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Journal Description

Risks

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

Open Access— free for readers, with article processing charges (APC) paid by authors or their institutions.
High visibility: indexed within Scopus, ESCI (Web of Science), EconLit, EconBiz, RePEc, and other databases.
Journal Rank: CiteScore - Q1 (Economics, Econometrics and Finance (miscellaneous))
Rapid Publication: manuscripts are peer-reviewed and a first decision is provided to authors approximately 23.2 days after submission; acceptance to publication is undertaken in 5.7 days (median values for papers published in this journal in the first half of 2025).
Recognition of Reviewers: reviewers who provide timely, thorough peer-review reports receive vouchers for a discount on the APC of their next publication in any MDPI journal, in appreciation of the work done

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

Imprint Information Journal Flyer Open Access ISSN: 2227-9091

Latest Articles

32 pages, 624 KB

Open AccessArticle

Effects of Traditional Reinsurance on Demographic Risk Under the Solvency II Framework

by Emily Bianchessi, Gian Paolo Clemente, Francesco Della Corte and Nino Savelli

Risks 2025, 13(10), 203; https://doi.org/10.3390/risks13100203 - 14 Oct 2025

This paper investigates the role of proportional reinsurance as a practical and flexible tool for managing demographic risk in life insurance, with a focus on its impact on both the Solvency Capital Requirement (SCR) and expected profitability. While much of the existing literature [...] Read more.

This paper investigates the role of proportional reinsurance as a practical and flexible tool for managing demographic risk in life insurance, with a focus on its impact on both the Solvency Capital Requirement (SCR) and expected profitability. While much of the existing literature focuses on mortality modeling or longevity-linked reinsurance instruments, this paper proposes a novel framework for analyzing traditional proportional reinsurance structures within the Solvency II market-consistent valuation environment. The framework integrates proportional reinsurance into the valuation of liabilities and the calculation of Solvency Capital Requirement, beginning with an outline of cash flow structures and their valuation under Solvency II principles. A key contribution is the introduction and decomposition of the net of reinsurance Claims Development Result (CDR), which allows us to assess the dual impact of reinsurance on risk mitigation and profit transfer. Through numerical analysis, we show how proportional reinsurance can effectively reduce capital requirements while quantifying the trade-off in expected profit transferred to the reinsurance company, with insights into how different reinsurance treaties affect capital efficiency and profitability. Full article

(This article belongs to the Special Issue Market-Consistent Actuarial Valuation and Risk-Based Capital Assessment)

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19 pages, 493 KB

Open AccessArticle

Hyperbolic Discounting and Its Influence on Loss Tolerance: Evidence from Japanese Investors

by Yu Kuramoto, Aliyu Ali Bawalle, Mostafa Saidur Rahim Khan and Yoshihiko Kadoya

Risks 2025, 13(10), 202; https://doi.org/10.3390/risks13100202 - 14 Oct 2025

Hyperbolic discounting, a key determinant of intertemporal behavior, captures individuals’ preferences for smaller immediate rewards over larger delayed ones. This study examined how hyperbolic discounting influences investment loss tolerance using a large-scale dataset of Japanese investors. Loss tolerance is defined as the extent [...] Read more.

Hyperbolic discounting, a key determinant of intertemporal behavior, captures individuals’ preferences for smaller immediate rewards over larger delayed ones. This study examined how hyperbolic discounting influences investment loss tolerance using a large-scale dataset of Japanese investors. Loss tolerance is defined as the extent of financial loss that an investor is willing to endure before changing their investment strategy. Although hyperbolic discounting shapes intertemporal investment decisions, its role in explaining loss tolerance remains largely unknown. Using a large dataset from the “Survey on Life and Money” comprising 107,294 observations and employing ordered probit regression, we found a significant negative relationship between hyperbolic discounting and investment loss tolerance: investors exhibiting stronger hyperbolic discounting are more likely to exit positions prematurely during market downturns, despite potential long-term recovery. The estimated marginal effect (−0.070 ***) underscores the economic significance of the association between hyperbolic discounting and loss tolerance. These results provide evidence that time-inconsistent preferences not only shape intertemporal choices but also reduce resilience to financial losses. The findings carry important implications for investors, highlighting the value of commitment mechanisms and education programs to counteract short-termism, and for policymakers seeking to design behavioral interventions that promote stable, long-term participation in financial markets. Full article

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14 pages, 334 KB

Open AccessArticle

Presidential Partisanship and Sectoral ETF Performance in U.S. Equity Markets

by Xiaoli Wang and Claire Guo

Risks 2025, 13(10), 201; https://doi.org/10.3390/risks13100201 - 14 Oct 2025

This study investigates how U.S. presidential political leadership affects financial market performance at the sector level, offering a novel contribution to the literature that has largely focused on aggregate market indices. While prior research documents partisan effects on overall stock returns, little is [...] Read more.

