Risks

25 pages, 644 KB

Open AccessArticle

Insider CEOs and Corporate Misconduct: Evidence from China

by Ying Zhang, Rusman bin Ghani and Danilah binti Salleh

Risks 2025, 13(9), 179; https://doi.org/10.3390/risks13090179 - 15 Sep 2025

Inspired by the limited research regarding the influence of CEO succession origin on corporate misconduct, this study draws on organizational identification theory and agency theory to examine this issue. Empirical analysis indicates that insider CEOs significantly constrain corporate misconduct in China. Furthermore, the [...] Read more.

Inspired by the limited research regarding the influence of CEO succession origin on corporate misconduct, this study draws on organizational identification theory and agency theory to examine this issue. Empirical analysis indicates that insider CEOs significantly constrain corporate misconduct in China. Furthermore, the moderating results indicate that internal control strengthens the negative association between insider CEOs and corporate misconduct, whereas institutional ownership weakens this governance effect. Further analysis confirms that the restraining effect of insider CEOs on corporate misconduct remains robust across different types of misconduct. Overall, our study emphasizes the positive role of insider CEOs from the perspective of CEO succession origins and provides valuable practical implications for controlling corporate misconduct. Full article

(This article belongs to the Special Issue Risk Management for Capital Markets)

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12 pages, 378 KB

Open AccessArticle

Correlation Metrics for Safe Artificial Intelligence

by Golnoosh Babaei and Paolo Giudici

Risks 2025, 13(9), 178; https://doi.org/10.3390/risks13090178 - 12 Sep 2025

Viewed by 212

Abstract

There is a growing need to provide AI risk management models that can assess whether AI applications are safe and trustworthy, to make them responsible. To date, there are a few research papers on this topic. To fill the gap, in this paper [...] Read more.

There is a growing need to provide AI risk management models that can assess whether AI applications are safe and trustworthy, to make them responsible. To date, there are a few research papers on this topic. To fill the gap, in this paper we extend the recently proposed SAFE framework, a comprehensive approach to measure AI risks across four key dimensions: security, accuracy, fairness, and explainability (SAFE). We contribute to the SAFE framework with a novel use of the coefficient of determination (

R^{2}

) to quantify deviations from ideal behavior not only in terms of accuracy but also for security, fairness, and explainability. Our empirical findings shows the effectiveness of the proposal, which leads to a more precise measurement of risks of AI regression applications, which involve the prediction of continuous response variables. Full article

17 pages, 534 KB

Open AccessArticle

Digital Transformation and Entrepreneurial Risk-Taking: Navigating Affordance and Apprehension in SME Intentions

by Konstantinos S. Skandalis and Dimitra Skandali

Risks 2025, 13(9), 177; https://doi.org/10.3390/risks13090177 - 11 Sep 2025

Viewed by 227

Abstract

Digitalization is reshaping entrepreneurship, yet the mechanisms that translate new technological possibilities into entrepreneurial intention remain poorly understood, especially for resource-constrained small and medium-sized enterprises (SMEs). Building on the Theory of Planned Behaviour, Entrepreneurial Risk-Taking Theory and Affordance Theory, this study proposes and [...] Read more.

Digitalization is reshaping entrepreneurship, yet the mechanisms that translate new technological possibilities into entrepreneurial intention remain poorly understood, especially for resource-constrained small and medium-sized enterprises (SMEs). Building on the Theory of Planned Behaviour, Entrepreneurial Risk-Taking Theory and Affordance Theory, this study proposes and tests an integrated model that captures how individual cognition, digital capability and platform-related risk interact to shape digital entrepreneurial intention (DEI). Survey data from 428 Greek SME owner-managers were analyzed with Partial Least Squares Structural Equation Modelling (PLS-SEM). Results show that entrepreneurial self-efficacy, financial risk tolerance, digital literacy and perceived platform affordances each exert significant positive effects on DEI, whereas perceived platform risk exerts a significant negative effect. Importantly, platform risk also dampens the positive impact of self-efficacy, revealing a boundary condition often overlooked in intention research. The findings position digital transformation as a double-edged phenomenon amplifying opportunity through affordances while simultaneously magnifying risk. The study advances theory by integrating risk perceptions and affordance recognition into a TPB framework, and it offers actionable guidance: policy makers should stabilize digital-regulatory regimes, platform providers should increase transparency and reliability, and SME support programs should blend digital-skills training with calibrated risk-management tools. Together, such measures can convert latent entrepreneurial confidence into resilient digital venture creation. This study contributes to theory by extending the Theory of Planned Behaviour with risk-sensitive boundary conditions, broadening Risk-Taking Theory to account for platform-specific uncertainties, and validating Affordance Theory in a digital SME context. Practically, it provides actionable guidance for entrepreneurs, policymakers, and platform operators on balancing digital capability development with systemic risk governance. Full article

