Risks doi: 10.3390/risks14050116

Authors: Dung Hai Dinh Thi Dang Minh Nguyen Huyen Le Thanh Nguyen Tobias Ametsbichler

The rapid development of the fintech sector has facilitated the emergence of digital multi-level marketing (MLM) schemes, raising concerns about investor protection. Despite extensive literature on MLM schemes and pyramid schemes, there remains a significant research gap regarding the psychological mechanisms and cognitive biases that drive investor participation behavior. This study investigates factors influencing Vietnamese female investors’ intention to participate in fintech MLM schemes, using Pi Network as a case study. Grounded in behavioral finance theories (Prospect Theory and Social Comparison Theory), the model empirically examines the impacts of herding bias and overconfidence bias, explaining participation intention through the mediating effect of the fear of missing out (FOMO) and perceived risk. A quantitative approach was employed using PLS-SEM analysis, with data collected from 264 female investors in Ho Chi Minh City. The results reveal that herding behavior and overconfidence significantly shape investors’ FOMO and perceived risk, with these biases significantly increasing FOMO and decreasing perceived risk. More importantly, these biases, mediated by FOMO and perceived risk, significantly shape participation intention in fintech MLM schemes. This study contributes empirical evidence showing the interaction between high social connectivity and cognitive-bias-driven vulnerabilities in a rapidly expanding and unregulated digital market such as Vietnam. This study has practical implications for policymakers and financial educators in protecting investors from financial schemes by monitoring social media to debunk “safety in numbers” narratives and prioritize the awareness of biases in financial education to mitigate impulse investments.

Risks doi: 10.3390/risks14050115

Authors: Constantin Colonescu Subhadip Ghosh Shahidul Islam

Crop insurance has to be priced and screened before the season’s main losses are known. This paper asks how far an insurer can get using only the information already available when a contract is enrolled: where the farm is, what crop and practice are insured, the chosen coverage level, and the local hail rate history. Using administrative records from 2006 to 2024, we build a practical underwriting score that separates the chance of a claim from the likely size of a loss, then test it on recent years and compare it with simpler rules and alternative models. The score ranks contracts better than regional averages, hail rate rules, or premium-based sorting, and the highest-ranked fifth of contracts contains about one third of the realized losses. Still, it misses much of the most severe loss risk. Coverage choice helps with prediction but also reflects farmer decisions, and thus the score should be read as a contract risk measure rather than a causal measure of hazard. These results suggest an enrollment-time information constraint for the specifications tested here, while leaving room for richer administrative and hazard-based extensions. Better tail prediction may require weather, farm history, and spatial information not used in the strict score.

Risks doi: 10.3390/risks14050114

Authors: Sourav Batabyal Alper Gormus

This study examines the relationship between climate policy uncertainty (CPU) and residential housing prices across U.S. metropolitan areas using the U.S. CPU index developed by Gavriilidis in 2021 and monthly S&P CoreLogic Case-Shiller Home Price Indices, covering January 1991 to May 2024. Employing a Fourier-augmented Toda–Yamamoto causality framework that accounts for both abrupt and gradual structural breaks, we document significant CPU → housing prices transmission in multiple metropolitan markets, with bidirectional transmission dynamics emerging in Los Angeles, New York, San Diego, and San Francisco, as well as at the U.S. national level. The results reveal substantial spatial heterogeneity across various market types. Coastal high-exposure markets exhibit strong CPU sensitivity, which may reflect the influence of physical climate risks and regulatory uncertainty; inland growth markets display housing prices → CPU feedback, likely operating through political economy channels; Midwest extreme-weather markets show persistent transmission despite their non-coastal locations; recession-sensitive markets become CPU-responsive following the Great Recession; and insulated markets show no significant transmission. The findings indicate that CPU operates as a priced systematic risk factor requiring integration into housing finance oversight, macroprudential frameworks, and investment strategies. These results have important implications for financial stability monitoring, mortgage credit risk assessment, and climate policy design as markets navigate transition risks in a low-carbon economy.

Risks doi: 10.3390/risks14050113

Authors: Wilasinee Jiaranaipayuk Amnuay Saengnoree Sujira Vuthisopon Kaimuk Pattananupong

This study examined the determinants of intentions to participate in digital asset investment among Thai mutual fund investors with medium-to-high risk tolerance by extending the Theory of Planned Behavior (TPB) to include financial literacy and trust. A quantitative survey was conducted with 360 respondents, and the proposed model was analyzed using structural equation modeling. The results indicate that attitudes toward behavior, subjective norms, and perceived behavioral control are positively associated with the intention to invest. Financial literacy and trust also demonstrate direct and indirect effects, with financial literacy emerging as the strongest overall predictor. In addition, perceived behavioral control and attitudes toward behavior serve as important mediating mechanisms linking cognitive and social factors to the intention to invest. The model explains 52% of the variance in investment intention, indicating moderate explanatory power. These findings suggest that intention to invest in digital assets among Thai mutual fund investors with medium-to-high risk tolerance is shaped by a combination of financial capability, social influence, perceived control, and evaluative judgment. However, these findings should be interpreted cautiously due to the purposive sampling approach and the fact that they are primarily applicable to financially experienced and relatively risk-tolerant investors rather than the broader Thai population.

Risks doi: 10.3390/risks14050112

Authors: Yelena Popova Olegs Cernisevs Evita Kalmane-Pivkina

Risk-based approaches constitute the cornerstone of contemporary anti-money laundering (AML) regulatory frameworks, requiring institutions to conduct Business Risk Assessments (BRAs). While the customer risk assessment component is well-defined across key regulatory dimensions, the “additional factors” capturing institution-specific risks remain unclear and underexplored, leaving a methodological gap in AML governance. This study examines this unaddressed issue by proposing and empirically validating a structured approach for identifying and evaluating institution-specific BRA factors. A PRISMA-guided literature review confirms the lack of standardized approaches for integrating additional factors into AML risk modelling. Empirical data from five European financial institutions were used to model 291 threats and 122 vulnerabilities via Partial Least Squares Structural Equation Modelling (PLS-SEM). The findings demonstrate that six dimensions, governance, operational, ICT, ESG, human resources, and regulatory compliance, significantly influence AML vulnerability, explaining 80.9% of variance (R2 = 0.809) with p < 0.001. Governance quality and ICT risk management emerge as the most critical drivers, underscoring the importance of institutional controls and technological resilience in mitigating AML risk.

Risks doi: 10.3390/risks14050111

Authors: Amin Hassan Hassan Zadeh Arman Rostami Kristina G. Stankova

This study investigates the impact of jointly modeling jumps in asset prices and mortality rates on the valuation of insurance guarantees. Mortality dynamics are specified using two extended frameworks based on the classical Lee–Carter model, with and without the inclusion of jump components. Financial asset returns are modeled using Merton jump–diffusion processes. In the proposed specification, asset prices evolve according to a two-regime Merton model, where the regimes correspond to pandemic and non-pandemic market conditions. Using historical mortality data for the U.S. population and financial market data from the S&P 500 index, we evaluate the pricing implications for a Guaranteed Minimum Death Benefit (GMDB) rider. Contract values and Greeks are computed across multiple issue ages and policy maturities. The empirical results highlight the importance of accounting for simultaneous mortality and market jumps and demonstrate that their interaction has a material effect on the valuation of GMDB products.

Risks doi: 10.3390/risks14050110

Authors: Weihua Zhao

Following the implementation of Basel IV, China’s financial regulators have replaced Value-at-Risk (VaR) with Expected Shortfall (ES) as the standard market risk measure, necessitating regulatory-oriented evaluation of ES forecasts. This study examines what constitutes a prudent ES forecasting method using e-backtesting, a sequential and model-free evaluation framework designed for regulatory monitoring. We evaluate 11 forecasting methods, including parametric, semiparametric, empirical, and deep-learning models, across four asset classes and four portfolio strategies in the Chinese market under Basel IV-consistent settings. Results show that parametric and semiparametric candidates exhibit clustered backtesting detections and increased computational burden around major market regime shifts, whereas the deep-learning model demonstrates greater resilience and produces more conservative ES forecasts during turbulent periods. These findings suggest that robustness to regime shifts should be considered a key criterion in the regulatory evaluation of ES forecasting models in the Chinese market.

Risks doi: 10.3390/risks14050108

Authors: Zoltán Köntös Ruszlan Megdetovics Rahimkulov

Standard asset pricing models treat price dynamics as a stochastic process driven by undifferentiated random noise, rendering them agnostic about the primary engine of price discovery: the arrival of economically significant information. This paper introduces ORAKULUM, a structured Information-Impact Asset Pricing Model that reconceptualises the log-price as a signed information ledger. Each market-relevant event appends a weighted entry that either permanently revises the market consensus or temporarily disturbs it before decaying exponentially toward the new equilibrium. Mathematically, ORAKULUM is a jump-diffusion process combining a Wiener component for continuous micro-uncertainty with a Poisson-driven jump component for discrete macroeconomic and geopolitical shocks. The log-price identity xt=x0+μ·t+∑Ai+∑Bi·e(−γt−ti)+σ·W(t) decomposes price dynamics into permanent and transient information impact, admits a natural event catalogue calibration, and supports Monte Carlo scenario simulation. We present the complete theoretical foundations, a closed-form expected path solution, a gradient-descent calibration procedure, and a fully documented Python3 reference implementation. An empirical illustration applies the model to XAU/USD and EUR/USD market data downloaded from Yahoo Finance, demonstrating ORAKULUM’s capacity to generate economically interpretable, real-time prediction clouds in response to central bank communications, inflation releases, and geopolitical shocks.

Risks doi: 10.3390/risks14050109

Authors: Ndonwabile Zimasa Mabandla Godfrey Marozva

This study investigates the effects of liquidity regulation, specifically the liquidity coverage ratio (LCR), on the capital structure of South African banks, with a focus on debt maturity composition. Using panel data covering the period 2015–2024, the analysis applies the Generalized Method of Moments (GMM) estimator to address potential endogeneity concerns. The findings reveal a significant positive relationship between LCR and banks’ total and long-term debt ratios, indicating a shift towards more stable funding structures. In contrast, the LCR is negatively associated with short-term debt. These results suggest that stricter liquidity requirements encourage banks to rely less on short-term funding and more on long-term debt instruments. Although the analysis is limited to a small sample of leading South African banks, the findings provide important insights into the structural implications of liquidity regulation. The study highlights the need for regulators to consider how liquidity requirements shape banks’ financing decisions within broader macroprudential frameworks. By promoting stable funding structures, liquidity regulations enhance banking sector resilience, protect depositors, and support sustainable credit provision. This study contributes novel evidence from an emerging market and addresses a gap in the post-crisis financial regulation literature by linking liquidity regulation to debt maturity profiles.

Risks doi: 10.3390/risks14050107

Authors: Reneta Duhova Angel Duhov Petia Georgieva Milena Lazarova

Organizations are currently in a stage where the volume of financial transactions and data is constantly growing. The same goes for risks associated with the use of data for risk management and strategic decision-making. The likelihood of transactional errors generally increases with data volume and process complexity, while fraud, although less frequent, may have more severe financial, compliance, and reputational consequences for organizations. Continuous auditing practices and well-established enterprise risk management (ERM) processes, combined with AI-driven pattern recognition, trend analysis and segmentation, can enhance timely detection and proper investigation of suspicious transactions. In areas with large volumes of transactions, the audit sampling process may be a lengthy process and pose a detection risk. Using machine learning (ML) models to support critical business processes could prove effective in managing enterprise risk overall. The current study offers new perspectives on managing risk and assurance with ML model output for flagging possible risky transactions within ERP (SAP) systems data. The study population consists of 69,158 finalized billing records extracted from the SAP production environment of a private sector organization, which covers a six-month operational period. The dataset was divided into an 80/20 train–test split, yielding 55,326 training and 13,832 test instances across six classification categories. The study examines the ML methods’ outcomes from billing datasets and their applicability in enhancing audit, assurance, and ERM processes by evaluating output data results from two supervised classification algorithms—multinomial logistic regression (SoftMax regression) and XGBoost—against various criteria generally accepted as risky in audit engagements. Model performance was assessed using accuracy, precision, recall, F1-score, ROC-AUC, and average precision (AP) from precision–recall curves. The results confirm that XGBoost achieves 99% overall accuracy with a macro F1-score of 0.965, outperforming logistic regression (macro F1 = 0.863), and that ML output allows early investigation and follow-up procedures to minimize the risk of fraud and errors and optimize risk management activities, thus strengthening internal control frameworks.

Risks doi: 10.3390/risks14050106

Authors: Szilárd Hegedűs Petronella Molnár

This study examines the financial resilience of Hungarian local governments in the period 2020–2023, which was characterised by successive economic shocks caused by the COVID-19 pandemic and the Russian–Ukrainian war. Based on a panel database covering 556 local governments, we constructed a composite resilience index normalised on a scale of 0–1 by integrating three dimensions: liquidity position, capital structure stability and operational efficiency. We used linear mixed models to examine temporal dynamics, while cluster analysis and non-parametric validation procedures were used to explore structural heterogeneity. The results show that there is no uniform, sector-level average shift in the composite resilience index during the period under review, but a significant part of the variance can be attributed to municipality-specific, persistent factors. The impact of the liquidity sub-index is significantly amplified in crisis years, especially in 2021, suggesting that liquidity plays a key role in managing financial shocks. Cluster analysis based on the period average of the sub-indices identified four markedly different resilience profiles, which are moderately but significantly related to the type of settlement. The research points out that municipal financial resilience is not a homogeneous and purely time-dependent phenomenon, but a multidimensional, structurally embedded adaptive capacity. From a policy perspective, the results support the need for differentiated, resilience-based fiscal interventions.

Risks doi: 10.3390/risks14050105

Authors: Linda Malifete Khathutshelo Mushavhanamadi Samuel Adekunle Clinton Aigbavboa

The insurance industry is under increasing pressure to undergo digital transformation as global markets, customer demands, and regulatory requirements evolve. Despite this growing importance, the sector continues to face persistent obstacles that hinder progress. This study investigates the factors influencing the successful implementation of digitalization in South Africa’s insurance industry, focusing on technological, organizational, and environmental factors derived from the Technology–Organization–Environment (TOE) framework and Dynamic Capabilities Theory. Key challenges identified include legacy systems, inadequate IT infrastructure, limited software capabilities, high maintenance costs, resistance to change, poor communication, insufficient employee readiness, and a lack of coherent digital strategies. Using the Delphi method, data were collected from 16 experts, comprising senior executives from leading South African insurance companies and academics specializing in business management and digital transformation. Through two iterative rounds, the experts evaluated and achieved consensus on the critical barriers affecting digitalization within the sector. The results form the basis for a conceptual framework that links the TOE dimensions with dynamic capabilities, providing actionable guidance for overcoming organizational and technological barriers. The findings highlight the urgent need for innovative strategies that emphasize organizational readiness, leadership, change management, and regulatory alignment. A conceptual framework that integrates the technology–organization–environment (TOE) framework and Dynamic Capabilities Theory is proposed, offering a structured response to overcome these barriers. By linking theoretical perspectives with practical insights, this study enriches the understanding of digital transformation in the insurance sector and provides actionable guidance for policymakers and practitioners seeking to enhance competitiveness, resilience, and customer-centric innovation.