This study investigates how U.S. presidential political leadership affects financial market performance at the sector level, offering a novel contribution to the literature that has largely focused on aggregate market indices. While prior research documents partisan effects on overall stock returns, little is known about how different sectors respond to changes in political leadership. Using sector-specific exchange-traded funds (ETFs) categorized by the Global Industry Classification Standard (GICS), we examine sectoral return patterns and volatility under Republican and Democratic presidencies. This study contributes to the growing intersection of finance and political economy by providing a nuanced, empirical understanding of sectoral behavior across political cycles. The results offer valuable insights for investors, portfolio managers, and policymakers, enhancing their ability to anticipate sector-level risks and opportunities under changing political leadership. Full article

15 pages, 753 KB

Open AccessArticle

Estimating Policy Impact in a Difference-in-Differences Hazard Model: A Simulation Study

by David A. Hsieh

Risks 2025, 13(10), 200; https://doi.org/10.3390/risks13100200 - 13 Oct 2025

This article estimates the impact of a policy change on an event probability in a difference-in-differences hazard model using four estimators. We examine the error distributions of the estimators via a simulation experiment with twelve different scenarios. In four simulation scenarios when all [...] Read more.

This article estimates the impact of a policy change on an event probability in a difference-in-differences hazard model using four estimators. We examine the error distributions of the estimators via a simulation experiment with twelve different scenarios. In four simulation scenarios when all relevant variables are known, three of the four methods yield accurate estimates of the policy impact. In eight simulation scenarios when an individual characteristic is unobservable to the researcher, only one method (nonparametric maximum likelihood) achieves accurate estimates of the policy change. The other three methods (standard Cox, three-step Cox, and linear probability) are severely biased. Full article

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

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26 pages, 1423 KB

Open AccessArticle

The Impact of Enterprise Risk Management on Firm Competitiveness: The Mediating Role of Competitive Advantage in the Omani Insurance Industry

by Ammar Al Lawati, Baharuddin M. Hussin, Mohd Rizuan Abdul Kadir and Mohamed Khudari

Risks 2025, 13(10), 199; https://doi.org/10.3390/risks13100199 - 13 Oct 2025

In today’s complex economy, firms face various risks. The increasing risks and exposures hinder top performance and impede investments in new project circles. This study examines how Enterprise Risk Management (ERM) practices affect the non-financial performance of Omani insurance companies and investigates the [...] Read more.

In today’s complex economy, firms face various risks. The increasing risks and exposures hinder top performance and impede investments in new project circles. This study examines how Enterprise Risk Management (ERM) practices affect the non-financial performance of Omani insurance companies and investigates the partial mediating role of Competitive Advantage (CA). Using 439 survey responses analysed with PLS-SEM, the results reveal that ERM practices have a positive and significant effect on non-financial performance, and that CA mediates the effects of Internal Environment, Event Identification, and Risk Assessment. This reinforces the strategic dimension of embedding competitive advantage into risk management frameworks. This study offers evidence of how integrating ERM practices can impact organisational performance. It provides a foundation for ongoing research in sectors and areas not previously examined, particularly in developing countries where organisational resilience is imperative. Our study demonstrates how ERM enhances non-financial performance within insurance companies while supporting the view that ERM is a long-term strategic element, not merely limited to risk management. The research contributes evidence for broader application by demonstrating competitive advantage as a mediator. The model facilitates the investigation of ERM impacts across various sectors and regions, especially in developing countries where organisational resilience is crucial. Full article

(This article belongs to the Special Issue ESG and Greenwashing in Financial Institutions: Meet Risk with Action)

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25 pages, 1453 KB

Open AccessArticle

Application of Standard Machine Learning Models for Medicare Fraud Detection with Imbalanced Data

by Dorsa Farahmandazad, Kasra Danesh and Hossein Fazel Najaf Abadi

Risks 2025, 13(10), 198; https://doi.org/10.3390/risks13100198 - 13 Oct 2025

Medicare fraud poses a substantial challenge to healthcare systems, resulting in significant financial losses and undermining the quality of care provided to legitimate beneficiaries. This study investigates the use of machine learning (ML) to enhance Medicare fraud detection, addressing key challenges such as [...] Read more.