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25 pages, 485 KB

Open AccessArticle

Factor Structure of Green, Grey, and Red EU Securities

by Ferdinantos Kottas

Risks 2025, 13(9), 176; https://doi.org/10.3390/risks13090176 - 11 Sep 2025

Viewed by 175

Abstract

This study examined the factor structure of Green, Grey, and Red EU securities using extended asset pricing models built on the Fama–French and Carhart frameworks. The findings show improved return predictability and consistently negative risk-adjusted alpha across categories post-Global Financial Crisis (GFC), suggesting [...] Read more.

This study examined the factor structure of Green, Grey, and Red EU securities using extended asset pricing models built on the Fama–French and Carhart frameworks. The findings show improved return predictability and consistently negative risk-adjusted alpha across categories post-Global Financial Crisis (GFC), suggesting systematic overestimation of expected returns. All environmental asset types are positively linked to the MKTRF, SMB, HML, and HML^Devil factors, indicating exposure to core risk premia. Green securities exhibit elevated currency risk and persistent negative momentum, while Red assets transition from positive to negative momentum. Green and Red securities show stronger gold associations post-GFC, signaling a hedging role. Grey assets shift away from safe-haven behavior, becoming more sensitive to volatility. FEAR factor exposure and QML results suggest evolving sensitivity and declining quality, particularly in Grey assets. These findings underscore the need for enriched asset pricing models to capture dynamic risk characteristics in environmental assets within the EU financial markets. Full article

(This article belongs to the Special Issue Risk and Return Analysis in the Stock Market)

24 pages, 784 KB

Open AccessArticle

Crisis, Support, and Structural Risk: Assessing the Financial Impact of COVID-19 on Polish Regional Airports

by Anna Zamojska, Magdalena Mosionek-Schweda, Dariusz Tłoczyński and Karolina Diakowska

Risks 2025, 13(9), 175; https://doi.org/10.3390/risks13090175 - 11 Sep 2025

Viewed by 171

Abstract

The global aviation sector underwent an unprecedented shock due to the COVID-19 pandemic, severely disrupting the passenger flows, flight operations, and revenues of Polish airports. In response, the government launched protective measures under the Anti-Crisis Shield and the COVID-19 Counteraction Fund. This study [...] Read more.

The global aviation sector underwent an unprecedented shock due to the COVID-19 pandemic, severely disrupting the passenger flows, flight operations, and revenues of Polish airports. In response, the government launched protective measures under the Anti-Crisis Shield and the COVID-19 Counteraction Fund. This study examines the financial impact of such public support on 12 Polish airports between 2016 and 2022, applying a two-step cointegration ECM framework with Driscoll–Kraay inference. Profitability (ROA, ROE, OM), liquidity, debt, and operational activity indicators were analysed, with particular attention to methodological distortions arising from including subsidies in operating revenues. The results indicate a material decline in profitability from 2020 to 2022, albeit with pronounced heterogeneity across airports. Larger hubs (Warsaw–Chopin, Kraków, Gdansk, Katowice, Poznan, and Wroclaw) demonstrated relative resilience, while many smaller, regionally owned airports (e.g., Bydgoszcz, Lodz, Lublin, Olsztyn-Mazury, Zielona Gora) remained structurally unprofitable despite substantial subsidies. In several cases, profitability, liquidity, and operating activity recovered by 2021–2022, yet the improvement was not uniform: for fiscally dependent airports, transfers merely masked persistent inefficiencies. Passenger volumes, flight operations, and employment emerged as the primary performance drivers, while capital expenditure, turnover of current assets, and liquidity were particularly relevant for ROE. The novelty of this research lies in disentangling the stabilising effect of subsidies from underlying profitability, revealing how non-market revenues distort standard performance metrics and accelerate short-run adjustment dynamics once netted out. The findings demonstrate asymmetric impacts of state aid across ownership structures, i.e., central state control at Warsaw versus regional self-government involvement elsewhere, and highlight structural inefficiencies that weaken systemic resilience. These insights underline the importance of subsidy-adjusted financial indicators, more selective allocation of support, and reporting standards that separate operating from non-market revenues to enhance resilience and ensure sustainable airport operations. Full article