Risks doi: 10.3390/risks14050104

Authors: Erdost Torun Erhan Demireli Simon Grima

The interaction between crude oil prices and exchange rates is central to understanding global financial stability and macro-economic balances. Contrary to traditional static analyses, the heterogeneous market hypothesis argues that market participants have different time horizons and that multi-scale analysis is necessary to capture dynamic changes in crisis periods. This study examines volatility spillovers between WTI crude oil and the Russian ruble using wavelet coherence, phase difference, and predictive information flow analysis in a time–frequency framework. The analysis separates short-term [2–32 days] transient shocks from long-term [32–256 days] structural changes. Findings show that a negative spillover, initially led by WTI, with evidence of dynamic, frequency-dependent leadership shifts during the 2020 shock, was interpreted as a result of the overnight price gap and a failure of microstructural synchronisation. With the outbreak of the 2022 Russia–Ukraine war, the relationship shifted to a strong, positive, and high-intensity risk transfer, consistent with contagion theory. Crucially, by 2024, a structural decoupling emerged due to geoeconomic fragmentation, signalling that the ruble no longer exhibits traditional petro-currency behaviour. These results offer critical signals for policymakers regarding reserve management and for market participants regarding new liquidity risks.

Risks doi: 10.3390/risks14050103

Authors: Ntebogang Dinah Moroke

This paper develops a deep reinforcement learning framework for cryptocurrency portfolio management in which transaction costs are derived from the Riemannian geometry of the underlying volatility model rather than assumed constant. A Proximal Policy Optimisation agent is trained on a reward function grounded in non-equilibrium thermodynamics: we use the free-energy Bellman equation, in which transaction costs are the geodesic slippage on the Fisher information manifold of a maximum-entropy Markov-switching GARCH model, and regime-transition costs are the Wasserstein-2 distance between the calm and turbulent return distributions. A thermodynamic Carnot bound on portfolio efficiency is established and empirically validated. Five hypotheses are tested across Bitcoin, Ethereum, Ripple, Litecoin, and Bitcoin Cash over January 2017 to March 2026. The geometric-cost agent achieves statistically superior Sharpe ratios relative to flat-fee baselines on four of five assets; portfolio turnover is reduced by 56 to 83 percent relative to signal-following; the thermodynamic friction point at which the agent prefers no-trade is asset-specific and ordered by turbulent half-life; a joint topological and geometric circuit breaker reduces Maximum Drawdown by 28 to 38 percent; and ablation confirms that every component of the observation vector contributes a statistically significant performance gain. The framework requires liquid cryptocurrency markets with validated parametric volatility models; transferability to other asset classes requires upstream recalibration.

Risks doi: 10.3390/risks14050102

Authors: Cem Yavrum A. Sevtap Selcuk-Kestel José Garrido

Climate change poses significant challenges to the agricultural and financial sectors, affecting crop productivity and the overall financial stability. This study evaluates the robustness of the Actuaries Climate IndexTM (ACI), a relatively recent tool to measure the impact of climate change, by comparing its explanatory power to well-established weather-based indexes (WBIs) across two key sectors. In the agricultural context, the yields of three major crops are predicted using generalized statistical models and advanced machine learning algorithms with climate indexes as explanatory variables. To enhance model reliability and address multicollinearity among weather-related variables, the study also incorporates both principal component analysis and functional principal component analysis. A total of 22 models, each constructed with different sets of explanatory variables, illustrate the significant impact of wind speed and sea-level changes, alongside temperature and precipitation, on crop yield variability across six regions of the United States. For the financial market application, the analysis adapts the weather-derivative framework, as it is a critical instrument for energy companies, insurers, and agribusinesses seeking to hedge against weather-related risks. By analyzing the payoffs of derivative contracts that use WBIs and ACI components as underlying variables, the findings reveal that the ACI framework holds a strong potential as a comprehensive climate risk indicator, not only for the agricultural sector but also for the finance and insurance industries.

Risks doi: 10.3390/risks14050101

Authors: Katarzyna Czech Michał Wielechowski

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Risks doi: 10.3390/risks14050100

Authors: Abdullah Hassan Farai Mlambo Wilson Tsakane Mongwe

We introduce the Normalising Flow GARCH (NF-GARCH), a two-stage hybrid framework that enhances traditional GARCH models by replacing restrictive parametric innovation distributions with learned densities via normalising flows. Our approach preserves the interpretability of standard variance dynamics while addressing the common issue of innovation misspecification. In the first stage, we estimate standard GARCH variants (sGARCH, TGARCH, and gjrGARCH) to extract standardised residuals. In the second stage, a Masked Autoregressive Flow learns the underlying residual distribution, with samples from the flow subsequently driving the GARCH recursion for out-of-sample forecasting. Evaluated on 13 daily financial series (six FX pairs and seven equities), NF-GARCH demonstrates systematic, statistically significant improvements in forecast accuracy for skewed-t baselines. Wilcoxon signed-rank tests confirm superior performance specifically for gjrGARCH-sstd and sGARCH-sstd specifications. While the framework offers enhanced flexibility and generative realism, we observe that computational overhead is increased, and the log-variance specification of eGARCH exhibits instability when paired with flow-based innovations. These results suggest that while NF-GARCH effectively captures empirical tail behaviour in univariate settings, future research should explore conditional flow architectures and multivariate extensions to account for time-varying innovation shapes. For risk management, gains are most relevant where skewed-t baselines are used and where closer residual realism supports scenario analysis; effect sizes remain modest relative to model risk and implementation cost.

Risks doi: 10.3390/risks14050099

Authors: Jeanne Kaspard Cesar Kamel Fleur Khalil Richard Beainy

The present study provides a high-frequency empirical assessment of the financial performance, volatility, and market integration of thematic sustainability-oriented equity funds, focusing on clean energy and environmental innovation indices. Specifically, the study compares the financial performance of representative thematic green equity funds, such as ICLN and QCLN, and an emerging-market benchmark (ECON) with conventional developed-market indices (SPY, QQQ, GSPC, and XLE) using daily stock prices from 2010 to 2025. The analysis employs a transparent and replicable framework based on daily logarithmic and cumulative returns and incorporates the compound annual growth rate (CAGR), Sharpe and Sortino ratios, beta estimation, correlation analysis, and maximum drawdown. The research frequency is appropriate for a thorough analysis of short-term market structures and performance. The results indicate that sustainability-oriented equity indices exhibit higher volatility, deeper drawdowns, and greater sensitivity to broad market movements than conventional benchmarks. Sustainability-focused equity indices that emphasize clean energy exhibit higher market sensitivity (betas above 1) and strong correlations with traditional equity indices. Correlation and beta estimates suggest a high degree of integration with traditional equity markets, implying limited diversification benefits within an equity-only framework. Periods of relative outperformance appear to be associated with favorable policy conditions and energy market dynamics, but are not consistently sustained over the sample period. In addition, the overall results suggest that sustainability investments generate substantial environmental and social externalities. Risk-adjusted performance measures suggest weaker historical performance over the sample period relative to conventional benchmarks. These findings should be interpreted as a comparative historical assessment rather than a structural risk model. From a policy perspective, the findings suggest that stable and credible regulatory frameworks, including long-term climate policy support and investment-enabling institutions, may be important for improving the financial resilience and long-term viability of green equity instruments. From a sustainability transition perspective, the observed volatility and market dependence of sustainability-oriented equity indices may constrain their effectiveness as standalone market-based financing mechanisms without complementary institutional and policy support.

Risks doi: 10.3390/risks14050098

Authors: Reham M. Essa Yasser El-Kassrawy Amer Alaya Nevien El-Kassrawy

In the context of behavioral finance, detecting credit card fraud remains a critical challenge, particularly when dealing with highly imbalanced datasets and ambiguous transaction patterns. This complexity highlights the limitations of traditional fraud detection models, which rely on a rigid binary distinction between “fraudulent” and “legitimate” transactions. Such a perspective restricts analysts’ ability to capture the nuanced and uncertain nature of fraudulent behavior, underscoring the need for a more flexible and practical approach. Accordingly, this study draws on Derrida’s deconstructive philosophy of binary oppositions to challenge the dominant dichotomy underlying conventional detection systems. This perspective provides a theoretical foundation for rethinking fraud detection by operationalizing deconstructive principles through the integration of fuzzy rules and machine learning architectures. The proposed approach is designed to address uncertainty, class imbalance, and semantic instability in financial transaction data. By combining fuzzy logic with deep learning, the framework deconstructs the rigid binary classification of transactions, enabling interpretation along a spectrum of legitimacy rather than as mutually exclusive categories. Deep learning techniques identify complex, nonlinear patterns that reveal overlaps between fraudulent and legitimate behaviors, while fuzzy membership functions model uncertainty and capture borderline cases that cannot be effectively handled by binary classification.

Risks doi: 10.3390/risks14050097

Authors: Sthembile Albertinah Fundama Thakhani Ravele Thinawanga Hangwani Tshisikhawe Caston Sigauke

Using daily data from Investing.com South Africa, this study investigates the forecasting performance of four Rand currency rate markets (EUR/ZAR, CHF/ZAR, BRL/ZAR, and CNY/ZAR) from 13 February 2018 until 24 February 2025. The predictive fitness of three competing models, Support Vector Regression (SVR), Principal Component Regression (PCR), and eXtreme Gradient Boosting (XGBoost), is explored between 80%/20% and 95%/5% training-testing splits. Forecasting accuracy is evaluated based on evaluation errors, i.e., Mean Absolute Error (MAE) and Root Mean Square Error (RMSE). The Diebold–Mariano test is employed to check for statistical significance. Empirical results show that the linear SVR model outperforms PCR across all markets, while XGBoost achieves competitive predictive accuracy on average; the trade-offs between SVR and XGBoost are often very small. The data indicate that linear kernel methods provide a robust prediction pipeline, especially when macroeconomic factors (gold, oil, platinum prices, and the USD/ZAR exchange rate) and calendar-based factors are taken into account, and offer a strong framework for predicting daily exchange rate fluctuations. The results of this research provide practitioners (traders, risk managers, and policymakers) with insights into the relative efficiency of the kernel vs. ensemble learning approaches for forecasting the value of emerging-market currencies in the presence of structural volatility.

Risks doi: 10.3390/risks14040096

Authors: Gioia Arnone

This paper examines how scam compounds, money mules and crypto-assets operate as interdependent elements of contemporary money-laundering chains. It assesses whether existing anti-money laundering (AML) and crypto-asset regulatory frameworks are capable of disrupting these chains holistically, rather than addressing individual components in isolation, with particular reference to scam-compound activity in Southeast Asia. The study adopts a qualitative comparative case-study methodology grounded in legal and regulatory analysis. Four empirically grounded cases are examined: two Southeast Asian scam-compound enforcement cases (Cambodia and Myanmar) and two European crypto-asset seizure cases (Ireland and Italy). Judicial decisions, enforcement actions and regulatory instruments are analysed through a chain-based analytical framework aligned with Financial Action Task Force (FATF) standards, the EU Markets in Crypto-Assets Regulation (MiCA) and the Anti-Money Laundering Authority (AMLA) framework. The analysis reveals a structural divergence in enforcement strategies: Southeast Asian responses increasingly prioritise network- and infrastructure-level disruption of scam compounds, whereas European approaches remain largely centred on post-offence crypto-asset seizure through traditional proceeds-of-crime mechanisms. Across all jurisdictions, money mules emerge as a critical yet systematically under-regulated intermediary layer enabling the resilience of crypto-laundering operations. The paper advances existing AML typologies by conceptualising scam compounds, money mules and crypto-assets as interconnected components of a single crypto-laundering chain. This chain-based perspective offers a novel analytical and regulatory lens for understanding organised crypto-enabled fraud. The study is based on a qualitative, case-based design and does not aim for statistical generalisation. However, the analytical framework developed is transferable to other jurisdictions experiencing similar scam-compound and crypto-laundering dynamics. The findings suggest that effective AML enforcement requires coordinated intervention across multiple nodes of the laundering chain, including scam compound infrastructure and money mule networks, alongside traditional asset-seizure mechanisms and CASP supervision. By highlighting the structural links between scam compounds, coercive labour and crypto-laundering mechanisms, the paper underscores the broader social harms of crypto-enabled fraud and the need for integrated regulatory responses that address both financial crime and human exploitation.

Risks doi: 10.3390/risks14040095

Authors: Tsolmon Sodnomdavaa Munkhtsetseg Sandagsuren

Machine learning-based credit risk models are commonly assessed using discrimination metrics alone. Such evaluation, however, does not fully capture economic consequences, temporal deployment conditions, or changes in the underlying risk environment. This study develops a decision-aligned, temporally consistent evaluation framework for real-world deployment. Using loan-level data, model performance is examined under a rolling forward validation scheme. A coverage-based alert policy is implemented to reflect operational resource constraints. Predictive discrimination is measured using PR-AUC, while economic performance is evaluated through a cost-sensitive saving function. The false-negative cost parameter (λ) is varied between 5 and 25 to assess sensitivity. Performance is also compared across high- and low-default regimes, and alternative alert budgets of 5%, 10%, and 20% are considered to examine policy stability. The results indicate no systematic decline in PR-AUC over time. Changes in λ do not alter predictive ranking, although economic returns scale proportionally with the cost parameter. Economic gains are higher in high-default regimes, yet no structural deterioration is observed in low-default periods. Increasing coverage improves recall but reduces economic benefit due to higher false-positive costs. To consolidate these stability dimensions, the Unified Policy Stability Index (UPSI) is proposed as a composite measure integrating predictive variability, economic volatility, and regime differences. The index indicates sustained overall stability during the study period. The findings suggest that credit risk model evaluation should extend beyond accuracy-centered metrics and incorporate decision consistency, temporal robustness, and policy stability within a deployment-oriented framework.

Risks doi: 10.3390/risks14040094

Authors: Vali Asimit Ziwei Chen Bogdan Ichim Pietro Millossovich

Multiple linear regression remains a foundational predictive methodology across a broad range of applications. We propose a novel regression framework that, rather than minimising the aggregate prediction error associated with the dependent variable, explicitly distributes the risk evenly across all model parameters. This approach provides a structural safeguard that is particularly suitable for data affected by substantial noise, as is often the case in time series environments characterised by regime shifts, structural breaks, and evolving trends. We provide a theoretical characterisation of our proposed estimator, named Parity Regression, and benchmark its analytical properties against existing penalised and shrinkage estimators in the literature. Both synthetic experiments and empirical applications demonstrate that the theoretical guarantees of the proposed method translate into enhanced out-of-sample forecasting stability in practice.