Medicare fraud poses a substantial challenge to healthcare systems, resulting in significant financial losses and undermining the quality of care provided to legitimate beneficiaries. This study investigates the use of machine learning (ML) to enhance Medicare fraud detection, addressing key challenges such as class imbalance, high-dimensional data, and evolving fraud patterns. A dataset comprising inpatient claims, outpatient claims, and beneficiary details was used to train and evaluate five ML models: Random Forest, KNN, LDA, Decision Tree, and AdaBoost. Data preprocessing techniques included resampling SMOTE method to address the class imbalance, feature selection for dimensionality reduction, and aggregation of diagnostic and procedural codes. Random Forest emerged as the best-performing model, achieving a training accuracy of 99.2% and validation accuracy of 98.8%, and F1-score (98.4%). The Decision Tree also performed well, achieving a validation accuracy of 96.3%. KNN and AdaBoost demonstrated moderate performance, with validation accuracies of 79.2% and 81.1%, respectively, while LDA struggled with a validation accuracy of 63.3% and a low recall of 16.6%. The results highlight the importance of advanced resampling techniques, feature engineering, and adaptive learning in detecting Medicare fraud effectively. This study underscores the potential of machine learning in addressing the complexities of fraud detection. Future work should explore explainable AI and hybrid models to improve interpretability and performance, ensuring scalable and reliable fraud detection systems that protect healthcare resources and beneficiaries. Full article

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

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30 pages, 515 KB

Open AccessArticle

Symmetric Positive Semi-Definite Fourier Estimator of Spot Covariance Matrix with High Frequency Data

by Jiro Akahori, Reika Kambara, Nien-Lin Liu, Maria Elvira Mancino, Tommaso Mariotti and Yukie Yasuda

Risks 2025, 13(10), 197; https://doi.org/10.3390/risks13100197 - 9 Oct 2025

This paper proposes a nonparametric estimator of the spot volatility matrix with high-frequency data. Our newly proposed Positive Definite Fourier (PDF) estimator produces symmetric positive semi-definite estimates and is consistent with a suitable choice of the localizing kernel. The PDF estimator is based [...] Read more.

This paper proposes a nonparametric estimator of the spot volatility matrix with high-frequency data. Our newly proposed Positive Definite Fourier (PDF) estimator produces symmetric positive semi-definite estimates and is consistent with a suitable choice of the localizing kernel. The PDF estimator is based on a modification of the Fourier estimation method introduced by Malliavin and Mancino. The estimator has two parameters: the frequency N, which controls the biases due to the asynchronicity effect and the market microstructure noise effect; and the localization parameter M for the employed Gaussian kernel. The sensitivity of the PDF estimator to the choice of these two parameters is studied in a simulated environment. The accuracy and the ability of the estimator to produce positive semi-definite covariance matrices are evaluated by an extensive numerical analysis, against competing estimators present in the literature. The results of the simulations are confirmed under different scenarios, including the dimensionality of the problem, the asynchronicity of data, and several different specifications of the market microstructure noise. The computational time required by the estimator and the stability of estimation are also tested with empirical data. Full article

(This article belongs to the Special Issue Integrating New Risks into Traditional Risk Management)

17 pages, 803 KB

Open AccessArticle

Bootstrap Initialization of MLE for Infinite Mixture Distributions with Applications in Insurance Data

by Aceng Komarudin Mutaqin

Risks 2025, 13(10), 196; https://doi.org/10.3390/risks13100196 - 4 Oct 2025

Maximum likelihood estimation (MLE) in infinite mixture distributions often lacks closed-form solutions, requiring numerical methods such as the Newton–Raphson algorithm. Selecting appropriate initial values is a critical challenge in these procedures. This study introduces a bootstrap-based approach to determine initial parameter values for [...] Read more.