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17 pages, 1157 KB

Open AccessFeature PaperArticle

Numerical Calculation of Finite-Time Ruin Probabilities in the Dual Risk Model

by Rui M. R. Cardoso and Andressa C. O. Melo

Risks 2025, 13(9), 174; https://doi.org/10.3390/risks13090174 - 11 Sep 2025

Viewed by 157

Abstract

In the dual risk model, while the ultimate ruin probability has an exact and straightforward formula, the mathematics becomes significantly more complex when considering a finite time horizon, and the literature on this topic is scarce. As a result, there is a need [...] Read more.

In the dual risk model, while the ultimate ruin probability has an exact and straightforward formula, the mathematics becomes significantly more complex when considering a finite time horizon, and the literature on this topic is scarce. As a result, there is a need for numerical approximations. To address this, we develop two numerical algorithms that can accommodate a wide range of distributions for the amount of individual earnings with minimal adjustments. These algorithms are grounded in the methodologies proposed by Cardoso and Egídio dos Reis (2002) and De Vylder and Goovaerts (1988), which involve approximating the continuous risk process with a discrete-time Markov chain framework. We work out some examples, providing approximate values for the density of the time to ruin, and we compare, in the long run, our approximations with the exact values for the ultimate ruin probability to evaluate their accuracy. We also benchmark our results against the few existing figures available in the literature. Our findings suggest that the proposed approaches offer an efficient and flexible methodology for computing finite-time ruin probabilities in the dual risk model. Full article

(This article belongs to the Special Issue Advances in Risk Models and Actuarial Science)

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23 pages, 413 KB

Open AccessArticle

Bank Mergers, Information Asymmetry, and the Architecture of Syndicated Loans: Global Evidence, 1982–2020

by Mohammed Saharti

Risks 2025, 13(9), 173; https://doi.org/10.3390/risks13090173 - 11 Sep 2025

Viewed by 174

Abstract

This study investigates how bank mergers and acquisitions (M&As) reshape the monitoring architecture of syndicated loans and, by extension, borrowers’ financing conditions. Using a global panel of 20,299 syndicated loan contracts, originating in 43 countries between 1982 and 2020, we link LPC DealScan [...] Read more.

This study investigates how bank mergers and acquisitions (M&As) reshape the monitoring architecture of syndicated loans and, by extension, borrowers’ financing conditions. Using a global panel of 20,299 syndicated loan contracts, originating in 43 countries between 1982 and 2020, we link LPC DealScan data to Securities Data Company M&A records to trace each loan’s lead arrangers before and after consolidation events. Fixed-effects regressions, enriched with borrower- and loan-level controls, reveal three key patterns. First, post-merger loans exhibit significantly more concentrated syndicates: the Herfindahl–Hirschman Index rises by roughly 130 points and lead arrangers retain an additional 0.8–1.1 percentage points of the loan, consistent with heightened monitoring incentives. Second, these effects are amplified when information asymmetry is acute, i.e., for opaque or unrated firms, supporting moral hazard theory predictions that lenders internalize greater risk by holding larger stakes. Third, relational capital tempers the impact of consolidation: borrowers with repeated pre-merger relationships face smaller increases in syndicate concentration, while switchers experience the most significant jumps. Robustness checks using lead arranger market share, alternative spread measures, and lag structures confirm the findings. Overall, the results suggest that bank consolidation strengthens lead arrangers’ incentives to monitor but simultaneously reduces risk-sharing among participant lenders. For borrowers, the net effect is a trade-off between potentially tighter oversight and reduced syndicate diversification, with the balance hinging on transparency and prior ties to the lender. These insights refine our understanding of how structural shifts in the banking sector cascade into corporate credit markets and should inform both antitrust assessments and borrower funding strategies. Full article

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

18 pages, 664 KB

Open AccessArticle

Explainable Machine Learning Framework for Predicting Auto Loan Defaults

by Shengkun Xie and Tara Shingadia

Risks 2025, 13(9), 172; https://doi.org/10.3390/risks13090172 - 11 Sep 2025

Viewed by 278

Abstract

This study develops a machine learning framework to improve the prediction of automobile loan defaults by integrating explainable feature selection with advanced resampling techniques. Using publicly available data, we compare Logistic Regression, Random Forest, eXtreme Gradient Boosting (XGBoost), and Stacked classifiers. Feature selection [...] Read more.

