuracy, operational enhancements, and complete transparency. The conceptual framework guides ethical financial inclusion strategy by offering a solution for marginalized communities, but remains consistent with global sustainability objectives. The research established foundational elements for implementing cutting-edge technologies within IMFIs, which will promote new economic growth and build confidence in Shariah-compliant financial systems. Full article

(This article belongs to the Special Issue Artificial Intelligence Risk Management)

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30 pages, 4414 KB

Open AccessArticle

Model Averaging and Grid Maps for Modeling Heavy-Tailed Insurance Data

by Lira B. Mothibe and Sandile C. Shongwe

Risks 2026, 14(1), 11; https://doi.org/10.3390/risks14010011 - 5 Jan 2026

This work presents a practical approach to improve risk quantification for heavy-tailed insurance claims through model averaging and grid map visualization, addressing the drawbacks of traditional single “best” model selection commonly used in actuarial and model-fitting literature. This is a data-driven study with [...] Read more.

This work presents a practical approach to improve risk quantification for heavy-tailed insurance claims through model averaging and grid map visualization, addressing the drawbacks of traditional single “best” model selection commonly used in actuarial and model-fitting literature. This is a data-driven study with a focus on Danish fire loss data, where the following are fitted: (i) 16 standard single distributions, (ii) 256 composite distributions, and (iii) 256 mixture distributions; wherein, for the composite and mixture distributions, we focus on the top 20 leading models in terms of the information criterion (i.e., Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC)). Model selection uncertainty is explicitly addressed by AIC and BIC weighted averaging within the Occam’s window (relying on weighted point estimates), while grid maps simultaneously plot information criteria against risk measures, specifically the Value-at-Risk (VaR) and Tail Value-at-Risk (TVaR) at 95% and 99% thresholds, to highlight critical-fit versus tail-risk trade-offs. It is observed that the model-averaged risk measures from composite models align more closely with the empirical values. That is, model-averaged estimates across all categories align closely with empirical VaR_0.95 but conservatively elevate TVaR_0.99, promoting safer capital reserves. Grid maps and model averaging confirm that mixture and composite models better capture the heavy-tailed nature of Danish fire claims data as compared to fitting a single distribution. Full article

(This article belongs to the Special Issue Statistical Models for Insurance)

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39 pages, 609 KB

Open AccessArticle

Unveiling ESG Controversy Risks: A Multi-Criteria Evaluation of Whistleblowing Performance in European Financial Institutions

by George Sklavos, Georgia Zournatzidou and Nikolaos Sariannidis

Risks 2026, 14(1), 10; https://doi.org/10.3390/risks14010010 - 4 Jan 2026

Financial institutions face increased reputational, regulatory, and ethical risks as the frequency and complexity of Environmental, Social, and Governance (ESG) controversies increase. Whistleblowing mechanisms are essential in the context of institutional resilience and the mitigation of internal governance failures. This study quantifies the [...] Read more.

Financial institutions face increased reputational, regulatory, and ethical risks as the frequency and complexity of Environmental, Social, and Governance (ESG) controversies increase. Whistleblowing mechanisms are essential in the context of institutional resilience and the mitigation of internal governance failures. This study quantifies the exposure of 364 European financial institutions to a variety of ESG controversies to assess the effectiveness of whistleblowing during the fiscal year 2024. A whistleblowing performance index that captures the relative influence of ESG-related risk factors—such as corruption allegations, environmental violations, and executive misconduct—is constructed using a hybrid Multi-Criteria Decision-Making (MCDM) framework that is based on Entropy Weighting and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). The results emphasize that the perceived efficacy of whistleblower systems is substantially influenced by the frequency of media-reported controversies and the presence of robust anti-bribery policies. The study provides a data-driven, replicable paradigm for assessing internal governance capabilities in the face of ESG risk pressure. Our findings offer actionable insights for regulators, compliance officers, and ESG analysts who are interested in evaluating and enhancing ethical accountability systems within the financial sector by connecting the domains of financial risk management, corporate ethics, and sustainability governance. Full article