Risks doi: 10.3390/risks14040093

Authors: Mosab I. Tabash Suzan Sameer Issa Loona Mohammad Shaheen Mohammed Alnahhal Zokir Mamadiyarov

This research article explores whether the climate transition risk (CTR) and climate physical risk (CPR) transmit greater shocks towards the sustainable, gold-backed, energy-related and Sharia-compliant cryptocurrencies during bullish market conditions as compared with the normal and bearish market conditions. We employ the novel quantile vector auto-regression (QVAR)-based connectivity framework. Overall findings suggested that CPR and CTR transmitted greater shocks towards cryptocurrency classes during extremely high and lower quantiles as compared with the median quantile. This U-shaped and non-linear climate risks shock transmission indicates that Sharia-compliant, energy-related and gold-backed cryptocurrencies become more vulnerable during extreme market conditions (higher and lower quantiles) and may not consistently serve as reliable hedging or diversification instruments, particularly during periods of heightened climate uncertainty. Overall findings suggested that both the CPR and CTR transmitted greater shocks towards energy-related, gold-backed, and Sharia-compliant cryptocurrencies as compared with the sustainable cryptocurrencies, across all the quantiles. Therefore, sustainable cryptocurrencies, particularly those with energy-efficient consensus mechanisms such as Stellar, Cardano and Ripple, exhibited resilience to climate risks and can therefore function as stabilizing core holdings in diversified portfolios. Fund managers should incorporate a rebalancing strategy that increases allocation to these climate-resilient, sustainable digital assets during periods of elevated climate risk. Fund managers should integrate CPR and CTR into the quantile-domain forecasting frameworks for predicting digital asset market returns to enhance financial stability. Portfolio managers should undertake dynamic and quantile-contingent climate risk hedging strategies that account for tail-risk exposure rather than relying on average market behavior.

Risks doi: 10.3390/risks14040092

Authors: Seol-Hyun Noh

This study aims to predict stock market risk and improve preparedness for potential economic crises by identifying sharp declines in stock returns using classification-based machine learning models. Using ten years of KOSPI 200 index data (2015 to 2024), a daily return series was constructed. A day was labeled a risk event (1) if its return fell below the 5th percentile of the returns observed over the preceding 100 trading days, indicating a sharp decline. Nine classification models—Logistic Regression, k-nearest Neighbor, Decision Tree, Random Forest, Linear Discriminant Analysis, Naive Bayes, Quadratic Discriminant Analysis, AdaBoost, and Gradient Boosting—were trained and validated. Among these, Logistic Regression demonstrated the strongest overall performance across multiple evaluation metrics, including accuracy, non-risk F1 score, risk F1 score, and AUC.

Risks doi: 10.3390/risks14040091

Authors: Finn L. Solly Raquel Soriano-Gonzalez Angel A. Juan Antoni Guerrero

In insurance portfolios, classifying customers without a prior history at a given company is particularly challenging due to the absence of historical behavior, extreme class imbalance, heavy-tailed loss distributions, and strict operational constraints. Traditional machine learning approaches, including the baseline methodology proposed in previous studies, typically optimize global predictive accuracy and therefore fail to capture business-critical outcomes, especially the identification of high-risk clients. This study extends the existing approach by evaluating two complementary business-aware classification strategies: (i) a balanced bagging ensemble specifically designed to handle class imbalance and maximize expected profit under explicit customer-omission constraints, and (ii) a lightweight Transformer-based architecture capable of learning richer feature representations. Both approaches incorporate the asymmetric financial cost structure of insurance and operate under operational selection limits. The empirical analysis is conducted on a proprietary large-scale auto insurance dataset comprising 51,618 customers and is complemented by validation on nine synthetic datasets to assess robustness. Model performance is evaluated using statistical tests (ANOVA, Friedman, and pair-wise comparisons) together with business-oriented metrics. The results show that both proposed approaches consistently outperform the baseline methodology (p < 0.001) in terms of profit, with the ensemble offering a better balance of performance and efficiency, while the Transformer shows stronger robustness and generalization under data perturbations. The balanced ensemble provides the most favourable trade-off between predictive performance, robustness, interpretability, and computational efficiency, making it suitable for deployment in regulated insurance environments, while the Transformer achieves competitive results and exhibits stronger generalization under data perturbations. The proposed approach aligns machine learning with actuarial portfolio optimization by explicitly integrating profit-driven objectives and operational constraints, offering two practical and scalable solutions for risk-based decision-making in real-world insurance settings.

Risks doi: 10.3390/risks14040090

Authors: Yuto Kitamura Yuta Kudo Makoto Shimoshimizu Makoto Goto

This paper derives a closed-form expression for the expected discounted value of aggregate cash flows when arrival times follow a Poisson process but both the time horizon and the number of arrivals are finite. The result provides a tractable analytical formula for the expected discounted sum under simultaneous constraints on time and arrival counts. We show that the expression converges to the well-known infinite-horizon and infinite-arrival results as limiting cases. Numerical illustrations demonstrate the behavior of the formula under different parameter values. The result can be interpreted as the valuation of a discounted compound Poisson process with finite constraints and may be useful in stochastic modeling and risk-analysis applications. The proposed formula provides a simple analytical tool for evaluating discounted losses or revenues in finite risk portfolios.

Risks doi: 10.3390/risks14040089

Authors: Noura El Hassan Bacel Maddah Nassim Nicholas Taleb

We develop a practical framework for identifying and quantifying the hidden layers of risks and optionality embedded in American options by introducing stochasticity into one or more of their underlying determinants. The heuristic approach remedies the problems of conventional pricing systems, which treat some key inputs deterministically, hence systematically underestimate the flexibility and convexity inherent in early-exercise features.

Risks doi: 10.3390/risks14040088

Authors: Carlos Arenas-Laorga Fernando Gil Capella

This study analyzes the relationship between stock market returns and investment flows in investment funds in Spain. Through a quantitative analysis covering the period from December 2001 to June 2025, it examines not only the existence of a correlation but also its temporal structure, functional form, and heterogeneity across different geographical areas (U.S., Europe, Japan, and Spain). Using monthly data on net flows from INVERCO and market indices, the study employs Ordinary Least Squares (OLS) regression models, segmented regressions, and fixed-effects panel models to obtain robust estimates. The results confirm a positive and statistically significant relationship between past returns and subsequent investment flows, with a temporal lag ranging from one to three months. This delay varies notably by geographical region, suggesting the existence of different investor profiles and information channels. The study also finds evidence of a convex relationship, indicating that investors react asymmetrically, aggressively pursuing high returns more than penalizing low ones. These findings, interpreted through the lens of behavioral finance, point to pro-cyclical and reactive behavior of Spanish investors, driven by biases such as loss aversion, trend-following, and delays in information processing. The study contributes to the academic literature by providing updated and methodologically robust evidence on Spain, a market that has traditionally been underexplored, and offers practical implications for investors, fund managers, and regulators in terms of financial education and risk management.

Risks doi: 10.3390/risks14040087

Authors: Fang Sun Shuangjiang He Ruiqi Wang Lingyun Ke Hongyu Shen Qiuyue Liao

This study presents an exploratory methodological framework for examining structural changes in regulatory risk disclosure using sentence embeddings, multivariate anomaly detection, and explainable artificial intelligence. Prior research typically relies on dictionary-based word frequencies, tone indicators, or topic proportions to quantify risk disclosure. While these measures capture disclosure intensity, they do not directly assess whether the internal semantic organization of risk narratives has shifted relative to historical patterns. We propose a structural semantic deviation framework that represents each company–year disclosure using thematic shares and embedding-based dispersion statistics and evaluates deviations from a historical baseline through unsupervised anomaly detection. Using Item 1A Risk Factors from Wells Fargo and JPMorgan Chase surrounding the 2016 regulatory shock as a focused two-firm case study, we show that traditional lexical metrics do not clearly isolate structural breaks, whereas embedding-based semantic trajectories reveal substantial narrative reconfiguration. Isolation-based modeling provides stable and discriminative anomaly scores in this setting, and SHAP decomposition highlights semantic distance, litigation emphasis, and disclosure contraction as important drivers of deviation in 2025 out-of-sample disclosures. These findings should be interpreted as methodological evidence rather than broad population-level claims. The study demonstrates how structural semantic modeling can be operationalized in regulatory disclosure analysis and provides a transparent framework that can be extended to larger panels and cross-industry settings in future research.

Risks doi: 10.3390/risks14040086

Authors: Peter Hatzopoulos Anastasios D. Statiou

This paper introduces the Copula Asymmetry Index (CAI), a rolling, rank-based measure of asymmetric tail dependence between equity returns and implied-volatility proxies. CAI is defined as the difference between the empirical frequency of joint “equity-down & volatility-up” tail events and that of the mirror state (“equity-up & volatility-down”) within a rolling window. Building on this core asymmetry measure, we develop CAI++, an implementation framework that transforms CAI into an operational defensive allocation signal through smoothing, standardization, delayed execution, hysteresis, and cost-aware portfolio mapping. Using daily data from 2000 onward across a broad cross-section of 50 equity-volatility pairs, we evaluate the CAI++ strategy against buy-and-hold equity, a 60/40 benchmark, an inverse-volatility risk-parity portfolio, and a moving-average timing rule. Cross-sectional results indicate that CAI improves terminal outcomes relative to equity-only exposure for most pairs and shows particularly strong performance versus 60/40 in both final wealth and Sharpe. However, CAI does not dominate structurally diversified low-volatility allocations: risk parity retains a pronounced advantage in downside risk and risk-adjusted metrics. Overall, the findings support CAI as a tail-aware overlay for equity-centric and balanced portfolios rather than a substitute for institutional low-volatility baselines.

Risks doi: 10.3390/risks14040085

Authors: Neylan Kaya Aslıhan Ersoy Bozcuk Güler Ferhan Ünal Uyar Münevver Sena Özden Mustafa Terzioğlu Burçin Tutcu Hasan Talaş

The life insurance sector plays a critical role in the financial stability of countries due to its long-term liability structure and strong interaction with the financial system. The aim of this study is to evaluate the performance of the life insurance sector in the EU and EEA countries using a multi-criteria decision-making (MCDM) approach. Eight performance criteria reflecting financial stability, profitability, growth, and risk were used in the study. Criterion weights were determined using the Modified Preference Selection Index (MPSI) method, an objective method free from subjective judgments, and the performance ranking of the countries was obtained using the Combined Compromise Solution (CoCoSo) method. The data used in the analysis were obtained from the insurance statistics database published by the European Insurance and Occupational Pensions Authority (EIOPA). The findings show that ROE is the most important indicator, and that Cyprus, Hungary, and Iceland exhibit a significant positive difference in the life insurance sector compared to other countries. This study provides a unique contribution to the limited literature on comparative analyses at the country level by examining the performance of the life insurance sector in EU and EEA countries using an objective weighting and integrated ranking approach. The study results reveal important findings for a comparative assessment of life insurance markets from the perspective of regulatory bodies, policymakers, and industry stakeholders. Based on cross-sectional data for 2024, the findings should be interpreted as a framework providing a country-level risk-sensitive performance comparison under varying conditions.

Risks doi: 10.3390/risks14040084

Authors: Gourav Salotra Tharunya Katikireddy Yaswanth Anumolu Eugene Pinsky

This study examines overnight vs. daytime static and momentum strategies applied to ten sector Exchange-traded funds (ETFs) over a 27-year period from 1999 to 2025. Our findings reveal that several such strategies, particularly reversal strategies, consistently outperform static and buy-and-hold strategies. This outperformance decreases significantly when transaction costs are taken into account. We consider two transaction-cost scenarios (1 bps vs. 2 bps), which are industry standards for institutional and retail investors, respectively. We provided a detailed analysis of volatility and drawdowns. Our results indicate that by considering night and daytime separately, it is possible to outperform passive strategies for most sector ETFs.

Risks doi: 10.3390/risks14040083

Authors: Beatriz A. Curioso Manuel L. Esquível Gracinda R. Guerreiro Nadezhda P. Krasii Pedro A. C. Sousa

This paper presents a physics-based hazard model for catastrophe (CAT) modelling of urban flood risk—a first step toward a complete CAT modelling framework. We introduce a linear second-order ordinary differential equation (ODE) system to simulate the underlying mechanisms of water accumulation, absorption, routing, and drainage across interconnected surfaces in densely built urban areas. The model treats an urban zone as a multivariate network of surfaces, each with unique hydrological properties, linked by directed water flows. For risk analysis, the external meteorological forcing (representing the precipitation input) is randomised. Our risk-analysis protocol relies on a Monte Carlo simulation of stochastic forcing. Its reliability is founded on rigorous mathematical properties proven for the ODE system (existence, uniqueness, positivity, monotonicity, and a priori bounds), ensuring that the probabilistic outputs are well-defined and physically plausible. A three-surface example illustrates the framework and a complete risk analysis is performed, yielding concrete risk metrics that inform mitigation strategies. Computational efficiency is shown to be optimal for linear ODE systems, outperforming generic methods. This work provides a foundational, physics-informed hazard model for next-generation CAT models, directly supporting the insurance industry’s adaptation to climate change.

Risks doi: 10.3390/risks14040082

Authors: Yarong Lyu Mengjuan Li Yihang Liu Jingyi Zhou Jiliang Ma

Like other agricultural products, food legumes production faces uncertainty risks stemming from climate change, which may affect yields and consequently impact farmers’ livelihoods. Agricultural insurance serves as one of the climate change adaptation measures available to farmers, helping mitigate the impacts of climate change on agricultural production and livelihoods. While considerable attention has been paid to climate change adaptation through production-side measures, comparatively fewer micro level studies examine insurance adoption as an adaptive response, particularly among food legume farmers. Based on a survey of 460 food legume farmers in Baicheng City, Jilin Province of China, this study employs a binary probit regression model to analyze the relationship between climate change perceptions and farmers’ adoption of agricultural insurance as an adaptation measure. Farmers’ climate change perception is measured through four indicators: perceived changes in average annual temperature, precipitation, drought severity, and frost severity over the past five years. Robustness tests are conducted by using a replacement econometric model, altering the climate change perception variable, and implementing sample restriction. Results indicate that food legume farmers’ perceptions of climate change exhibits significant correlation with their agricultural insurance purchasing behavior. Farmers who perceive lower temperatures and more severe frosts are more inclined to purchase agricultural insurance. Participation in food legume production cooperatives and prior experience with yield reductions exert significant positive correlation with insurance purchase decisions. Therefore, enhancing targeted outreach and education, leveraging the role of cooperatives in insurance promotion, and implementing differentiated insurance promotion based on disaster experiences hold positive implications for reducing farmers’ exposure to climate change risks. The findings further offer valuable insights into climate adaptation policy in other drought-prone, legume-growing regions.