Maximum likelihood estimation (MLE) in infinite mixture distributions often lacks closed-form solutions, requiring numerical methods such as the Newton–Raphson algorithm. Selecting appropriate initial values is a critical challenge in these procedures. This study introduces a bootstrap-based approach to determine initial parameter values for MLE, employing both nonparametric and parametric bootstrap methods to generate the mixing distribution. Monte Carlo simulations across multiple cases demonstrate that the bootstrap-based approaches, especially the nonparametric bootstrap, provide reliable and efficient initialization and yield consistent maximum likelihood estimates even when raw moments are undefined. The practical applicability of the method is illustrated using three empirical datasets: third-party liability claims in Indonesia, automobile insurance claim frequency in Australia, and total car accident costs in Spain. The results indicate stable convergence, accurate parameter estimation, and improved reliability for actuarial applications, including premium calculation and risk assessment. The proposed approach offers a robust and versatile tool both for research and in practice in complex or nonstandard mixture distributions. Full article

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43 pages, 4746 KB

Open AccessArticle

The BTC Price Prediction Paradox Through Methodological Pluralism

by Mariya Paskaleva and Ivanka Vasenska

Risks 2025, 13(10), 195; https://doi.org/10.3390/risks13100195 - 4 Oct 2025

Bitcoin’s extreme price volatility presents significant challenges for investors and traders, necessitating accurate predictive models to guide decision-making in cryptocurrency markets. This study compares the performance of machine learning approaches for Bitcoin price prediction, specifically examining XGBoost gradient boosting, Long Short-Term Memory (LSTM), [...] Read more.

Bitcoin’s extreme price volatility presents significant challenges for investors and traders, necessitating accurate predictive models to guide decision-making in cryptocurrency markets. This study compares the performance of machine learning approaches for Bitcoin price prediction, specifically examining XGBoost gradient boosting, Long Short-Term Memory (LSTM), and GARCH-DL neural networks using comprehensive market data spanning December 2013 to May 2025. We employed extensive feature engineering incorporating technical indicators, applied multiple machine and deep learning models configurations including standalone and ensemble approaches, and utilized cross-validation techniques to assess model robustness. Based on the empirical results, the most significant practical implication is that traders and financial institutions should adopt a dual-model approach, deploying XGBoost for directional trading strategies and utilizing LSTM models for applications requiring precise magnitude predictions, due to their superior continuous forecasting performance. This research demonstrates that traditional technical indicators, particularly market capitalization and price extremes, remain highly predictive in algorithmic trading contexts, validating their continued integration into modern cryptocurrency prediction systems. For risk management applications, the attention-based LSTM’s superior risk-adjusted returns, combined with enhanced interpretability, make it particularly valuable for institutional portfolio optimization and regulatory compliance requirements. The findings suggest that ensemble methods offer balanced performance across multiple evaluation criteria, providing a robust foundation for production trading systems where consistent performance is more valuable than optimization for single metrics. These results enable practitioners to make evidence-based decisions about model selection based on their specific trading objectives, whether focused on directional accuracy for signal generation or precision of magnitude for risk assessment and portfolio management. Full article

(This article belongs to the Special Issue Portfolio Theory, Financial Risk Analysis and Applications)

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25 pages, 4130 KB

Open AccessArticle

Resilience in Jordan’s Stock Market: Sectoral Volatility Responses to Financial, Political, and Health Crises

by Abdulrahman Alnatour

Risks 2025, 13(10), 194; https://doi.org/10.3390/risks13100194 - 4 Oct 2025

Sectoral vulnerability to distinct crisis types in small, open, and geopolitically exposed markets—such as Jordan—remains insufficiently quantified, constraining targeted policy design and portfolio allocation. This study’s primary purpose is to establish a transparent, comparable metric of sector-level market resilience that reveals how crisis [...] Read more.