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19 pages, 5812 KB

Open AccessArticle

Credit Risk Management Dynamics: Evidence from Indonesian Rural Banks

by Moch Doddy Ariefianto, Triasesiarta Nur and Bryna Meivitawanli

Risks 2026, 14(1), 9; https://doi.org/10.3390/risks14010009 - 4 Jan 2026

This paper investigates credit risk management as a dynamic system. Panel Vector Autoregression (PVAR) is employed to model interrelationships among four key components: Non-Performing Loans (NPLs), Loan Loss Provision (LLP), loan charge-off (LCO) and capital. The Cost-to-Income ratio (CIR) and Size and Net [...] Read more.

This paper investigates credit risk management as a dynamic system. Panel Vector Autoregression (PVAR) is employed to model interrelationships among four key components: Non-Performing Loans (NPLs), Loan Loss Provision (LLP), loan charge-off (LCO) and capital. The Cost-to-Income ratio (CIR) and Size and Net Profit-to-Equity ratio (ROE) are used as control variables. The panel dataset comprises 1461 conventional rural banks in Indonesia with a quarterly frequency from June 2010 to March 2024. There are several key findings of this study. First, credit risk management practices in rural banks predominantly follow an incurred loss approach, although the expected loss model appears to be more commonly adopted by larger institutions. Second, capital serves a critical function as a buffer against credit losses. Third, subsample investigation reveals a significant role of accounting discretionary. This study offers significant implications for both policy development and academic research in microfinance. Full article

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25 pages, 513 KB

Open AccessArticle

Regulatory Risk in Green FinTech: Comparative Insights from Central Europe

by Simona Heseková, András Lapsánszky, János Kálmán, Michal Janovec and Anna Zalcewicz

Risks 2026, 14(1), 8; https://doi.org/10.3390/risks14010008 - 4 Jan 2026

Green fintech merges sustainable finance with data-intensive innovation, but national translations of EU rules can create regulatory risk. This study examines how such risk manifests in Central Europe and which policy tools mitigate it. We develop a three-dimension framework—regulatory clarity and scope, supervisory [...] Read more.

Green fintech merges sustainable finance with data-intensive innovation, but national translations of EU rules can create regulatory risk. This study examines how such risk manifests in Central Europe and which policy tools mitigate it. We develop a three-dimension framework—regulatory clarity and scope, supervisory consistency, and innovation facilitation—and apply a comparative qualitative design to Hungary, Slovakia, Czechia, and Poland. Using a common EU baseline, we compile coded national snapshots from primary legal texts, supervisory documents, and recent scholarship. Results show material cross-country variation in labelling practice, soft-law use, and testing infrastructure: Hungary combines central-bank green programmes with an innovation hub/sandbox; Slovakia aligns with ESMA and runs hub/sandbox, though the green-fintech pipeline is nascent; Czechia applies a principles-based safe harbour and lacks a national sandbox; and Poland relies on a virtual sandbox and binding interpretations with limited soft law. These choices shape approval timelines, retail penetration, and cross-border portability of green-labelled products. We conclude with a policy toolkit: labelling convergence or explicit safe harbours, a cross-border sandbox federation, ESRS/ESAP-ready proportionate disclosures, consolidation of recurring interpretations into soft law, investment in suptech for green-claims analytics, and inclusion metrics in sandbox selection. Full article

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14 pages, 856 KB

Open AccessArticle

ESG Risk and Agricultural Commodity Integration

by Alper Gormus, Yoav Wachsman and Elif Gormus

Risks 2026, 14(1), 7; https://doi.org/10.3390/risks14010007 - 4 Jan 2026

This study investigates how major agricultural commodities interact with diversified U.S. equity funds, sorted by their environmental, social, and governance (ESG) risk exposure. Using daily Morningstar data on 880 U.S. equity mutual funds, we construct portfolios representing high- and low-ESG-risk equities and examine [...] Read more.