Risks doi: 10.3390/risks14040081

Authors: Xiaoque Chen Trairong Swatdikun Pankaewta Lakkanawanit Jin Zhao

This study examines whether IAS 38-recognized identifiable intangible assets (excluding goodwill) are associated with corporate leverage in Thailand, an emerging bank-dominated financial system, and whether that relationship changed after the COVID-19 shock. Panel on listed firms supports a stepwise design. Estimation begins with firm fixed-effects models, then proceeds to stricter specifications that add year fixed effects and, in the preferred model, industry-by-year fixed effects; dynamic robustness is evaluated using System GMM. In baseline firm fixed-effects specifications, recognized intangible intensity is positively associated with leverage, and the post-COVID-19 interaction is also significant under lighter controls. Statistical significance, however, fades after accounting for broader macro-financial and sector-specific financing conditions, and System GMM results similarly yield weak coefficients for recognized intangibles once leverage persistence is accounted for. The findings imply that apparent financing relevance for recognized intangibles depends strongly on the surrounding financing regime rather than on a robust independent debt-capacity effect.

Risks doi: 10.3390/risks14040080

Authors: Salem Al Mustanyir

This research examines market reactions to financial distress announcements by small privately held Canadian oil firms operating in the upstream sector between 2015 and 2021, employing an event study methodology, with daily spot prices for Brent and WTI crude oil serving as market benchmarks. The sample includes 11 firms that filed for insolvency, giving 99 observations for analysis. Data were collected from the publicly available Haynes Boone repository, ensuring transparency and verifiability. Abnormal returns were computed using market-adjusted returns to control for general market movements, isolating event-specific effects. The findings reveal statistically significant yet modest abnormal returns around the announcement day, indicating a measured market reaction. These results indicate that investors may partially anticipate such events and interpret them as potential restructuring opportunities rather than indicators of sector-wide collapse. The study underscores the importance of transparent disclosure and structured legal frameworks in moderating market volatility during financial distress. While the analysis is confined to short-term effects and small firms, it provides valuable insights into how financial distress in small upstream oil firms influences commodity markets, contributing new evidence to the literature on event studies and financial distress in energy markets, and offers implications for policymakers aiming to enhance market stability.

Risks doi: 10.3390/risks14040079

Authors: Rika Fitriani Hyukjun Gweon Shu Li

Inclusive insurance plays a critical role in reducing household vulnerability in developing countries such as Indonesia. This study investigates the factors influencing inclusive insurance participation across regencies in the Special Region of Yogyakarta Province using multinomial logistic regression and stereotype logistic regression. Insurance participation status is classified into three categories: uninsured, government-subsidized, and insured-without-support. Socioeconomic, demographic, and regional characteristics are examined. The results indicate that households with higher spending, higher education, and formal employment are less likely to be uninsured or to rely on government-subsidized insurance. Urban residence has varying effects across regencies. Furthermore, the results from the stereotype logistic regression model suggest that the uninsured group is conceptually closer to the government-subsidized group than to the insured-without-support group. These findings highlight the need for targeted, region-specific policies to expand coverage and facilitate transitions toward stable, non-subsidized insurance, thereby promoting inclusive insurance in Indonesia.

Risks doi: 10.3390/risks14040078

Authors: Olushola Christy Akilo Milan Christian De Wet

Optimal dividend policy remains a key topic of debate in corporate finance, particularly in emerging markets where investor preferences and macroeconomic volatility affect decision making. This study therefore examines the relationship between dividend policy and the Johannesburg Stock Exchange (JSE) market index over the period 2000 to 2020. The study uses firm-level dividend data to construct a market-capitalization-weighted aggregate dividend index. The paper further employs an Autoregressive Distributed Lag (ARDL) and error correction model to assess the long-run equilibrium relationship and short-run adjustments. The results show evidence of a long-run relationship between dividends and the JSE index price. In the short run, dividend payments exhibit negative effect on index prices while lagged dividends have a significant positive effect on index implying delayed market response. These findings suggest that South African investors place more confidence and emphasis on capital gains rather than dividend distributions. This study contributes evidence on the aggregate dividend dynamics within the context of an emerging market and offers practical insights for managers, investors and policy makers.

Risks doi: 10.3390/risks14040077

Authors: Budi Setiawan Sasiska Rani Emilda Emilda Firmansyah Arifin Dinarossi Utami

This study investigates the determinants of FinTech adoption and its role in supporting financial inclusion among micro, small, and medium enterprises (MSMEs) in South Sumatra, Indonesia. The analysis applies an extended Unified Theory of Acceptance and Use of Technology (UTAUT) framework that incorporates digital financial literacy, artificial intelligence literacy, green self-identity, and perceived green finance. Data from 632 MSMEs, comprising 377 rural and 255 urban enterprises, were analyzed using partial least squares structural equation modeling (PLS-SEM), multi-group analysis (MGA), and importance performance map analysis (IPMA). The results indicate that facilitating conditions represent the most influential determinant of FinTech adoption among rural MSMEs, while effort expectancy emerges as the dominant factor in urban enterprises. FinTech adoption also significantly strengthens both FinTech continuance intention and financial inclusion across the two groups, highlighting the role of digital financial technologies in promoting inclusive economic development. In addition, the IPMA shows that rural MSMEs place strong emphasis on facilitating conditions as the key driver of FinTech adoption, whereas urban MSMEs prioritize effort expectancy. By extending the UTAUT framework with sustainability-related constructs, this study provides new evidence on how digital financial innovation can support inclusive growth and contribute to Sustainable Development Goal 8.

Risks doi: 10.3390/risks14040076

Authors: Ahmad Farhan Alshira’h

This research examines Jordanian risk disclosure policies and how board size, meeting frequency, CEO duality, and board expertise affect them, exploring how political ties moderate the link between board features and risk disclosure. In 2014–2023, the research examined 90 non-financial enterprises registered on the Amman Stock Exchange, yielding 900 firm-year observations. Word-based manual content analysis quantifies risk disclosure. The postulated associations are tested using moderate regression. The board’s competence positively affects risk disclosure. CEO dual function hurts risk disclosure. However, the findings did not suggest that board size or meeting frequency affect risk disclosure. Political ties modify the board of directors’ relationship with business risk disclosure, according to the research. This research examines how board of directors’ characteristics affect risk disclosure processes in non-financial enterprises in Jordan, adding to the little knowledge. This research is one of the first empirical studies of political ties as a moderating factor in Jordan’s non-financial sector. The 2014–2023 study examines governance trends before and after the 2018 corporate governance rule reform. The findings improve understanding of board oversight systems and business risk disclosure.

Risks doi: 10.3390/risks14040075

Authors: Xinyan Deng Kentaka Aruga Chaofeng Tang

Conventional VAR-based volatility spillover measures rely on homoskedasticity and single-Gaussian assumptions, limiting their ability to capture structural breaks and heterogeneous shocks during crises. This study develops a flexible framework to analyze volatility transmission in U.S. commodity markets under multiple crisis regimes. We propose a Bayesian Structural Vector Autoregressive Mixture Normal (BSVAR-MIX) model that embeds finite normal mixtures within a mixture-based heteroskedastic structural VAR framework. The model combines generalized forecast error variance decomposition with posterior-probability weighting. Daily data for eight U.S. benchmark commodities across food, energy, and precious metals markets are examined over the 2008–2016 global financial crisis and the 2017–2025 multi-crisis period, including COVID-19 and the Russia–Ukraine conflict. The BSVAR-MIX framework provides a flexible descriptive setting for capturing multimodal shocks, heteroskedastic volatility states, and regime-dependent spillover patterns in commodity markets. Empirically, Gold and oil dominate systemic volatility transmission, soybeans amplify food–energy spillovers, while coal and wheat exhibit rising fragility under policy and geopolitical shocks. Assets commonly viewed as safe havens may contribute to systemic stress during extreme events. Overall, the framework offers a robust tool for structural shock identification and cross-commodity risk monitoring relevant to U.S. macroprudential policy.

Risks doi: 10.3390/risks14040074

Authors: Katarzyna Czech Michał Wielechowski

Exogenous shocks can affect equity markets by changing volatility and cross-market co-movement. This study examines how two U.S.-centred events, treated as different shock types, influence time-varying conditional correlations between the U.S. stock market and other G7 markets. The uncertainty shock is proxied by the U.S. presidential election of 5 November 2024, while the action shock is proxied by President Trump’s 2 April 2025 announcement of reciprocal tariffs. Using daily log returns for the S&P 500 and leading indices for Canada, France, Germany, Italy, Japan and the United Kingdom, we cover January 2010 to July 2025 and assess event effects using correlation paths for June 2024–June 2025 and symmetric ±30-day windows. We employ a DCC-GARCH model to jointly estimate conditional variances and dynamic correlations for six USA-G7 pairs. The results indicate persistent correlation dynamics, with Canada/USA the highest and Japan/USA the lowest. Election-related uncertainty is associated with declines in correlation for European pairs, suggesting temporary decoupling, while Canada and Japan show only small changes. By contrast, the tariff action shock significantly increases conditional correlations across all country/USA pairs, implying stronger market synchronisation, with the largest increases in North America and parts of Europe, and the smallest adjustment in Japan.

Risks doi: 10.3390/risks14040073

Authors: Samuel Montañez Jacquez Luis Alberto Quezada Téllez Rodrigo Morales Mendoza Ernesto Moya-Albor Guillermo Fernández Anaya Milagros Santos Moreno

Systemic risk in banking systems arises from losses transmitted through networks of contractual exposures. Yet, most widely used measures rely on market-implied volatility and equity prices rather than structural balance sheet fragilities. This paper develops a flow network framework that models systemic risk as a capacity-constrained loss-diffusion process governed by flow conservation, contractual seniority, and interbank topology. Using regulatory balance sheet data for four major U.S. banks across six quarters of the 2007–2008 financial crisis, we simulate millions of unit-consistent cascade scenarios to characterize the distribution of bank failures and aggregate losses. Despite severe macro-financial stress, the system remains in a subcritical contagion regime, exhibiting frequent single-bank failures, virtually no multi-bank cascades, and quasi-stationary aggregate losses concentrated around USD 420–430B.We extend the model to a stochastic setting in which the initial shock magnitude is randomized while propagation mechanics remain deterministic. The resulting loss distribution remains tightly concentrated and scales approximately linearly with shock size, suggesting that uncertainty in shock realizations does not induce nonlinear cascade amplification. Applying an efficient network benchmark, we estimate that 10–23% of expected systemic loss is attributable to suboptimal network architecture, implying potential gains from structural policy intervention. A comparison with SRISK reveals early divergence and convergence only at peak stress, highlighting the complementary roles of structural and market-based systemic risk measures. Finally, a graph neural network trained on synthetic flow network data fails to reproduce threshold-driven cascade dynamics, underscoring the importance of considering network structures vis-à-vis data-driven approaches.

Risks doi: 10.3390/risks14030072

Authors: Taehan Bae Jieun Kim Jae Youn Ahn

In the insurance literature, accurately predicting extreme losses has been a persistent and important problem. Recently, under the modelling framework of weighted distributions, several finite-mixture size-biased distributions, including size-biased Weibull and size-biased truncated log-normal distributions, have gained popularity for modelling heavy-tailed insurance claim data. In this study, unlike existing models, we explicitly account for the individual heterogeneity commonly observed in insurance claims by treating the order of size-biased weighting as a continuous latent variable, thereby constructing a mixed size-biased distribution. In particular, we study the various distributional properties of the mixed log-normal distribution with a truncated normal prior, which serves as a conjugate prior for the size-biased log-normal model. For applications in non-life insurance, we discuss the Bayesian credibility premium and present an estimation of a regression model via the EM algorithm. We further conduct a real-data analysis using insurance loss data, comparing goodness-of-fit and tail risk measures with those of standard heavy-tailed distributions.

Risks doi: 10.3390/risks14030071

Authors: John E. Grable Eun Jin Kwak

Research on financial risk tolerance and risk-taking increasingly incorporates personality traits into predictive and descriptive models of risk-taking behavior; however, intercorrelations among traits can obscure the unique contributions of individual traits. This is known as the suppressor effect. This study employed a two-stage analytic framework to test and adjust for suppressor effects across the Big Five personality dimensions in describing financial risk tolerance. In Stage 1, correlation and OLS regression analyses identified suppression patterns, revealing that the explanatory validity of some factors was distorted by shared variance. In Stage 2, suppression-adjusted trait estimates were used to reassess their unique association with financial risk-taking mediated through financial risk tolerance. Results indicate that Openness to Experience and Extraversion are the strongest descriptors of financial risk-taking once suppressor effects are controlled. At the same time, Agreeableness and Conscientiousness contribute modestly and context-dependently to descriptions of financial risk-taking. These findings demonstrate that ignoring suppression effects can lead to mischaracterizing the role of personality in financial decision-making. This study shows that more precise estimates of trait influences can improve theoretical models of investor behavior and enhance the delivery of financial advice and education.

Risks doi: 10.3390/risks14030070

Authors: Mosab I. Tabash Suzan Sameer Issa Mohammed Alnahhal Zokir Mamadiyarov Krzysztof Drachal

The objective of this study is to explore the time-varying shock transmission mechanism between aggregated financial stress indices (FSIs) of developed economies (the U.S., the U.K., the European Union (EU) and Japan) and the emerging economy of China. We employ a novel Time-Varying Parameter Vector Auto-Regression (TVP-VAR)-based “connectedness approach” to capture dynamic shock spillovers without the limitations of arbitrarily chosen rolling windows, loss of observations, or excessive sensitivity to outliers, as it is grounded in a multivariate Kalman filter structure. The aggregated measures of the FSIs of China, the U.S., the U.K., the EU and Japan are incorporated from the Asian Development Bank’s data repository by using time-series observations from January 2010 to September 2023. The findings indicate that the FSI of China is influenced by financial stress shocks originating from Japan (18.35%) and the U.S. (16.86%) the most, whereas the U.K. (EU) contributes to only 8.42% (6.54%) of FSI shocks in China. This research article significantly captures China’s heightened vulnerability to external financial stress shocks from developed economic systems and underscores the critical importance of reinforcing financial resilience, strengthening macro-prudential regulations and early-warning systems, and expanding financial buffers during episodes of trade uncertainty like restrictions on China’s rare earth exports and solar panels, U.S. restrictions on industrial metal imports, Brexit, supply chain disruptions amid COVID-19, and geopolitical uncertainties like the Russia–Ukraine war. Overall, this study provides actionable guidance for mitigating the impact of global financial stresses, improving risk management, and safeguarding economic stability in an increasingly interconnected and volatile international environment.