This study develops a machine learning framework to improve the prediction of automobile loan defaults by integrating explainable feature selection with advanced resampling techniques. Using publicly available data, we compare Logistic Regression, Random Forest, eXtreme Gradient Boosting (XGBoost), and Stacked classifiers. Feature selection methods, including SHapley Additive exPlanations (SHAP) values and Mutual Information (MI), and resampling techniques such as Synthetic Minority Over-sampling TEchnique (SMOTE), SMOTE-Tomek, and SMOTE Edited Nearest Neighbor (SMOTE-ENN), are evaluated. The results show that combining SHAP-based feature selection with SMOTE-Tomek resampling and a Stacked Classifier consistently achieves superior predictive performance. These findings highlight the value of explainable AI in enhancing credit risk assessment for auto lending. This research also offers valuable insights for addressing other financial modeling challenges involving imbalanced datasets, supporting more informed and reliable decision-making. Full article

(This article belongs to the Special Issue Statistics, Stochastic Modelling and Quantitative Risk Management for Insurance)

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29 pages, 1977 KB

Open AccessArticle

Evaluating the Decline Registered Auditors Will Have on the Future of the Assurance Industry in South Africa

by Thameenah Abrahams and Masibulele Phesa

Risks 2025, 13(9), 171; https://doi.org/10.3390/risks13090171 - 10 Sep 2025

Viewed by 297

Abstract

Purpose: This article evaluated the decline of registered auditors (RAs) and its impact on the future of the assurance industry in South Africa. Auditors play a critical role in ensuring the transparency, trust, and credibility of financial statements. The decrease in the [...] Read more.

Purpose: This article evaluated the decline of registered auditors (RAs) and its impact on the future of the assurance industry in South Africa. Auditors play a critical role in ensuring the transparency, trust, and credibility of financial statements. The decrease in the number of registered auditors has become a pressing issue, raising concerns about the assurance industry’s ability to maintain a sufficient number of registered auditors and continue providing assurance services to public and private entities. Methodology: A qualitative Delphi methodology was employed, involving interviews with RAs who are registered with the Independent Regulatory Board for Auditors (IRBA). Eight RAs participated in structured interviews. This approach enabled the researcher to gather expert opinions, identify emerging trends, and explore challenges and opportunities within the audit profession related to the decline of RAs. Main findings: The decline of RAs is straining client demands, increasing workloads, and leading to a shortage of audit firms, which in turn affects audit quality and methodologies. Audit firms struggle to attract and retain talent due to regulatory burdens, economic pressures, and concerns about work–life balance. These pressures have resulted in higher audit fees, increased compliance costs, and more extensive training requirements. Smaller audit firms are especially impacted, with some downscaling their assurance services or exiting the market entirely. Practical implications: This study underscores the pressing need for regulatory bodies, such as the IRBA, to address the challenges faced by audit firms, particularly in terms of compliance and workforce retention. Proactive strategies are required to preserve the quality and accessibility of assurance services. Contribution: This study contributes to the ongoing discourse on the future of the audit profession by offering grounded insights into how the industry might sustain itself amid a declining number of RAs and changing professional dynamics. Full article

(This article belongs to the Special Issue Risks in Finance, Economy and Business on the Horizon in the 2030s)

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19 pages, 765 KB

Open AccessArticle

Digital Financial Literacy and Anxiety About Life After 65: Evidence from a Large-Scale Survey Analysis of Japanese Investors

by Jargalmaa Amarsanaa, Trinh Xuan Thi Nguyen, Yu Kuramoto, Mostafa Saidur Rahim Khan and Yoshihiko Kadoya

Risks 2025, 13(9), 170; https://doi.org/10.3390/risks13090170 - 8 Sep 2025

Viewed by 306

Abstract

In the context of Japan’s rapidly aging population, people’s anxiety about life after 65, especially regarding financial sustainability, has become a growing concern. This study examines old age anxiety through the lens of digital financial literacy (DFL), which can significantly impact people’s retirement [...] Read more.