This study investigates how major agricultural commodities interact with diversified U.S. equity funds, sorted by their environmental, social, and governance (ESG) risk exposure. Using daily Morningstar data on 880 U.S. equity mutual funds, we construct portfolios representing high- and low-ESG-risk equities and examine their linkages with prices for eight agricultural commodities. Applying Fourier-augmented Toda–Yamamoto VAR and LM-GARCH models that accommodate both abrupt and gradual structural breaks, we document clear heterogeneity across ESG risk segments. Low-ESG-risk portfolios exhibit minimal price and volatility spillovers from agricultural commodities, whereas high-ESG-risk portfolios display strong and often bidirectional transmissions—particularly for coffee, corn, cotton, livestock, and soybeans. These findings highlight ESG risk exposure as a key dimension shaping commodity–equity integration and provide new evidence on how sustainability-related risks influence equity market vulnerability to commodity shocks. Full article

(This article belongs to the Special Issue Risk Management in Financial and Commodity Markets)

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16 pages, 496 KB

Open AccessArticle

Why Do Family Firms Hold Cash? Agency Conflicts and Valuation Perspectives

by Ghada Tayem, Diana Abu-Ghunmi, Adel Bino and Mohammad Tayeh

Risks 2026, 14(1), 6; https://doi.org/10.3390/risks14010006 - 4 Jan 2026

This study aims to examine whether family firms differ from nonfamily firms in their propensity to save cash, particularly in response to new investment opportunities, and to assess how investors value the cash holdings of family versus nonfamily firms in light of potential [...] Read more.

This study aims to examine whether family firms differ from nonfamily firms in their propensity to save cash, particularly in response to new investment opportunities, and to assess how investors value the cash holdings of family versus nonfamily firms in light of potential agency concerns. The study uses the context of Jordan—a small emerging market characterized by weak investor protection and the dominance of family-managed firms, a setting that exacerbates principal–principal conflicts. Employing treatment effects and propensity score matching estimation techniques to address the endogeneity between family control and firm cash holdings, this study finds that family enterprises maintain significantly higher cash reserves than their nonfamily counterparts. Moreover, the analysis finds that family firms do not exhibit a significantly greater propensity to save cash in response to new investment opportunities, implying that financial flexibility concerns are not significantly different between family and nonfamily firms. However, the results further demonstrate that investors assign a higher valuation to cash held by nonfamily firms, suggesting that investors associate family control with potential agency conflicts regarding the deployment of cash reserves. Full article

27 pages, 1186 KB

Open AccessArticle

Legal Dimensions of Global AML Risk Assessment: A Machine Learning Approach

by Olha Kovalchuk, Ruslan Shevchuk, Serhiy Banakh, Nataliia Holota, Mariana Verbitska and Oleksandra Lutsiv

Risks 2026, 14(1), 5; https://doi.org/10.3390/risks14010005 - 3 Jan 2026

Money laundering poses a serious threat to financial stability and requires effective national frameworks for prevention. This study investigates how the quality of legal and institutional frameworks affects the effectiveness of national anti-money laundering (AML) systems and their implications for financial risk management. [...] Read more.

Money laundering poses a serious threat to financial stability and requires effective national frameworks for prevention. This study investigates how the quality of legal and institutional frameworks affects the effectiveness of national anti-money laundering (AML) systems and their implications for financial risk management. We conducted an empirical analysis of 132 jurisdictions in 2024 using the Basel AML Index (AMLI) and the WJP Rule of Law Index (RLI). The Random Forest method was employed to model the relationship between rule-of-law indicators and AML risk levels. Findings reveal a significant inverse relationship between rule-of-law indicators and AML risk levels, with an overall classification accuracy of 69.6%. The model performed best for low-risk countries (precision 75%, recall 92.31%), moderately for medium-risk countries (precision 65.22%, recall 78.95%), but failed to identify high-risk jurisdictions, suggesting a legal institutional “threshold” necessary for effective AML functioning. Key predictors included protection of fundamental rights and mechanisms for civil oversight, with strong negative correlations between AML risk and criminal justice impartiality (−0.35), civil justice fairness (−0.35), and equality before the law (−0.41). These results show that legal factors strongly affect AML risk and can guide regulators in improving risk-based standards, enhancing regulatory certainty, and managing financial risk. Full article