Risks doi: 10.3390/risks14030069

Authors: Oliver Takawira Emmanuel Amo-Bediako Dimakatso Sekwati Silas Marimo

In today’s world, climate change has become a global predicament. The implications for financial sector activities have given rise to ample literature on the climate change and banking system stability nexus in developing economies. However, there still remain important knowledge gaps pertaining to areas such as the asymmetric impact of climate change on banking system relationships, threshold effects, and transmission channels. Therefore, this research investigated the impact of climate change on banking system stability in the Southern Africa Development Communities (SADC). The study employed a panel data estimation technique, analysing fixed and random effects to test these hypotheses in SADC. In doing so, it not only explored how climate-related risks affect banking stability but also assessed how economic, environmental, and institutional dynamics mediate this relationship. The findings contribute to informing regional policy on financial resilience and adaptive climate strategies within fragile banking environments.

Risks doi: 10.3390/risks14030068

Authors: Asahi Shiiku Gideon Otchere-Appiah Mostafa Khan Yoshihiko Kadoya

This study investigates the relationship between digital financial literacy (DFL) and hyperbolic discounting among 104,993 active securities account holders in Japan. As digital financial services expand rapidly, individuals increasingly require not only traditional financial knowledge but also the capacity to understand digital platforms, evaluate online financial information, and manage emerging technological risks. Using data from the 2025 wave of the Survey on Life and Money, hyperbolic discounting is measured through intertemporal monetary choice scenarios, while DFL is constructed as a multidimensional index encompassing digital knowledge, financial knowledge, service awareness, attitudes, behaviors, practical capability, and protection against digital fraud. Probit regression results reveal a statistically significant negative association between DFL and hyperbolic discounting, indicating that individuals with stronger digital financial competencies are less likely to exhibit hyperbolic discounting. Attitudinal components of DFL exhibit the strongest effects, suggesting that internalized financial beliefs may play a more decisive role than technical knowledge in promoting time-consistent decision-making. Subsample analyses further highlight gender-differentiated patterns in demographic and economic influences on present bias. These findings contribute to behavioral finance by integrating digital capability into intertemporal choice research and provide policy-relevant implications for designing comprehensive financial education and digital literacy initiatives in increasingly digitalized financial environments.

Risks doi: 10.3390/risks14030066

Authors: Perpetual Andam Boiquaye Bernadette Aidoo Samuel Asante Gyamerah

This study investigates the probability of consumer default across both secured and unsecured assets, with a particular focus on borrower behavior and the role of moral hazard in shaping individual credit risk. It examines how different borrower decisions, such as investing in secured and unsecured projects after loan disbursement, affect default outcomes, especially under limited lender supervision. The Ornstein–Uhlenbeck process is used to capture the dynamics of risky asset returns and identifies the conditions under which borrowers are likely to switch from safer to riskier investments. We assume that borrowers may allocate loan funds to both secured and unsecured projects, thereby recognizing that credit risk assessment inherently involves behavioral factors that are difficult to quantify. Monte Carlo simulations are used to assess how return volatility influences borrower decision-making, showing that higher uncertainty increases the probability of returns exceeding the repayment obligation, thereby incentivizing risk-shifting behavior. The results indicate that unsecured lending is more exposed to strategic risk shifting and experiences more frequent and severe default outcomes than secured lending. As a result, this study recommends that microfinance institutions prioritize collateral-backed lending as a more effective strategy for mitigating credit risk and reducing exposure to borrower opportunism.

Risks doi: 10.3390/risks14030067

Authors: Jihwan Choi

This study examines the extent to which a firm’s business strategy shapes its strategic and audit risk profiles, and whether these risk characteristics ultimately manifest as measurable auditor–client disagreements. Auditor–client disagreement is operationalized using a direct, disclosure-based measure constructed as the scaled difference between unaudited preliminary net income—manually collected from mandatory timely filings disclosed through the Korea Financial Supervisory Service’s Electronic Disclosure System (DART)—and final audited net income reported in the audited financial statements. Using a sample of 6504 firm-year observations drawn from firms listed on the Korea Exchange (KOSPI and KOSDAQ) over the period 2020–2024, I find that a higher strategic score, reflecting a more innovation-oriented, prospector-type strategic posture, is consistently and significantly positively associated with the likelihood of auditor–client disagreement. Conversely, firms pursuing a cost-efficiency-oriented, defender-type strategy exhibit a significantly lower likelihood and smaller magnitude of disagreement. These findings suggest that business strategy functions as a fundamental, ex-ante determinant of inherent risk and audit risk, directly shaping auditors’ effort allocation and financial reporting outcomes. Collectively, this study contributes to the auditing literature by providing empirical evidence that a client’s strategic positioning constitutes a material, firm-level risk factor—consistent with the risk assessment framework mandated by International Standard on Auditing (ISA) 315—and should therefore be explicitly incorporated into auditors’ engagement risk assessments and the design of risk-based audit procedures.

Risks doi: 10.3390/risks14030065

Authors: Babatunde Lawrence Anurag Chaturvedi Adefemi A. Obalade Mishelle Doorasamy

This study examined the ‘too central to fail’ concept in the South African equity sector. We employed the Granger causality framework and PageRank algorithm to generate the centrality scores of the sectors on the Johannesburg Stock Exchange under extreme market conditions. Using the realized volatilities of sectoral returns for the full sample period (3 January 2006–31 December 2021), as well as during the global financial crisis (GFC), European debt crisis (EDC), COVID-19 pandemic, and US–China trade war sub-periods, we analyzed the sectors’ interconnections and calculated each sector’s centrality score across the entire sample and under different extreme market conditions. This allowed us to rank sectors relative to their centrality scores. The results indicate that, in the full sample, the insurance sector has the highest PageRank centrality score, suggesting it is too central to fail. This implies that the insurance sector acts as a systemic receiver of risks and provides stability within the network of sectors. However, the sub-period analyses reveal that General Industrial and Automobiles emerged as the key sectors with the highest PageRank centrality scores, and shocks from other sectors can disproportionately affect these industries during crisis periods. Underperformance in these sectors could have destabilizing effects on the South African economy. The findings have significant implications for regulators and policymakers, portfolio and fund managers, local and international investors, and researchers in the field of finance.

Risks doi: 10.3390/risks14030064

Authors: Matthew Jaramillo Donald Lacombe Leobardo Diosdado Laura Ricaldi

Household composition in the United States is increasingly diverse; however, research into the diversity of the financial decision maker’s sexual orientation has yet to be explored. This analysis examines whether there are differences in financial risk tolerance between same-sex and different-sex couples using data from the Survey of Consumer Finances. The results from a propensity score matching technique, a Mann–Whitney U test, and interpretations of average treatment effects and average treatment effects of the treated suggest there is a statistical difference in risk tolerance between couples and that, on average, same-sex households are significantly more likely to report higher risk tolerance scores, at the 10% alpha level, when compared to their counterparts. Both treatment effect estimates suggest a high impact of the treatment at the 1% alpha level. This highlights the importance of not assuming homogeneous risk preferences across household types. These findings emphasize the importance of recognizing diversity in household composition. Thus, this study identifies the need for inclusiveness in all segments of financial planning.

Risks doi: 10.3390/risks14030063

Authors: Binxu Lei

This study provides a structured review of statistical arbitrage research in the context of artificial intelligence, with a particular focus on machine learning based methods. The reviewed literature highlights the evolution from linear, rule-based strategies to increasingly complex data-driven models, while also documenting persistent challenges related to tail-risk exposure, regime instability, limited interpretability, and regulatory and governance constraints in practical applications. Building on this literature synthesis, the paper develops a conceptual AI-led, human-in-the-loop statistical arbitrage framework that integrates ML-generated signal modeling with structured human oversight—encompassing risk calibration, discretionary intervention, and interpretability review. This framework resonates with human-AI collaboration systems across other financial domains, collectively supporting the proposition that collaborative systems show potential to enhance resilience compared to purely AI-driven alternatives under specific market stress scenarios. It is positioned as a governance-oriented synthesis that qualitatively extends existing human-in-the-loop concepts by structurally embedding adaptive oversight within the statistical arbitrage decision architecture.

Risks doi: 10.3390/risks14030062

Authors: Shandra Widiyanti Hermanto Siregar Anny Ratnawati Suwandi Noer Azam Achsani

Indonesia’s financial system is bank-centric, with banks managing approximately 78% of the nation’s financial assets; therefore, the effectiveness of monetary policy transmission depends on banks’ responsiveness to the central bank’s interest rate policy (the BI Rate). However, a policy-relevant anomaly persists: deposit rate pricing is more strongly anchored to the Deposit Insurance benchmark (IDIC Rate) than to the BI Rate. This study argues that this research is significant because it identifies a “Dual Benchmark System” that traditional single-anchor models fail to address, representing a critical friction in emerging market transmission. This study examines this dual-benchmark paradigm and the associated asymmetric risks using a panel VAR with a Generalized Impulse Response Function (GIRF) on quarterly data for 63 commercial banks from 2010 to 2024. The results indicate that IDIC Rate shocks have a larger and more persistent effect on deposit rates than BI Rate shocks, generating asymmetric transmission risks. This dominance creates a structural “price ceiling” that keeps funding costs high, ultimately raising lending rates for borrowers and distorting deposit growth rates. Furthermore, this analysis reveals that external policy signals are far more influential than internal financial performance. This suggests that under the Basel III framework and prevailing financial regulations, banks prioritize liquidity compliance and safety net protection over internal operational efficiency. Macroeconomic shocks remain weaker than policy shocks and dissipate more quickly. This finding reveals a potential systemic coordination risk, implying an urgent need for tighter policy coordination between the Central Bank and the IDIC to reduce structural frictions, maintain transmission effectiveness, and protect long-term financial stability.

Risks doi: 10.3390/risks14030061

Authors: Godfrey Marozva

Utilising data from the selected companies listed on the Johannesburg Stock Exchange and using the Panel Autoregressive Distributed Lag (ARDL) specifically employing the Pooled Mean Group approach, this study examines the cointegrating and causal relationships among firm liquidity, performance and firm leverage. The results reveal a negative and significant long-run and short-run relationship between profitability and leverage. Conversely, higher leverage is found to diminish firm performance, consistent with trade-off theory implications regarding financial distress costs. On liquidity, results revealed a bidirectional long-run relationship among liquidity, leverage, and firm value as measured by Tobin’s Q. Also, liquidity plays a pivotal moderating role, where firms with stronger liquidity and profitability exhibit reduced reliance on external debt, highlighting the interplay between financial health and capital structure decisions. Additionally, a positive bidirectional relationship between Tobin’s Q and leverage suggests that growth opportunities and market valuation influence firms’ debt utilisation. The error correction terms confirm stable long-run equilibrium and moderate adjustment speeds. These results contribute to the understanding of optimal capital structure by integrating liquidity and performance factors and provide practical insights for corporate financial management and policy formulation.

Risks doi: 10.3390/risks14030060

Authors: Pierre Picard Jennifer Wang Kili C. Wang

This study analyzes how the insurance distribution channel can affect insurance fraud. It uses econometric models that confirm the existence of claim manipulation as a form of insurance fraud, whereby policyholders circumvent the bonus–malus system and reduce the actual burden of insurance deductibles. The econometric approach is based on joint regression models for the probability that a claim is manipulated on one hand, and the probability that the policyholder has strong incentives to do so, on the other hand. The estimation shows that there is a significantly positive residual correlation between these regressions, which establishes the likelihood of fraudulent claim manipulation. The econometric modelling of claim cost allows us to disentangle the manipulation of claims that correspond to true losses and small false claims filed at the end of the policy year, and also to highlight the role of the insurance distribution channel in these fraud mechanisms. Using data from two Taiwanese car insurers with very different distribution channels in 2010, we compare an insurer that relies heavily on dealer-owned agents (DOAs) with another insurer that does not rely on DOAs at all. We find strong evidence of severe claim manipulation when insurance is sold through DOAs. Moreover, as the first insurer significantly reduced its reliance on the DOA channel over time, we perform a before–after comparison using data from 2010 and 2018. The results show that the claim manipulation fraud previously observed in the DOA channel decreases as the market share of this distribution channel is reduced. All these results highlight the role of automobile insurance agencies in facilitating this fraud process. The theoretical underpinnings of our analysis are provided by a claim fraud model considering collusion and audit.

Risks doi: 10.3390/risks14030059

Authors: Cezar A. Câmpeanu Yechao Meng

This study extends existing mortality prediction frameworks by incorporating information borrowed from population–gender–age subgroups that exhibit similar mortality patterns. The borrowed information is integrated into classical mortality models to improve the accuracy of future mortality rate forecasts. To capture structural similarities among mortality trajectories, several distance measures are evaluated in combination with four linkage methods, particularly when each subgroup comprises multiple age-specific mortality trajectories. Extensive empirical analyses using data from the Human Mortality Database demonstrate the superior predictive performance of the proposed approach.

Risks doi: 10.3390/risks14030058

Authors: Marius Hofert

Considering achieving a goal in each of several time intervals when, in every time interval, an adverse event may lead to a failure raises the question of the return probability of adverse events, so the probability of at least one failure to happen during the time period of interest. Through basic mathematical arguments in tractable cases, we investigate the behavior of the return probability of adverse events in various setups. In the univariate case, we consider the independent and identically distributed setup, the independent setup, the dependent but not necessarily identically distributed setup, and the dependent and identically distributed setup. In the multivariate case, we consider several goals to be achieved in each time period. Besides different setups for the marginal failure probabilities, we study dependence in terms of comonotone blocks and independent blocks and via nested copulas. In case closed form expressions are not available, we derive bounds on the return probability of at least one failure. Our results are interpretable in terms of decision-making, provide insight into what affects such return probabilities and thus may help to develop strategies to lower them.

Risks doi: 10.3390/risks14030057

Authors: Pichit Boonkrong Junwei Yang Xueyuan Huang Teerawat Simmachan

This study develops an interpretable machine learning framework for type 1 motor insurance renewal classification using 70,290 real-world Thai policies, providing essential insights for pricing, customer retention, and operational decision making. The dataset was partitioned into a 70% training set, utilizing 5-fold cross-validation for hyperparameter tuning and model selection, and a 30% hold-out testing set to evaluate final model performance. Five machine learning models including Binary Logistic Regression, K-Nearest Neighbors, Support Vector Machines, Random Forests, and XGB are systematically evaluated across multiple curated feature sets generated through statistical filtering, stepwise selection, and permutation-based importance. Non-parametric tests are employed to compare model performance across scenarios. Experimental results show that a reduced four-feature Random Forest model (car age, net premium, sum insured, and car group) achieves the highest predictive performance (AUC = 99.62%; F1 = 98.15%), outperforming full-feature models while maintaining superior computational efficiency. Consequently, H2OAutoML serves as an external validation tool to verify that this manually curated, interpretable pipeline remains highly competitive with fully automated systems. Integrating a SHAP-based explainability layer quantifies predictor influence, ensuring transparency and regulatory alignment. Prioritizing feature parsimony, this study provides integrable insights for dynamic pricing and risk-adjusted retention, enhancing decision support within evolving motor insurance markets through transparent systems.