In the context of Japan’s rapidly aging population, people’s anxiety about life after 65, especially regarding financial sustainability, has become a growing concern. This study examines old age anxiety through the lens of digital financial literacy (DFL), which can significantly impact people’s retirement well-being and long-term financial security in today’s digital environment. Drawing on a large-scale dataset from the “Survey on Life and Money,” jointly conducted by Rakuten Securities and Hiroshima University, we analyze responses from 94,695 individuals aged 40 to 64 who are active bank account holders. Based on ordinal logistic regression, our findings reveal a negative association between DFL and old age anxiety. Further analysis of the five dimensions of DFL demonstrates that several practical components, such as digital financial know-how, decision-making abilities, and self-protection skills, are associated with alleviated old age anxiety. In contrast, a reliance on basic financial knowledge and general awareness alone may exacerbate anxiety. These findings underscore the need to move beyond basic digital awareness and focus on promoting practical skills in digital finance, ultimately supporting better financial decision-making and enhancing overall well-being in older age. Full article

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28 pages, 1156 KB

Open AccessFeature PaperArticle

Financial Systemic Risk and the COVID-19 Pandemic

by Xin Huang

Risks 2025, 13(9), 169; https://doi.org/10.3390/risks13090169 - 4 Sep 2025

Viewed by 295

Abstract

The COVID-19 pandemic has caused market turmoil and economic distress. To understand the effect of the pandemic on the U.S. financial systemic risk, we analyze the explanatory power of detailed COVID-19 data on three market-based systemic risk measures (SRMs): Conditional Value at Risk, [...] Read more.

The COVID-19 pandemic has caused market turmoil and economic distress. To understand the effect of the pandemic on the U.S. financial systemic risk, we analyze the explanatory power of detailed COVID-19 data on three market-based systemic risk measures (SRMs): Conditional Value at Risk, Distress Insurance Premium, and SRISK. In the time-series dimension, we use the Dynamic OLS model and find that financial variables, such as credit default swap spreads, equity correlation, and firm size, significantly affect the SRMs, but the COVID-19 variables do not appear to drive the SRMs. However, if we focus on the first wave of the COVID-19 pandemic in March 2020, we find a positive and significant COVID-19 effect, especially before the government interventions. In the cross-sectional dimension, we run fixed-effect and event-study regressions with clustered variance-covariance matrices. We find that market capitalization helps to reduce a firm’s contribution to the SRMs, while firm size significantly predicts the surge in a firm’s SRM contribution when the pandemic first hits the system. The policy implications include that proper market interventions can help to mitigate the negative pandemic effect, and policymakers should continue the current regulation of required capital holding and consider size when designating systemically important financial institutions. Full article

(This article belongs to the Special Issue Systemic Risk in the Financial System: New Developments and Challenges)

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13 pages, 1224 KB

Open AccessArticle

Cryptocurrency Market Dynamics: Copula Analysis of Return and Volume Tails

by Giovanni De Luca and Andrea Montanino

Risks 2025, 13(9), 168; https://doi.org/10.3390/risks13090168 - 2 Sep 2025

Viewed by 496

Abstract

This paper investigates the dependence structure between returns and trading volumes for five major cryptocurrencies: Bitcoin, Cardano, Ethereum, Litecoin, and Ripple. Using a copula-based framework, we focus on a mixture of the Joe copula and its 90-degree rotation to capture asymmetric relationships, especially [...] Read more.

This paper investigates the dependence structure between returns and trading volumes for five major cryptocurrencies: Bitcoin, Cardano, Ethereum, Litecoin, and Ripple. Using a copula-based framework, we focus on a mixture of the Joe copula and its 90-degree rotation to capture asymmetric relationships, especially in the tails of the distribution. Our findings reveal significant upper and lower–upper tail dependencies, suggesting that extreme trading volumes are associated with both positive and negative return extremes. The results confirm a nonlinear and asymmetric volume–return relationship, which traditional linear models fail to capture. Full article

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18 pages, 1171 KB

Open AccessArticle

Financial Institutions of Emerging Economies: Contribution to Risk Assessment

by Yelena Popova, Olegs Cernisevs, Sergejs Popovs and Almas Kalimoldayev

Risks 2025, 13(9), 167; https://doi.org/10.3390/risks13090167 - 1 Sep 2025

Viewed by 408

Abstract

Conventional risk assessment frameworks usually define risk as a function of vulnerabilities and threats, but they frequently lack a single quantitative model that incorporates the unique features of each element. In order to close this gap, this paper creates a flexible, open, and [...] Read more.