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21 pages, 766 KB

Open AccessArticle

ESG and Its Components: Impact on Stock Returns Across Firm Sizes in Europe and the United States

by Luis Jacob Escobar-Saldívar, Dacio Villarreal-Samaniego and Roberto J. Santillán-Salgado

Risks 2026, 14(1), 4; https://doi.org/10.3390/risks14010004 - 1 Jan 2026

A longstanding debate in finance concerns the impact of social responsibility actions on firms’ long-term profitability. This study provides a broad analysis on the relationship between ESG, its components, and stock returns. Using a dataset that spans from December 2014 to December 2023, [...] Read more.

A longstanding debate in finance concerns the impact of social responsibility actions on firms’ long-term profitability. This study provides a broad analysis on the relationship between ESG, its components, and stock returns. Using a dataset that spans from December 2014 to December 2023, this research analyzes an annual average of around 2260 publicly traded companies from Europe and the United States. The findings consistently show a negative link between ESG ratings, their components, and stock returns, a result that is possibly explainable by the mixed effect of a reduction of risk (lower risk premium) from social responsibility, and lower profitability from associated costs. The coefficients for ESG and its pillars in explaining stock returns are generally consistent, with a few exceptions for the environmental and governance components. The environmental pillar has a stronger influence in Europe, across firm sizes, while in the US, the effect is limited to larger companies. For governance, variations align with differing ownership structures across regions and changing investor priorities as firms grow, with stronger influence in Midcaps of both regions and in U.S. Large Caps. The effects of overall ESG scores and individual pillars on stock returns across regions, firm sizes, and their interaction, provide a more comprehensive perspective on their relationship. Full article

(This article belongs to the Special Issue Climate Risk in Financial Markets and Institutions)

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45 pages, 10369 KB

Open AccessArticle

Evaluation and Prediction of Stock Market Crash Risk in Mexico Using Log-Periodic Power-Law Modeling

by Suryansh Sunil, Amit Kumar Goyal, Rajesh Mahadeva and Varun Sarda

Risks 2026, 14(1), 3; https://doi.org/10.3390/risks14010003 - 1 Jan 2026

This study applies the Log-Periodic Power-Law (LPPL) framework to three major equity markets—Mexico (IPC), Brazil (IBOVESPA), and the United States (NYSE Composite)—using daily closes from 8 November 1991–30 January 2025 for IPC and NYSE, and 3 May 1993–30 January 2025 for IBOVESPA. Multi-window [...] Read more.

This study applies the Log-Periodic Power-Law (LPPL) framework to three major equity markets—Mexico (IPC), Brazil (IBOVESPA), and the United States (NYSE Composite)—using daily closes from 8 November 1991–30 January 2025 for IPC and NYSE, and 3 May 1993–30 January 2025 for IBOVESPA. Multi-window calibrations (L

ϵ

180, 240, 300, 360, 420) are estimated in raw and log space to evaluate bubble signatures and the stability of the critical time

t_{c}

. Across all indices, log-space fits consistently outperform raw fits in terms of RMSE and

R^{2}

, and longer windows reduce parameter variability, yielding coherent clusters of

t_{c}

. Under full-sample conditions, the LPPL structure points to March–April 2025 for NYSE, mid-October 2025 for IBOVESPA, and October–December 2025 for IPC, while shorter windows pull

t_{c}

forward. A rolling early-warning ensemble translates these estimates into lead-based risk bands, with numerical reporting used when median leads fall just outside the 60-trading-day decision horizon. The early-2025 weakening in the U.S. market is consistent with the NYSE cluster, whereas Brazil and Mexico remain within their projected windows as of September 2025. The analysis highlights the strengths of LPPL—behavioral interpretability and hazard-based framing—while noting limitations such as window sensitivity and parameter sloppiness, reinforcing the need for conservative communication and the use of longer-window weighting in practical applications. Full article