Risks doi: 10.3390/risks14030056

Authors: Kota Ogura Manaka Yamaguchi Sakiho Aizawa Mostafa Saidur Rahim Khan Yoshihiko Kadoya

Inflation expectations play a central role in monetary policy effectiveness, yet relatively little is known about how individual behavioral traits shape expectation formation. This study examines whether time discounting and hyperbolic discounting, key dimensions of intertemporal preferences, are systematically associated with household inflation expectations. Using large-scale survey data from Japan that elicit both time preference measures and qualitative inflation expectations, we analyze expectations over one-, three-, and five-year horizons. The empirical analysis employs ordered probit models that fit well with the categorical nature of survey-based inflation expectations and controls for a rich set of demographic, socioeconomic, and behavioral characteristics, including financial literacy and risk preferences. The results reveal clear horizon-dependent patterns. Hyperbolic discounting is positively associated with short-term inflation expectations, suggesting that present-biased individuals place greater weight on recent inflation developments. In contrast, higher time discount rates are associated with higher inflation expectations at medium and longer horizons, indicating that impatience is more relevant for beliefs about distant future prices. These findings provide novel evidence on the behavioral micro-foundations of inflation expectation formation and highlight the importance of heterogeneity in time preferences. From a policy perspective, the results suggest that one-size-fits-all communication strategies may be insufficient and that effective expectation management may require tailoring messages to account for differences in individuals’ time orientation across forecast horizons.

Risks doi: 10.3390/risks14030055

Authors: Fabian Moodley Babatunde Lawrence

Rising geopolitical tensions and fluctuating financial market conditions have increased volatility and negatively impacted property returns across BRICS countries, yet this critical area remains underexplored despite its significant implications for policy reform and investor participation. To this extent, the objective of the study is to examine the effect of geopolitical uncertainty on BRICS property market returns under changing market conditions. Using a Markov regime-switching model for the period February 2011 to June 2025, the findings reveal regime-specific effects. That being said, Brazil’s property market returns are affected positively (negatively) by South Africa’s (China’s) geopolitical uncertainty, whereas India’s and South Africa’s property market returns are affected negatively and positively by Russia’s geopolitical uncertainty, respectively. These findings were further evident in the bear market condition, as Russia’s geopolitical uncertainty has a significant negative effect on Brazil’s property market returns. Similarly, BRICS countries’ returns are dominated by bear market conditions, revealing negative returns, suggesting the BRICS property market returns are less resilient to periods of uncertainty. The findings underscore the need for new policy reforms to regulate BRICS members’ participation and minimize spillover effects, while investors should closely monitor geopolitical uncertainty within BRICS countries to manage return prospects effectively.

Risks doi: 10.3390/risks14030054

Authors: Sanae Rujivan Seyha Lim Nopporn Thamrongrat Angelo E. Marasigan

This paper develops a unified analytical framework for pricing discretely sampled volatility-average swaps under the 4/2 stochastic volatility model. The model accommodates a broad range of volatility dynamics by combining affine and inverse-affine components in the instantaneous volatility specification, thereby unifying and extending the structural features of the classical Heston and 3/2 stochastic volatility models. Closed-form expressions for the conditional complex moments of the asset price are derived and serve as the fundamental building blocks for obtaining explicit analytical pricing formulas for volatility-average swaps under discrete sampling. The validity of the proposed pricing formulas is rigorously established within the admissible parameter space of the model. Extensive numerical experiments verify the accuracy and computational efficiency of the analytical results when compared with Monte Carlo simulations. The numerical analysis further reveals that discretely sampled volatility swap prices converge to their continuous-time counterparts in a manner that may be monotonic or non-monotonic, depending on the interaction between the volatility and inverse-volatility components of the 4/2 model, thereby emphasizing the importance of sampling effects in volatility derivative valuation. A detailed sensitivity analysis demonstrates how variations in the parameters governing the volatility and inverse-volatility components influence the fair strike prices, underscoring the structural flexibility of the 4/2 stochastic volatility model. Overall, the proposed framework provides an analytically tractable and computationally efficient approach for pricing volatility-linked derivatives under discrete sampling, offering valuable insights for both theoretical research and practical applications in volatility markets.

Risks doi: 10.3390/risks14030053

Authors: Francis Agana Eben Maré

Modern financial systems do not exist in isolation but form part of a complex global network of interconnected financial systems. This globalization of financial systems significantly increases the risk of contagion in financial markets, impacting asset prices and other important economic factors, including interest rates and market volatility. This phenomenon informs not only investors’ investment strategies but also the prices of contingent claims. In this article, we present a derivative pricing model in an incomplete and globalized financial market. To appreciate the dynamics and impact of some important market factors, particularly default risks due to contagion, we consider two different financial markets with defaultable assets: in one market, we consider a stock whose price process follows a Heston stochastic volatility model, and in the other, a stock that follows a Hawkes-type jump diffusion model whose intensity is subjected to external systemic shocks. In both markets, we derive an indifference price for a contingent claim that is subject to the risk of default and show the impacts the investor’s risk aversion and external shocks on the price of the contingent claim.

Risks doi: 10.3390/risks14030052

Authors: Arnold Gideon Irangi Paul-Francois Muzindutsi Hilary Tinotenda Muguto Malibongwe Cyprian Nyati

Regional financial integration in East Africa remains shallow, yet contagion risks persist due to market fragility and illiquidity. Using daily data from 2014 to 2025 from the Nairobi Securities Exchange (NSE), Dar es Salaam Stock Exchange (DSE), Rwanda Stock Exchange (RSE), and Uganda Securities Exchange (USE), this study examines volatility spillovers, dynamic connectedness, and contagion through autoregressive moving average – generalised autoregressive conditional heteroscedasticity (ARMA–GARCH) diagnostics, asymmetric dynamic conditional correlation (ADCC–GARCH) correlations, and the Diebold–Yilmaz framework. The results show weak spillovers and limited connectedness in tranquil periods, reflecting persistent segmentation. However, systemic stress triggers abnormal surges in correlations and connectedness, consistent with contagion as a temporary amplification of cross-market linkages. The NSE emerges as the dominant transmitter, driven by liquidity and cross-listings, while the USE acts as a passive absorber. The RSE and DSE alternate between marginal transmitters and receivers depending on conditions. These findings support the Adaptive Market and Financial Instability Hypotheses, underscoring the need for harmonised regulation, liquidity reforms, and adaptive risk management to bolster resilience.

Risks doi: 10.3390/risks14030051

Authors: Yaser Sadati-Keneti Mohammad Vahid Sebt Reza Tavakkoli-Moghaddam Orod Ahmadi

The aim of this research is to employ improved machine learning techniques to determine the best Bitcoin trading positions in response to sudden price changes caused by global emergencies such as pandemics, conflicts, and economic disputes. Specifically, this study examines price fluctuations during the COVID pandemic as a case study to evaluate the performance of the algorithms investigated. We present a novel hybrid approach that merges Density-Based Spatial Clustering of Applications with Noise (DBSCAN) and Decision Tree (DT) classification to effectively eliminate noisy data and extract pertinent information for accurate position forecasting. The DBSCAN algorithm organizes the data to reveal important patterns, while the DT classifier sorts the trading signals. The performance of the proposed DBSCAN-DT model is rigorously compared with established alternatives, including the Multi-Layer Perceptron (MLP), Support Vector Classifier (SVC), and traditional Decision Trees. Findings from the experiments show that the DBSCAN-DT hybrid consistently outperforms these benchmarks during the outbreak, epidemic, and pandemic phases of COVID, attaining greater accuracy in forecasting both trading positions and market trends. These findings emphasize the essential importance of incorporating pandemic-related disruptions into cryptocurrency price prediction models and showcase the flexibility of our method in addressing sudden market changes.

Risks doi: 10.3390/risks14030050

Authors: Ibrahim Elsiddig Ahmed

This study aims to analyze the impact of environmental, social, and governance (ESG) disclosure quality on banking risk. Data were collected from the 100 largest commercial banks in the Middle East and Africa over ten years and examined using econometric analysis to measure the influence of ESG disclosure quality on banking risks. The findings indicate that both social and environmental disclosures have high predictability, while governance disclosure shows lower predictability. A significant negative relationship exists between the ESG disclosure quality and risk. Governance disclosure, Tier 1 capital, has a strong influence, and capital adequacy has the least. Managerial and practical implications are based on bank compliance, coverage, and debt. Unlike previous studies, this study moves from ESG performance to its disclosure quality and combines the random forest method (machine learning) with dynamic panel analysis (econometrics), bringing innovation and contribution to knowledge (the stakeholder theory) and practice.

Risks doi: 10.3390/risks14030049

Authors: Haim Levy

The mean-variance rule (M-V) conforms with the expected utility paradigm only in limited and economically unacceptable scenarios. Thus, the most widely employed portfolio-selection rule seemingly loses ground. We show with the commonly employed utility functions in economics, with a preference for a positive skewness, that choosing from the M-V efficient frontier conforms with expected utility maximization even with long investment horizon and skewed distributions of returns. The economic loss induced by choosing from the M-V frontier is negligible. Thus, the M-V rule is universal, or almost universal, provided that the commonly employed utility functions in economics are employed. This is an astonishing result that even Markowitz has not dreamed of.

Risks doi: 10.3390/risks14030048

Authors: Zhibin Tao

The increasing recognition of biodiversity loss as a critical environmental and financial risk has heightened calls for greater emphasis on corporate information disclosures. However, limited understanding remains as to how corporate biodiversity information disclosure affects institutional investors’ willingness to engage in corporate social responsibility (CSR) investments. To address this gap, this study utilizes a sample of 426 valid data points from institutional investors in China and employs SEM for empirical analysis. The results indicate that (1) corporate biodiversity information disclosure (CB) positively influences institutional investors’ CSR investment willingness; (2) CB fosters the development of institutional investors’ intergenerational responsibility, which, in turn, enhances their CSR investment willingness; (3) institutional investors’ intergenerational responsibility significantly mediates the relationship between CB and their CSR investment willingness; and (4) corporate environmental risk management positive moderates the relationship between CB and institutional investors’ intergenerational responsibility. Theoretically, this study contributes to the CSR literature by providing insights into the interconnections between biodiversity disclosure, intergenerational responsibility, and environmental risk management from a risk-oriented perspective. Practically, it underscores the importance of strategically utilizing biodiversity disclosure and environmental risk management to attract responsible institutional investments, offering valuable guidance for corporate managers, policymakers, and investors, particularly in emerging markets.

Risks doi: 10.3390/risks14030047

Authors: Alexandros Garefalakis Kounali Despoina Erasmia Angelaki Christos Papademetriou Ioannis Passas

ESG criteria have become central to corporate sustainability, reshaping governance, reporting, and the accounting profession. This study investigates how chartered accountants engage with ESG by combining micro-level survey evidence from Greece with macro-level bibliometric analysis of global ESG scholarship. The survey explored accountants’ knowledge, practices, and perceptions of ESG indicators, revealing significant generational differences: younger professionals reported higher familiarity and stronger implementation of ESG practices, while older respondents demonstrated more limited engagement. Training emerged as a decisive factor, with formally trained accountants applying a broader range of ESG criteria and perceiving greater strategic benefits in credibility, competitiveness, and adaptability. Complementing these insights, the bibliometric analysis of 861 articles published between 1993 and 2025 demonstrated exponential growth in ESG-related research, particularly after 2019, with sustainable development emerging as the conceptual anchor of the field. Thematic mapping highlighted climate change, decision-making, and corporate governance as central concerns, while collaborations between countries such as China, Italy, and the United States underscored global research dynamics. Overall, the study shows that accountants are increasingly positioned as gatekeepers of sustainability reporting, but their effectiveness depends on continuous training, regulatory alignment, and integration into global ESG frameworks.

Risks doi: 10.3390/risks14030046

Authors: Opale Guyot Heather A. Montgomery Peiqing Yang

Persistent deviations from Uncovered Interest Rate Parity (UIRP) represent a central puzzle in international finance and a key source of currency risk for global investors. This study examines the UIRP puzzle in the JPY/USD market through the lens of financial risk transmission, focusing on how risk premiums, liquidity conditions, and relative equity market performance jointly shape short-run exchange rate dynamics. Using daily data from 2018 to 2024, we employ a vector autoregression (VAR) framework to capture the endogenous interactions between change in the interest rate differentials, foreign exchange liquidity, global risk indicators (including the VIX, oil price shocks, and currency risk reversals), and relative equity returns consistent with the Uncovered Equity Parity (UEP) hypothesis. The results reveal that traditional interest rate differentials do not directly explain short-term exchange rate movements, confirming persistent UIRP deviations. Instead, risk-related financial channels act as indirect financial risk transmission channels. Shocks to global risk sentiment and currency risk premiums significantly affect JPY/USD returns, while relative equity market performance emerges as a key intermediary linking risk conditions to exchange rate adjustments. The findings also support the Japanese Yen’s continued role as a safe-haven currency during periods of heightened market uncertainty and underline the importance of carry trade dynamics in amplifying risk-driven exchange rate fluctuations. Overall, this study highlights the importance of integrating financial risk measures and portfolio-based transmission channels into exchange rate models. The results have direct implications for risk management, currency exposure hedging, and the assessment of systemic risk spillovers across financial markets.

Risks doi: 10.3390/risks14030045

Authors: Donald Ray Deis J. Kenneth Reynolds Christopher Wertheim Tian Xu Daqun Zhang

Corporate management and their auditors are required to evaluate whether there is a risk that the company’s ability to continue as a going concern is impaired. For fixed asset-intensive firms, however, regulatory inspections consistently identify problems with auditors’ testing of property, plant, and equipment (PPE), raising doubts about whether auditors understand the risks associated with these assets. This paper examines whether auditors incorporate the risks associated with PPE into their going concern evaluation and the accuracy of that evaluation. Using probit regression on financial and auditing data of U.S. public firms contained in S&P Global Compustat North America, Audit Analytics, and the Center for Research in Security Prices (CRSP) from 2000 to 2019, this paper examines the effects of PPE intensity, impairment, and age on the likelihood that an auditor issues a going concern modification. We test the accuracy of the auditor’s going concern evaluation by comparing it to the client’s subsequent viability or bankruptcy. Our results find that PPE intensity and PPE impairments are positively associated with the likelihood of an auditor issuing a going concern modification, indicating that auditors view PPE as contributing to substantial doubt about the entity’s ability to continue as a going concern. We do not find a significant association between PPE age and going concern modification. Additionally, the going concern evaluation is more accurate for firms with higher PPE intensity. These findings imply that auditors appropriately consider PPE assets in their going concern evaluations.