Conventional risk assessment frameworks usually define risk as a function of vulnerabilities and threats, but they frequently lack a single quantitative model that incorporates the unique features of each element. In order to close this gap, this paper creates a flexible, open, and theoretically sound risk assessment formula that is still reliable even in the absence of complete vulnerability data. This is particularly important for financial institutions operating in emerging markets, where regulators rarely provide centralized vulnerability assessments and where Basel-type frameworks are only partially implemented. The contribution of the paper is a practically verified Bayesian network model that integrates threat likelihoods, vulnerability likelihoods, and their impacts within a probabilistic structure. Using 500 stratified Monte Carlo scenarios calibrated to real fintech and banking institutions operating under EU and national supervision, we demonstrate that excluding vulnerability impact from the model does not significantly reduce the predictive performance. These findings advance the theory of risk assessment, simplify practical implementation, and enhance the scalability of risk modeling for both traditional banks and fintech institutions in emerging economies. Full article

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19 pages, 395 KB

Open AccessFeature PaperArticle

Robust Tail Risk Estimation in Cryptocurrency Markets: Addressing GARCH Misspecification with Block Bootstrapping

by Christos Christodoulou-Volos

Risks 2025, 13(9), 166; https://doi.org/10.3390/risks13090166 - 29 Aug 2025

Viewed by 487

Abstract

This study examines the use of Filtered Historical Simulation (FHS) to estimate tail risk in cryptocurrency markets for the optimization of robustness in this area under model misspecification. An ARMA-GARCH model is employed on the daily returns on Binance Coin and Litecoin in [...] Read more.

This study examines the use of Filtered Historical Simulation (FHS) to estimate tail risk in cryptocurrency markets for the optimization of robustness in this area under model misspecification. An ARMA-GARCH model is employed on the daily returns on Binance Coin and Litecoin in order to compare the performance of classical and block bootstrap procedures in residual risk. Diagnostic tests indicate that standardized residuals are dependent, contrary to the independent and identically distributed (i.i.d.) assumption of conventional FHS. Comparing the block and ordinary bootstrapping approaches, we find that block bootstrap produces wider, more conservative confidence intervals, particularly in extreme tails (e.g., 0.1% and 99.9% percentiles). The findings suggest that block bootstrapping can be employed as a correction instrument in risk modeling where the standard volatility filters do not work. The article highlights the necessity to account for remaining dependencies and offers practical recommendations for more robust tail risk estimation during volatile markets. Full article

(This article belongs to the Special Issue Computational Methods and Models in the Financial Risk Management Process)

20 pages, 1969 KB

Open AccessArticle

Contagion or Decoupling? Evidence from Emerging Stock Markets

by Lumengo Bonga-Bonga and Zinzile Lorna Ndiweni

Risks 2025, 13(9), 165; https://doi.org/10.3390/risks13090165 - 29 Aug 2025

Viewed by 337

Abstract

This paper uses a statistical test based on entropy theory to propose a new way to distinguish between interdependence, contagion, and the decoupling hypotheses in the context of shock transmission and spillover. Applying the proposed approach, the three hypotheses are examined when measuring [...] Read more.

This paper uses a statistical test based on entropy theory to propose a new way to distinguish between interdependence, contagion, and the decoupling hypotheses in the context of shock transmission and spillover. Applying the proposed approach, the three hypotheses are examined when measuring the extent of shock spillover between selected developed and emerging markets during idiosyncratic crisis and normal periods. The US and EU are identified as developed economies. However, emerging markets are classified by regions to determine whether their responses to shocks from developed economies are homogeneous or heterogeneous depending on the region to which they belong. The suggested entropy test is based on the conditional correlations obtained from an asymmetric dynamic conditional correlation generalized autoregressive conditional heteroscedasticity (A-DCC GARCH) model. In addition to economic methods, statistical methods based on the regime-switching technique are used to date the different phases of the global financial crisis (GFC) and the European sovereign debt crisis (ESDC). Our findings show that all emerging markets decoupled from developed economies in at least one of the phases of the two crises. These findings provide valuable insights for policymakers, investors, and asset managers for portfolio allocation and financial regulations. Full article

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34 pages, 616 KB

Open AccessArticle

Does ERP Implementation Lower Corporate Financing Costs? A Dual Perspective from Risk Management and Value Creation

by Juanjuan Zhang, Song Zhou and Fuhui Ma

Risks 2025, 13(9), 164; https://doi.org/10.3390/risks13090164 - 27 Aug 2025

Viewed by 618

Abstract

This study examines the impacts of Enterprise Resource Planning (ERP) systems on financing costs from the dual perspectives of risk management and relative value creation based on corporate value maximization objectives. Data were manually collected from the listed companies in China. It is [...] Read more.