(This article belongs to the Special Issue Stochastic Modelling in Financial Mathematics, 2nd Edition)

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20 pages, 5153 KB

Open AccessArticle

Forecasting Commodity Prices Using Futures: The Case of Copper

by Gonzalo Cortazar, Mariavictoria Enberg and Hector Ortega

Risks 2026, 14(1), 2; https://doi.org/10.3390/risks14010002 - 24 Dec 2025

This paper analyzes three forecasting methods for commodity spot prices and applies them to copper prices. The first method uses futures prices from either LME or COMEX. The second method uses analysts’ consensus expectations, reported by Bloomberg. The third method jointly uses futures [...] Read more.

This paper analyzes three forecasting methods for commodity spot prices and applies them to copper prices. The first method uses futures prices from either LME or COMEX. The second method uses analysts’ consensus expectations, reported by Bloomberg. The third method jointly uses futures and analysts’ expectations as inputs to a multifactor stochastic pricing model, with time-varying risk premiums that smooth its data using the Kalman filter. All three alternatives are compared with the well-known no-change forecast benchmark and with each other. The main finding is that analysts’ expectations are a valuable source of data for forecasting copper prices. Also, when futures prices are relatively higher than spot prices, the model presented is the best alternative for forecasting copper prices at any horizon up to 24 months, and when prices are relatively lower than spot prices, the model is the best alternative for long-term forecasts and for LME futures prices for 1 to 12 months. Full article

(This article belongs to the Special Issue Risk Management in Financial and Commodity Markets)

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20 pages, 1405 KB

Open AccessArticle

ESG Narrative Quality in Green Bond Disclosures: Implications for Risk Perception, Transparency, and Market Trust

by Parul Gaur, Mohammad Irfan, R Kanesaraj Ramasamy, Shakeeb Mohammad Mir and Parameswaran Subramanian

Risks 2026, 14(1), 1; https://doi.org/10.3390/risks14010001 - 22 Dec 2025

This research evaluates the extent to which firms’ “green” bond disclosures create and convey a meaningful representation of their Environmental, Social, and Governance (“ESG”) commitments. Additionally, this research explores how investors distinguish between disclosures that represent genuine commitment to sustainability and those that [...] Read more.

This research evaluates the extent to which firms’ “green” bond disclosures create and convey a meaningful representation of their Environmental, Social, and Governance (“ESG”) commitments. Additionally, this research explores how investors distinguish between disclosures that represent genuine commitment to sustainability and those that may be indicative of “greenwashing,” and how such distinctions impact their assessment of an issuer’s credibility as well as the issuer’s performance subsequent to the issuance of a “green” bond. The methodology employed in this research employs a convergent mixed-methods approach that combines quantitative methods (Natural Language Processing (“NLP”), financial modeling, etc.) with qualitative methodologies (case studies, interviews). The NLP methodology employed in this research includes sentiment analysis, topic modeling, and ambiguity measurement in order to determine the tone, thematic content, and linguistic clarity of the disclosure texts. Subsequently, the results of the NLP methodologies are correlated with firm level outcomes using cross validated partial least squares regression (“PLS-R”), event study methodologies, and one way ANOVA to test for temporal and industrial variability. Finally, the results of the computational and financial methodologies are supplemented by qualitative case studies and interviews to provide context for the patterns identified in the computational and financial methodologies. In summary, the results of this research demonstrate that firms that communicate in a clear, balanced, and verifiable manner experience better market reaction and more favorable accounting results subsequent to the issuance of a “green” bond than do firms whose communications are vague, overly optimistic, or lacking in consistency. Conversely, the findings suggest that investors have become increasingly sensitive to potential “greenwashing” and therefore are less likely to respond favorably to communications characterized by the aforementioned characteristics. Full article

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14 pages, 977 KB

Open AccessArticle

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

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

Open AccessArticle

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

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

T P_{2}

,

R P_{2}

, 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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