Risks doi: 10.3390/risks14030044

Authors: John Kerper Lee Bowron

The Kerper–Bowron Method (KB Method) is a patent-pending approach that revolutionizes service contract loss estimation and accounting by introducing a precise, contract-level approach to forecasting expected losses and cancellations. Building on a prior 2007 paper, this update presents the Earned Contract formula, aligning with Solvency II and modern accounting standards. By leveraging a probabilistic exposure base and Generalized Linear Models, the KB Method enhances accuracy in claims and cancel liabilities as well as other liability and asset estimates across global service contract markets. This methodology offers superior precision, automation, and compliance, redefining actuarial and financial practices for vehicle and other service contracts.

Risks doi: 10.3390/risks14030043

Authors: Vitor Fonseca Machado Beling Dias Rodrigo Fernandes Malaquias

The main purpose of this study was to analyze the dynamics of the conditional correlation between Bitcoin and BOVA11 (a Brazilian stock market ETF that has seen a significant increase in foreign investors) across the pre-, during, and post-COVID-19 pandemic periods. This analysis allowed us to investigate the Bitcoin characteristics as a diversifier, hedge, or safe haven relative to the ETF. The study employed a DCC-GARCH model using daily closing prices from 2 January 2015 to 26 September 2025. A robustness check was conducted using Large Language Models (LLMs). Results indicated that in the pre- and post-pandemic periods, Bitcoin showed no significant correlation with the ETF, potentially acting as a weak hedge. Conversely, during the pandemic, Bitcoin behaved as a diversifier for the ETF rather than a safe haven. This finding may surprise market participants, particularly given the widespread narrative of Bitcoin as “digital gold” and, therefore, a natural protection in scenarios of high uncertainty. The results suggest that, during the pandemic, Bitcoin’s behavior aligned more closely with risk assets than with safe havens, underscoring the need for cautious, context-specific empirical assessments of its protective properties.

Risks doi: 10.3390/risks14020042

Authors: Jude Martin B. Grozen Rogemar S. Mamon

Guaranteed annuity options (GAOs) allow policyholders to convert accumulated funds into life annuities at maturity at a guaranteed minimum rate. Thus, insurers are exposed to both investment and longevity risks. Accurate valuation of these long-term, survival-contingent contracts is essential for solvency assessment and risk management. Many existing approaches assume independence between interest rate and mortality risks. This paper develops a computationally efficient pricing framework for GAOs that jointly models interest and mortality rates as correlated stochastic processes with regime-switching dynamics governed by a finite-state continuous-time Markov chain. Model parameters are estimated using U.S. interest rates and cohort mortality data via quasi-maximum likelihood estimation. A semi-analytic valuation formula is derived based on the joint distribution of the underlying processes. Numerical results show that incorporating correlation and regime-switching materially increases GAO prices relative to conventional one-state models. The proposed semi-analytic approach delivers substantial computational advantages over standard Monte Carlo simulations. Sensitivity analysis further identifies the parameters most relevant for long-horizon pricing and solvency considerations. This highlights the practical relevance of the framework for managing longevity-linked guarantees under economic and demographic uncertainty.

Risks doi: 10.3390/risks14020041

Authors: Eleonora Salzmann

Despite the economic relevance of climate-transition risk, firm-level carbon exposure often fails to appear as a robustly priced factor when ESG measures and sustainability shocks are conflated. This study examines whether carbon exposure is conditionally priced in European equity returns using a strongly balanced quarterly panel of 238 firms from the MSCI Europe ESG Leaders universe (2018–2024). Total greenhouse gas emissions act as a proxy for carbon exposure, mapped to within-year percentiles and standardized by sector-year. Regressions control for ESG scores and controversies and include firm and quarter fixed effects with firm-clustered, dependence-robust standard errors. The linear carbon coefficient is small and statistically indistinguishable from zero, indicating no stable return premium from within-firm changes in carbon exposure. Functional-form tests reject linearity: quadratic and quintile specifications reveal curvature and a non-monotonic pattern, with return differences concentrated in the middle of the carbon distribution. Conditioning on macro-financial stress, measured by the ECB Composite Indicator of Systemic Stress, yields limited evidence of a uniform carbon penalty. However, high-controversy states are associated with lower returns, while ESG scores show negative associations under dependence-robust inference. Overall, carbon-related pricing appears to be nonlinear and state-dependent, whereas controversy risk is the most robust sustainability predictor of returns.

Risks doi: 10.3390/risks14020040

Authors: Gideon Otchere-Appiah Yu Kuramoto Aliyu Ali Bawalle Yoshihiko Kadoya

This study builds on the concept of loss tolerance by introducing investment grip, a behavioral interpretation that captures investors’ commitment to long-term objectives under adverse market conditions. While loss tolerance traditionally measures the maximum financial loss an investor can withstand, investment grip focuses on the behavioral and psychological dimensions of maintaining long-term investment objectives when facing short-term setbacks, thus providing a more behaviorally grounded and operationalizable approach for evaluating client risk profiles. The investment grip framework integrates insights from self-control theory, emotional regulation research, and goal-commitment models. Using data from 92,792 Japanese retail investors in the 2025 “Survey on Life and Money,” we examine how gain-framed and loss-framed messages are associated with investment grip, controlling for digital financial literacy and demographic, socioeconomic, and psychological factors. Our findings reveal that loss framing is robustly associated with stronger investment grip, whereas gain framing demonstrates no statistically meaningful effect. These findings offer new insights into Japanese household financial behavior, explaining why conservative savings patterns persist despite the availability of better investment alternatives. The results underscore the role of information framing in shaping household investment behavior, with implications for investor protection and financial communication policy.

Risks doi: 10.3390/risks14020039

Authors: Bassam Al-Own Mohannad Obeid Al Shbail Zaid Jaradat Ghaith N. Al-Eitan

Using a panel dataset of 385 European bank-year observations covering the 2012 to 2022 period, this study aimed to investigate the impact of derivatives on bank value and performance. We used bank-level panel data and conducted several multivariate statistical analyses, i.e., ordinary least squares (OLS), random-effects, and feasible generalized least squares (FGLS) regressions, to examine the ways in which using derivatives for different purposes influences bank value and performance. The regression results indicated a positive and significant association between hedging derivatives and bank performance, while trading derivatives had a negative effect on bank performance and value. Furthermore, the findings suggest that using such derivatives for hedging does not enhance value. Regarding the practical implications of this study and banking sector soundness, financial market regulators and policymakers should be cautious of the potential negative consequences of extensive trading derivative use. In particular, maintaining an acceptable level in this regard is essential to ensuring that the costs of engaging in derivative markets do not surpass their benefits. Hedging through derivatives may not translate into higher bank value, thus managers should justify to investors how such hedging derivatives enhance shareholder wealth. Additional research could focus on whether using derivatives in the banking industry offers any palpable advantage in the intermediate to long term; whether their use by non-financial organizations has different implications that than of financial firms; and the extent to which such financial instruments are useful for enhancing bank value.

Risks doi: 10.3390/risks14020038

Authors: Sello Dalton Pitso Taryn Michael

Banks often assume that higher credit limits increase customer default risk because greater exposure appears to imply greater vulnerability. This reasoning, however, conflates correlation with causation. Whether increasing a customer’s credit limit truly raises the likelihood of default remains an open empirical question that this work seeks to answer. We applied Bayesian causal inference to estimate the causal effect of credit limits on default probability. The analysis incorporated Directed Acyclic Graphs (DAGs) for causal structure, d-separation for identification, and Bayesian logistic regression using a dataset of 30,000 credit card holders in Taiwan (April–September 2005). Twenty-two confounding variables were adjusted for, covering demographics, repayment history, and billing and payment behavior. Continuous covariates were standardized, and posterior inference was performed using NUTS sampling with posterior predictive simulations to compute Average Treatment Effects (ATEs). We found that a one-standard-deviation increase in credit limit reduces default probability by 1.44 percentage points (94% HDI: [−2.0%, −1.0%]), corresponding to a 6.3% relative decline from the baseline default rate of 22.1%. The effect was consistent across demographic subgroups, with homogeneous treatment effects observed for age, education, and gender categories, and remained robust under sensitivity analysis addressing potential unmeasured confounding. The findings suggest that increasing credit limits can causally reduce default risk, likely by enhancing financial flexibility and lowering utilization ratios. These results have practical implications for credit policy design and motivate further investigation into mechanisms and applicability across broader lending environments. These estimates are explicitly interpreted as context-specific causal effects for a pre-crisis consumer credit environment, with external validity assessed conceptually rather than assumed.

Risks doi: 10.3390/risks14020037

Authors: Shih-Ying Chen Kuen-Lin Lin Ming-Tang Tsai

In deregulated electricity markets, Generation Companies (GENCOs) are exposed to substantial financial risk due to volatile and uncertain electricity prices. Traditional generation asset valuation approaches, which rely primarily on expected profit, fail to adequately capture downside risk under market uncertainty. This study proposes an integrated risk-aware framework for generation asset valuation by embedding Value-at-Risk (VaR) into a Price-Based Unit Commitment (PBUC) model. VaR is employed to quantify potential profit losses at different confidence levels, enabling GENCOs to explicitly assess downside exposure associated with electricity price fluctuations. Spot price uncertainty is modeled using the Delta-Normal approach based on historical PJM market data. The resulting nonlinear mixed-integer optimization problem is solved using an Improved Immune Algorithm (IIA) enhanced with the Taguchi Method to improve convergence stability and solution diversity. Case studies on the IEEE 15-unit system demonstrate that the proposed IIA consistently outperforms conventional evolutionary algorithms in terms of profitability, robustness, and convergence reliability. The VaR analysis further reveals pronounced left-tail risk in profit distributions, particularly during peak-load periods, highlighting the importance of risk-adjusted commitment strategies. The proposed framework provides a practical decision-support tool for GENCOs to balance profitability and downside risk in competitive electricity markets.

Risks doi: 10.3390/risks14020036

Authors: Zhibin Tao

As global climate and sustainable challenges gain more attention, green finance has emerged as a significant focus of worldwide financial reform, with green bonds serving as a key indicator. Retail investors, as an important part of the financial market, have a significant impact on the development of green finance through their investment willingness. This study aims to explore the influencing factors and mechanisms on the investment willingness of China retail investors towards green bonds. Based on empirical analysis of data from 2219 valid respondents in China, carried out using the SEM method, the results suggest that perceived usefulness (PU), investment literacy (IL), and information transparency (IT) all positively influence retail investors’ willingness to invest in green bonds. Additionally, PU, IL, and IT contribute to fostering an open attitude toward change (OATC) among retail investors, which, in turn, significantly promotes their investment willingness. This study also identifies the mediation effect of OATC. The findings provide both theoretical and practical insights to promote the development of green finance, enhance market activity, and support policy frameworks.

Risks doi: 10.3390/risks14020035

Authors: Güler Ferhan Ünal Uyar Mustafa Terzioğlu Neylan Kaya Aslıhan Ersoy Bozcuk

This study models the auditor–client relationship as a strategic game shaped by two-sided information asymmetry and examines how this structure influences key audit outcomes, namely audit delay and audit fees, in Türkiye. Using a game-theoretic framework complemented by empirical analysis, the study analyzes independent audit reports dated 31 December 2024 for 201 Borsa Istanbul firms audited by Big Four auditors. Two ordinary least squares models are estimated: one for audit delay and one for the logarithm of audit fees. The findings indicate that firm size and effort-related cost proxies play a central role in explaining audit fees, reflecting scale-related audit complexity. Financial risk, while not significantly associated with audit fees, is found to be negatively related to audit delay, suggesting that riskier firms may accelerate the reporting process through stronger monitoring, earlier planning, or tighter regulatory scrutiny. Audit opinion, by contrast, does not exhibit a statistically meaningful association with reporting delay, likely due to limited variation within the sample. Overall, the results partially support the risk–effort–cost mechanism proposed by the game-theoretic framework and highlight how institutional features of the Turkish audit market shape the relationship between risk and reporting timeliness. The study contributes to the literature by framing the audit process as a strategic decision environment and by providing updated evidence from an emerging market context.

Risks doi: 10.3390/risks14020034

Authors: Natalia Bou Sakr Stéphane Loisel Gihane Mansour Yahia Salhi

Lebanon does not have a national mortality table that reflects its demographic and health conditions. Despite ongoing changes in mortality patterns driven by economic crises, political instability, and social changes, outdated foreign tables such as AM80 remain in use in the insurance and public sectors. This dependency introduces significant risks in actuarial calculations, policy design, and long-term planning. This study addresses this gap by building a mortality table specifically adapted to the Lebanese insurance context, together with a first estimation of population-level mortality. In the absence of any official mortality database, we collaborated directly with local insurance companies to access and organize internal records of insured lives. These data, which represent one of the few available structured sources of mortality information in the country, form the core of our analysis. We apply actuarial methods to estimate age-specific death rates and life expectancy and benchmark the results against national and international references to assess consistency and range. By offering a locally grounded, data-driven alternative to imported mortality assumptions, this work fills a critical statistical need. The resulting table supports more accurate forecasting, pricing, and demographic modeling, with applications across insurance, pensions, and public health planning in Lebanon.

Risks doi: 10.3390/risks14020033

Authors: Farzaneh Nassirzadeh Davood Askarany Fatemeh Keyvani

This study investigates whether Corporate Social Responsibility Disclosure (CSRD) serves as a risk-mitigating or cost-inducing signal for firms’ market value in an emerging market. Utilising a panel dataset of 120 companies listed on the Tehran Stock Exchange (2015–2023) and employing content analysis alongside panel regression and System GMM models, we find that disclosure quality in social, employee, and environmental dimensions is positively associated with market value, while customer-related disclosure is not. The role of family ownership is nuanced: baseline specifications suggest no broad moderating influence, yet robust dynamic modelling reveals that family ownership significantly enhances the positive market valuation of environmental disclosure. The primary contribution is a nuanced, dimension-specific analysis of CSRD’s value relevance, challenging blanket assumptions about family firm behaviour and offering granular, methodologically informed insights for stakeholders in institutionally complex environments.

Risks doi: 10.3390/risks14020032

Authors: Changjie Hu Zhu Chen Ting Cao

This study investigates credit unions’ expansion into non-core lending and its association with risk and financial resilience. Using US credit union call report data from 1994 to 2024, we measure the share of purchased loans, lease receivables, and loans held for sale in non-core lending. We document robust conditional, within-credit-union associations that point to a clear risk trade-off. Credit unions with higher non-core exposure grow faster in terms of loans and membership but exhibit weaker financial buffers, including lower net worth ratios and weaker economic solvency, alongside higher delinquency. Decomposition tests indicate that loans held for sale are most strongly associated with adverse buffer and asset quality patterns, while purchased loans and lease receivables display smaller and less uniform relationships. Scale interactions suggest that these associations are generally weaker for larger institutions for both membership and assets. Post-COVID estimates indicate that the baseline relationships are broadly stable, while the growth link is becoming stronger.