This study examines the impacts of Enterprise Resource Planning (ERP) systems on financing costs from the dual perspectives of risk management and relative value creation based on corporate value maximization objectives. Data were manually collected from the listed companies in China. It is found that the equity financing cost and debt financing cost of enterprises implementing ERP systems are both significantly higher than those without, and the impact of the ERP systems on equity financing cost is more significant than on debt financing cost. The endogeneity problems are addressed using the fixed effect, the instrumental variables in the two-stage least squares (2SLS) regression test, and the Heckman two-stage regression test. Further exploration into the underlying reasons for these results through mechanism analysis reveals that ERP systems can systematically and effectively enhance risk management levels and corporate value returns, bringing higher returns for investors and achieving a win-win situation. These research findings fundamentally help alleviate the agency problems between companies and investors, and also explain the advantages of an investment-oriented capital market in resolving conflicts among its various participants. Additionally, heterogeneity analysis further shows that the ownership structure and age structure of enterprises have a significantly negative moderating effect on the above results, and the moderating effect on equity financing cost is stronger than on debt financing cost. Full article

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34 pages, 1917 KB

Open AccessArticle

Enhancing Insurer Portfolio Resilience and Capital Efficiency with Green Bonds: A Framework Combining Dynamic R-Vine Copulas and Tail-Risk Modeling

by Thitivadee Chaiyawat and Pannarat Guayjarernpanishk

Risks 2025, 13(9), 163; https://doi.org/10.3390/risks13090163 - 27 Aug 2025

Viewed by 518

Abstract

This study develops an integrated risk modeling framework to assess capital adequacy and optimize portfolio performance for Thai life and non-life insurers. Leveraging ARMA–GJR–GARCH models with skewed Student-t innovations, extreme value theory, and dynamic R-vine copulas, the framework effectively captures volatility, tail risks, [...] Read more.

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

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28 pages, 802 KB

Open AccessArticle

On the Multi-Periodic Threshold Strategy for the Spectrally Negative Lévy Risk Model

by Sijia Shen, Zijing Yu and Zhang Liu

Risks 2025, 13(9), 162; https://doi.org/10.3390/risks13090162 - 22 Aug 2025

Viewed by 334

Abstract

As a crucial modeling tool for stochastic financial markets, the Lévy risk model effectively characterizes the evolution of risks during enterprise operations. Through dynamic evaluation and quantitative analysis of risk indicators under specific dividend- distribution strategies, this model can provide theoretical foundations for [...] Read more.

As a crucial modeling tool for stochastic financial markets, the Lévy risk model effectively characterizes the evolution of risks during enterprise operations. Through dynamic evaluation and quantitative analysis of risk indicators under specific dividend- distribution strategies, this model can provide theoretical foundations for optimizing corporate capital allocation. Addressing the inadequate adaptability of traditional single-period threshold strategies in time-varying market environments, this paper proposes a dividend strategy based on multiperiod dynamic threshold adjustments. By implementing periodic modifications of threshold parameters, this strategy enhances the risk model’s dynamic responsiveness to market fluctuations and temporal variations. Within the framework of the spectrally negative Lévy risk model, this paper constructs a stochastic control model for multiperiod threshold dividend strategies. We derive the integro-differential equations for the expected present value of aggregate dividend payments before ruin and the Gerber–Shiu function, respectively. Combining the methodologies of the discounted increment density, the operator introduced by Dickson and Hipp, and the inverse Laplace transforms, we derive the explicit solutions to these integro-differential equations. Finally, numerical simulations of the related results are conducted using given examples, thereby demonstrating the feasibility of the analytical method proposed in this paper. Full article

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24 pages, 3300 KB

Open AccessArticle

ETF Resilience to Uncertainty Shocks: A Cross-Asset Nonlinear Analysis of AI and ESG Strategies

by Catalin Gheorghe, Oana Panazan, Hind Alnafisah and Ahmed Jeribi

Risks 2025, 13(9), 161; https://doi.org/10.3390/risks13090161 - 22 Aug 2025

Viewed by 638

Abstract

This study investigates the asymmetric responses of AI and ESG Exchange Traded Funds (ETFs) to geopolitical and financial uncertainty, with a focus on resilience across market regimes. The NASDAQ-100 and MSCI ESG Leaders indices are used as proxies for thematic ETFs, and their [...] Read more.