Sectoral vulnerability to distinct crisis types in small, open, and geopolitically exposed markets—such as Jordan—remains insufficiently quantified, constraining targeted policy design and portfolio allocation. This study’s primary purpose is to establish a transparent, comparable metric of sector-level market resilience that reveals how crisis typology reorders vulnerabilities and shapes recovery speed. Applying this framework, we assess Jordan’s equity market across three archetypal episodes—the Global Financial Crisis, the Arab Spring, and COVID-19—to clarify how shock channels reconfigure sectoral risk. Using daily Amman Stock Exchange sector indices (2001–2025), we estimate

G A R C H (1,1)

models for each sector–crisis window and summarize volatility dynamics by persistence

(α + β)

, interpreted as an inverse proxy for resilience; complementary diagnostics include maximum drawdown and days-to-recovery, with nonparametric (Kruskal–Wallis) and rank-based (Spearman, Friedman) tests to evaluate within-crisis differences and cross-crisis reordering. Results show pronounced heterogeneity in every crisis and shifting sectoral rankings: financials—especially banking—display the highest persistence during the GFC; tourism and transportation dominate during COVID-19; and tourism/electric-related industries are most persistent around the Arab Spring. Meanwhile, food & beverages, pharmaceuticals/medical, and education recurrently exhibit lower persistence. Higher persistence aligns with slower post-shock normalization. We conclude that resilience is sector-specific and contingent on crisis characteristics, implying targeted policy and portfolio responses; regulators should prioritize liquidity backstops, timely disclosure, and contingency planning for fragile sectors, while investors can mitigate crisis risk via dynamic sector allocation and volatility-aware risk management in emerging markets. Full article

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

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19 pages, 435 KB

Open AccessArticle

The Cannabis Conundrum: Persistent Negative Alphas and Portfolio Risks

by Davinder K. Malhotra and Sheetal Gupta

Risks 2025, 13(10), 193; https://doi.org/10.3390/risks13100193 - 3 Oct 2025

This study investigates whether publicly listed cannabis shares provide enough risk-adjusted returns to warrant their incorporation into diversified portfolios. An equally weighted portfolio of cannabis companies is constructed using monthly data from January 2015 to December 2024. Risk-adjusted performance is assessed using the [...] Read more.

This study investigates whether publicly listed cannabis shares provide enough risk-adjusted returns to warrant their incorporation into diversified portfolios. An equally weighted portfolio of cannabis companies is constructed using monthly data from January 2015 to December 2024. Risk-adjusted performance is assessed using the Sharpe, Sortino, and Omega ratios and compared to the Russell 3000 Index and the FTSE All-World ex-US Index. In addition, we estimate both unconditional and conditional Fama–French five-factor model enhanced by momentum. The findings indicate that cannabis stocks persistently underperform U.S. and global benchmarks in both absolute and risk-adjusted metrics. Downside risk is elevated because cannabis portfolios exhibit much higher value at risk (VaR) and conditional value at risk (CVaR) than broad indices, especially after COVID-19. The findings show that cannabis stocks are quite volatile and fail to generate significant returns on a risk-adjusted basis. The study highlights the sector’s structural vulnerabilities and cautions investors, portfolio managers, and regulators against treating cannabis shares as dependable long-term investments. Full article

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53 pages, 7642 KB

Open AccessArticle

The Italian Actuarial Climate Index: A National Implementation Within the Emerging European Framework

by Barbara Rogo, José Garrido and Stefano Demartis

Risks 2025, 13(10), 192; https://doi.org/10.3390/risks13100192 - 3 Oct 2025

This paper presents the development of a high-resolution composite index to monitor and quantify climate-related risks across Italy. The country’s complex climatic variability, extensive coastline, and low insurance penetration highlight the urgent need for robust, locally calibrated tools to bridge the climate protection [...] Read more.

This paper presents the development of a high-resolution composite index to monitor and quantify climate-related risks across Italy. The country’s complex climatic variability, extensive coastline, and low insurance penetration highlight the urgent need for robust, locally calibrated tools to bridge the climate protection gap. Building on the methodological framework of existing actuarial climate indices, previously adapted for France and the Iberian Peninsula, the index integrates six standardised indicators capturing warm and cool temperature extremes, heavy precipitation intensity, dry spell duration, high wind frequency, and sea level change. It leverages hourly ERA5-Land reanalysis data and monthly sea level observations from tide gauges. Results show a clear upward trend in climate anomalies, with regional and seasonal differentiation. Among all components, sea level is most strongly correlated with the composite index, underscoring Italy’s vulnerability to marine-related risks. Comparative analysis with European indices confirms both the robustness and specificity of the Italian exposure profile, reinforcing the need for tailored risk metrics. The index can support innovative risk transfer mechanisms, including climate-related insurance, regulatory stress testing, and resilience planning. Combining scientific rigour with operational relevance, it offers a consistent, transparent, and policy-relevant tool for managing climate risk in Italy and contributing to harmonised European frameworks. Full article