Risks doi: 10.3390/risks14020031

Authors: Levi Mbaka Matimbia Abraham Deka Huseyin Ozdeser Sindiso Deka

Ecological sustainability is one of the key dimensions of sustainable development in any economy. Developing economies exhibit high-risk levels in terms of political stability and corruption, thereby inhibiting them from successfully adopting techniques for ecological sustainability. A framework that comprises a strong financial system for green financial investment, coupled with correct policy frameworks becomes fundamental in the attainment of sustainable environments. Pervasive corruption in developing nations is a formidable barrier that impedes financial development and undermines green finance initiatives’ efficacy in fostering ecological sustainability. This research takes the data of the Central African nations, which is analyzed with the ‘Methods of Moments Quantile Regression’ technique. The major results presented show that digitalization, renewable energy, and governance support ecological sustainability. Institutional quality and green finance are expected to increase ecological sustainability, but the findings show that in the Central African countries with high corruption they tend to reduce ecological sustainability. The poor institutional quality in the Central African nations, because of high corruption and political instabilities, impedes the efficacy of financial development and green finance in advancing ecological sustainability. The Central African nations can achieve sustainability by fostering digitalization and renewable energy, as well as reducing corruption and political instabilities.

Risks doi: 10.3390/risks14020030

Authors: Sana Gaied Chortane Kamel Naoui

Cryptocurrencies play a central role in modern financial markets; however, geopolitical tensions and environmental concerns raise critical questions about their stability and informational efficiency. This study distinguishes between green cryptocurrencies (GCs), based on low-energy validation mechanisms, and dirty cryptocurrencies (DCs), which rely on energy-intensive protocols, to examine their behaviour under geopolitical stress. The objective of this paper is to assess how information dynamics, market resilience, and efficiency differ between GCs and DCs during periods of heightened geopolitical uncertainty, with particular focus on the Russia–Ukraine war. Using daily data from 28 April 2019 to 5 October 2023, we employ advanced information-theoretic measures, including mutual information, the rolling local nearest-neighbour entropy estimator (RLNNEE), and approximate entropy. The results show that DCs exhibit stronger information dominance than GCs, with this gap widening during the conflict. In contrast, GCs display lower but more stable mutual information, indicating greater informational resilience. Approximate entropy further reveals a decline in market complexity during the war period. Overall, the findings highlight the relevance of entropy-based tools for evaluating stability and risk in cryptocurrency markets facing geopolitical shocks.

Risks doi: 10.3390/risks14020029

Authors: Jaungura Kaune Andy Esterhuizen Valdemar J. Undji

Climate change has emerged as one of the defining risks in recent years. These risks are associated with economic losses and, ultimately, the stability of the financial system. This study examines the impact of climate change on financial stability in Namibia using quarterly data spanning from the period 2009 to 2023. The Nonlinear Autoregressive Distributed Lag (NARDL) approach is employed to assess how climate change asymmetrically affects the stability of Namibia’s financial system. The findings reveal that both increases and decreases in rainfall, as well as higher temperatures, exert negative long-term asymmetric effects on financial stability, while rises in CO2 emissions appear to enhance it. Accordingly, this study recommends the integration of climate-related risks into financial institutions’ risk assessment frameworks, together with the adoption of long-term monitoring and mitigation strategies. Finally, regulators are also encouraged to conduct climate stress tests to assess the resilience of the financial system under varying climate scenarios.

Risks doi: 10.3390/risks14020027

Authors: Irina Georgescu

This paper investigates tail-risk transmission and asymmetric dependence in commodity markets using an asymmetric fuzzy vine copula framework applied to gold, crude oil, natural gas, and silver from 1 January 2015 to 1 January 2025, extracted from Yahoo Finance. Bootstrap-based trapezoidal fuzzy numbers are used to estimate fuzzy tail dependence, VaR, and CoVaR, capturing both sampling variability and parameter uncertainty. Results show generally weak and symmetric dependence among commodities, except for strong lower-tail dominance between crude oil and natural gas, indicating downside contagion within the energy sector. Adding the SKEW index as a market-implied tail-risk proxy has negligible effects on dependence and spillovers, revealing that equity-market tail-risk sentiment does not influence commodity markets. Systemic risk remains localized within energy and precious-metal linkages, underscoring the need for sector-specific monitoring.

Risks doi: 10.3390/risks14020028

Authors: Nam Thinh Vong Thinh Tien Bui

This study investigates the impacts of geopolitical risks on corporate leverage decisions of Vietnamese listed firms from 2017 to 2024. The research findings reveal a negative impact of geopolitical risks on both corporate leverage and short-term leverage. That is, Vietnamese listed firms actively reduce corporate leverage and short-term leverage as firms face rising geopolitical risks and uncertainties. Additionally, the effects of geopolitical risks are more pronounced for financially unconstrained firms, HOSE- and HNX-listed firms. Based on the main findings, policymakers at government levels and managers at corporate levels should consider the impacts of geopolitical risks when designing and implementing new policies in order to mitigate the negative effects of these risks and increase the resilience of Vietnamese firms considering geopolitical risks and uncertainties.

Risks doi: 10.3390/risks14020026

Authors: Zhiyong (John) Liu Jin Park Mengying Wang He Wen

Businesses increasingly rely on algorithmic systems and machine learning models to make operational decisions about customers, employees, and counterparties. These “algorithmic operations” can improve efficiency but also concentrate liability in a small number of technically complex, drifting models. Algorithmic operations liability (AOL) risk arises when these systems generate legally cognizable harm. We develop a simple taxonomy of AOL risk sources: model error and bias, data quality failures, distribution shift and concept drift, miscalibration, machine learning operations (MLOps) and integration failures, governance gaps, and ecosystem-level externalities. Building on this taxonomy, we outline a simple analysis of AOL risk pricing using some basic actuarial building blocks: (i) a confusion-matrix-based expected-loss model for false positives and false negatives; (ii) drift-adjusted error rates and stress scenarios; and (iii) credibility-weighted rates when insureds have limited experience data. We then introduce capital and loss surcharges that incorporate distributional uncertainty and tail risk. Finally, we link the framework to AOL risk controls by identifying governance, documentation, model-monitoring, and MLOps practices that both reduce loss frequency and severity and serve as underwriting prerequisites.

Risks doi: 10.3390/risks14020025

Authors: Ardak Akhmetova Assem Shayakhmetova Nurken Abdurakhmanov

The rapid expansion of small and medium enterprise (SME) lending has intensified the need for accurate and interpretable credit risk forecasting. Financial institutions must anticipate potential business loan delinquency to maintain portfolio stability and meet regulatory standards. This study proposes an interpretable multi-model framework that integrates statistical (correlation screening and ordinary least squares regression), probabilistic (Gaussian Naïve Bayes), and classical time-series (SARIMA) methods to balance explanatory insight and predictive accuracy in delinquency forecasting. Ordinary least squares regression is used to quantify the direction and strength of each driver and yields statistically significant coefficients (β ≈ 1.336 for the overdue 15+ days bucket, p < 10−22). The Naïve Bayes classifier provides a probabilistic early-warning signal with an out-of-sample accuracy of 55%, precision of 43%, recall of 75%, and ROC AUC of 0.371. Finally, a seasonal ARIMA model fitted on the selected regressors achieves a mean absolute percentage error (MAPE) of 7.6% and an out-of-sample R2 of 0.49, demonstrating competitive forecasting performance while maintaining interpretability. The results show that the framework offers actionable insights for risk managers by identifying key risk drivers, providing probabilistic alarms, and generating calibrated point forecasts. The proposed approach contributes to the development of intelligent and explainable forecasting and control systems for modern financial institutions.

Risks doi: 10.3390/risks14020024

Authors: Alexandros Garefalakis Erasmia Angelaki Christos Papademetriou Panagiotis Giannopoulos Markos Kourgiantakis

This paper investigates how monetary and ESG-related risks—especially those stemming from asymmetric policy transmission across Eurozone economies—have evolved over time, with a focus on the post-COVID-19 era. Using a mixed-method bibliometric analysis of 216 peer-reviewed articles (1996–2025), it maps thematic developments in monetary governance and sustainability discourse. Findings reveal a post-2020 surge in scholarly engagement, marked by a decisive shift: ESG risks, once peripheral, are now central to discussions of macro-financial stability and institutional resilience. This thematic realignment aligns with major EU regulatory milestones (e.g., SFDR, EU Taxonomy, CSRD), signaling a structural transformation in EU governance. The study concludes that the convergence of monetary asymmetry and ESG integration represents a new frontier in economic policy and academic inquiry, raising critical questions about institutional convergence, regulatory capacity, and sustainability-informed monetary frameworks in post-crisis Europe.

Risks doi: 10.3390/risks14020023

Authors: Yan Zhang Lin Chen Yixiang Tian

Interpretability analysis methods, such as LIME and SHAP, are widely employed to explain the predictions of artificial intelligence models; however, they primarily function as post hoc tools and do not directly quantify the intrinsic interpretability of the models. Although it is commonly assumed that model transparency decreases with increasing complexity, there is currently no standardized framework for evaluating interpretability as an inherent property of AI models. In this study, we examine the prediction of bond defaults using several widely used machine learning algorithms. The classification performance of each algorithm is first evaluated, followed by the application of LIME and SHAP to assess the influence of input features on model outputs. Based on these analyses, we propose a novel approach for quantifying intrinsic model interpretability. The results align with theoretical expectations and provide insights into the trade-off between model complexity and interpretability.

Risks doi: 10.3390/risks14020022

Authors: Toure Talnan Aboulaye Ouattara Zieh Moussa Kacou Yves Thierry Kacou Tuo Siele Jean

Motivated by the coexistence of retail and wholesale banks with distinct risk profiles under uniform capital regulation, and by the lack of quantitative evidence on whether differentiated capital requirements can reduce bank runs and interbank frictions in low-income monetary unions, this paper aims to determine a capital ratio for retail banks that can reduce the likelihood of bank runs in the WAEMU area. The study also compares the impact of imposing capital requirements on retail banks versus implementing the same level of regulation for wholesale banks. The key findings are as follows: A capital ratio of 10 percent for retail banks is found to be sufficient to reduce the probability of bank runs and mitigate interbank market frictions in the WAEMU area. Similarly, applying the same requirements to wholesale banks also reduces the likelihood of bank runs. Implementing capital requirements on retail banks does not significantly affect interbank lending costs, whereas imposing the same requirements on wholesale banks leads to an increase in these costs. Consequently, regulating retail banks tends to shift assets towards wholesale banks, while regulating wholesale banks reallocates assets towards retail banks. The calculated capital ratio of 10 percent for retail banks maximizes welfare, surpassing the welfare achieved when the same requirements are imposed on wholesale banks. Therefore, the same capital ratio offers greater stability benefits for retail banks than wholesale banks, highlighting the mismatch between uniform capital regulations and heterogeneous banking models.

Risks doi: 10.3390/risks14010021

Authors: Manaka Yamaguchi Kota Ogura Tomoka Kiba Mostafa Saidur Rahim Khan Yoshihiko Kadoya

Understanding how investors maintain positions during adverse market conditions, investment grip, is increasingly important as retail participation rises and information environments diversify. While prior research identifies demographic, psychological, and economic determinants of investment grip, little is known about how information sources influence investors’ tolerance for losses. This study examines the relationship between investment information channels and investment grip among Japanese retail investors using a large-scale dataset of 161,677 respondents from the 2025 Survey on Life and Money. Investment grip is measured through a hypothetical loss scenario, and ordered probit and probit models are used to analyze associations between loss tolerance, information sources, and investor characteristics. Results show that reliance on professional information sources such as outsourced independent financial advisors, one’s own securities company, other securities firms, and external financial experts is negatively associated with investment grip. Free information sources, including mass media and personal networks, are also linked to lower loss tolerance. In contrast, reliance on social media is consistently associated with higher investment grip. Financial literacy, wealth, and age increase investment grip, whereas risk aversion, short-term outlooks, and family responsibilities reduce it. These results have implications for policy design, advisory practices, and digital and AI-enhanced investment platforms.

Risks doi: 10.3390/risks14010020

Authors: Artid Sringam Pongpisit Wuttidittachotti

As Thailand’s financial sector accelerates its digital transformation, cybersecurity has transitioned from a mere technical support function to a strategic imperative that governs operational risk and financial stability. This study empirically examines the efficacy of cyber risk controls and their correlation with perceived organizational readiness. Utilizing a quantitative survey of 53 specialized practitioners (N = 53), we assessed maturity across the six dimensions of the Bank of Thailand’s Cyber Resilience Assessment regulatory framework: Governance, Identification, Protection, Detection, Response, and Third-Party Risk Management. While descriptive statistics indicate high overall maturity (x¯ = 4.19, S.D. = 0.37), multiple regression analysis uncovers a critical “Protection Paradox”. Specifically, the “Protection” dimension exhibits a statistically significant negative impact on readiness (β = −0.432, p = 0.01), suggesting that over-engineered technical controls induce operational friction. In contrast, “Identification” emerged as the primary positive driver of readiness (β = 0.627, p < 0.01), highlighting visibility as a superior strategic lever. Furthermore, a structural disconnect was identified between strategic “Governance” and “Third-Party Risk Management” (r = 0.46), highlighting a “Silo Effect” where board-level policy fails to effectively mitigate supply chain risks. These findings suggest that financial institutions must pivot from volume-based compliance to risk-optimized integration to bridge these strategic and operational gaps.

Risks doi: 10.3390/risks14010019

Authors: Wassim J. Aloulou Nawaf F. Alshohail

This study investigates the impact of Governance, Risk management, and Compliance (GRC) practices on operational efficiency and corporate reputation. Drawing on the Resource-Based View (RBV), Stakeholder Theory, and the signaling perspective, it conceptualizes GRC as a set of organizational capabilities that enhance operational efficiency and company reputation. It also examines the mediating role of operational efficiency in the GRC–reputation relationship, particularly within technologically advanced and regulated sectors. Data were collected through a structured questionnaire distributed to 126 professionals across various Saudi technology-driven organizations, and the analyses combined descriptive statistics, hierarchical regression, and bootstrapped mediation testing using PROCESS to assess direct and indirect effects. The results indicate that operational efficiency partially mediates the effects of governance and compliance on reputation, supporting the argument that strengthened internal processes enhance external stakeholder evaluations; meanwhile, no mediation was found for risk management. Although the study offers meaningful insights, its sample size and sectoral focus limit the generalizability of conclusions, suggesting the need for broader or longitudinal research. This study contributes by advancing the conceptualization of GRC as organizational capabilities and empirically demonstrating their roles in strengthening both efficiency and reputation within technology-driven firms where digital governance and compliance capabilities are increasingly central.

Risks doi: 10.3390/risks14010018

Authors: Badar Nadeem Ashraf Ningyu Qian

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.

Risks doi: 10.3390/risks14010017

Authors: Ana Flávia Albuquerque Ventura Roberto Frota Decourt Clea Beatriz Macagnan

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.