This study investigates the asymmetric responses of AI and ESG Exchange Traded Funds (ETFs) to geopolitical and financial uncertainty, with a focus on resilience across market regimes. The NASDAQ-100 and MSCI ESG Leaders indices are used as proxies for thematic ETFs, and their dynamic interlinkages are examined in relation to volatility indicators (VIX, GPR), alternative assets (Bitcoin, Ethereum, gold, oil, natural gas), and safe-haven currencies (CHF, JPY). A daily dataset spanning the 2016–2025 period is analyzed using Quantile-on-Quantile Regression (QQR) and Wavelet Coherence (WCO), enabling a granular assessment of nonlinear, regime-dependent behaviors across quantiles. Results reveal that ESG ETFs demonstrate stronger downside resilience under extreme uncertainty, maintaining stability even during periods of elevated geopolitical and financial risk. In contrast, AI-themed ETFs tend to outperform under moderate-risk conditions but exhibit greater vulnerability during systemic stress, reflecting differences in asset composition and investor risk perception. The findings contribute to the literature on ETF resilience and cross-asset contagion by highlighting differential behavior patterns under varying uncertainty regimes. Practical implications emerge for investors and policymakers seeking to enhance portfolio robustness through thematic diversification during market turbulence. Full article

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14 pages, 1100 KB

Open AccessArticle

Algorithmic Bias Under the EU AI Act: Compliance Risk, Capital Strain, and Pricing Distortions in Life and Health Insurance Underwriting

by Siddharth Mahajan, Rohan Agarwal and Mihir Gupta

Risks 2025, 13(9), 160; https://doi.org/10.3390/risks13090160 - 22 Aug 2025

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Abstract

The EU Artificial Intelligence Act (Regulation (EU) 2024/1689) designates AI systems used in life and health insurance underwriting as high-risk systems, imposing rigorous requirements for bias testing, technical documentation, and post-deployment monitoring. Leveraging 12.4 million quote–bind–claim observations from four pan-European insurers (2019 Q1–2024 [...] Read more.

The EU Artificial Intelligence Act (Regulation (EU) 2024/1689) designates AI systems used in life and health insurance underwriting as high-risk systems, imposing rigorous requirements for bias testing, technical documentation, and post-deployment monitoring. Leveraging 12.4 million quote–bind–claim observations from four pan-European insurers (2019 Q1–2024 Q4), we evaluate how compliance affects premium schedules, loss ratios, and solvency positions. We estimate gradient-boosted decision tree (Extreme Gradient Boosting (XGBoost)) models alongside benchmark GLMs for mortality, morbidity, and lapse risk, using Shapley Additive Explanations (SHAP) values for explainability. Protected attributes (gender, ethnicity proxy, disability, and postcode deprivation) are excluded from training but retained for audit. We measure bias via statistical parity difference, disparate impact ratio, and equalized odds gap against the 10 percent tolerance in regulatory guidance, and then apply counterfactual mitigation strategies—re-weighing, reject option classification, and adversarial debiasing. We simulate impacts on expected loss ratios, the Solvency II Standard Formula Solvency Capital Requirement (SCR), and internal model economic capital. To translate fairness breaches into compliance risk, we compute expected penalties under the Act’s two-tier fine structure and supervisory detection probabilities inferred from GDPR enforcement. Under stress scenarios—full retraining, feature excision, and proxy disclosure—preliminary results show that bottom-income quintile premiums exceed fair benchmarks by 5.8 percent (life) and 7.2 percent (health). Mitigation closes 65–82 percent of these gaps but raises capital requirements by up to 4.1 percent of own funds; expected fines exceed rectification costs once detection probability surpasses 9 percent. We conclude that proactive adversarial debiasing offers insurers a capital-efficient compliance pathway and outline implications for enterprise risk management and future monitoring. Full article

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Risks, Volume 13, Issue 9 (September 2025) – 20 articles

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