(This article belongs to the Special Issue Climate Change and Financial Risks)

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20 pages, 1157 KB

Open AccessArticle

Examining Strategies to Manage Climate Risks of PPP Infrastructure Projects

by Isaac Akomea-Frimpong and Andrew Victor Kabenlah Blay Jnr

Risks 2025, 13(10), 191; https://doi.org/10.3390/risks13100191 - 3 Oct 2025

Tackling climate change in the public–private partnership (PPP) infrastructure sector requires radical transformation of projects to make them resilient against climate risks and free from excessive carbon emissions. Types of PPP infrastructure such as transport, power plants, hospitals, schools and residential buildings experience [...] Read more.

Tackling climate change in the public–private partnership (PPP) infrastructure sector requires radical transformation of projects to make them resilient against climate risks and free from excessive carbon emissions. Types of PPP infrastructure such as transport, power plants, hospitals, schools and residential buildings experience more than 30% of global climate change risks. Therefore, this study aims to examine the interrelationships between the climate risk management strategies in PPP infrastructure projects. The first step in conducting this research was to identify the strategies through a comprehensive literature review. The second step was data collection from 147 PPP stakeholders with a questionnaire. The third step was analysing the interrelationships between the strategies using a partial least square–structural equation model approach. The findings include green procurement, defined climate-resilient contract award criteria, the identification of climate-conscious projects and feasible contract management strategies. The results provide understanding of actionable measures to counter climate risks and they encourage PPP stakeholders to develop and promote climate-friendly strategies to mitigate climate crises in the PPP sector. The results also serve as foundational information for future studies to investigate climate change risk management strategies in PPP research. Full article

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

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17 pages, 576 KB

Open AccessArticle

Using Daily Stock Returns to Estimate the Unconditional and Conditional Variances of Lower-Frequency Stock Returns

by Chris Kirby

Risks 2025, 13(10), 190; https://doi.org/10.3390/risks13100190 - 3 Oct 2025

If intraday price data are unavailable, then using daily returns to construct realized measures of the variances of lower-frequency returns is a natural substitute for using high-frequency returns in this context. Notably, a suitable application of this approach yields realized measures that are [...] Read more.

If intraday price data are unavailable, then using daily returns to construct realized measures of the variances of lower-frequency returns is a natural substitute for using high-frequency returns in this context. Notably, a suitable application of this approach yields realized measures that are unbiased estimators of the unconditional and conditional variances of holding period returns for any investment horizon. I use a long sample of daily S&P 500 index returns to investigate the merits of constructing realized measures in this fashion. First, I conduct a Monte Carlo study using a data generating process that reproduces the key dynamic properties of index returns. The results of the study suggest that using realized measures constructed from daily returns to estimate the conditional and unconditional variances of lower-frequency returns should lead to substantial increases in efficiency. Next, I fit a multiplicative error model to the realized measures for weekly and monthly index returns to obtain out-of-sample forecasts of their conditional variances. Using the forecasts produced by a generalized autoregressive conditional heteroskedasticity model as a benchmark, I find that the forecasts produced by the multiplicative error model always generate lower mean absolute errors. Furthermore, the improvements in forecasting performance are statistically significant in most cases. Full article

(This article belongs to the Special Issue Volatility Modeling in Financial Market)

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17 pages, 1119 KB

Open AccessArticle

Cryptocurrencies as a Tool for Money Laundering: Risk Assessment and Perception of Threats Based on Empirical Research

by Marta Spyra, Rafał Balina, Marta Idasz-Balina, Adam Zając and Filip Różyński

Risks 2025, 13(10), 189; https://doi.org/10.3390/risks13100189 - 2 Oct 2025

As the global economy undergoes rapid digital transformation, cryptocurrencies have emerged as a prominent alternative class of financial assets. Their decentralized nature, pseudonymity, and lack of centralized oversight have attracted considerable interest among investors while simultaneously raising significant concerns among regulators and compliance [...] Read more.

As the global economy undergoes rapid digital transformation, cryptocurrencies have emerged as a prominent alternative class of financial assets. Their decentralized nature, pseudonymity, and lack of centralized oversight have attracted considerable interest among investors while simultaneously raising significant concerns among regulators and compliance professionals. While cryptocurrencies offer benefits such as enhanced accessibility and transactional privacy, they also pose notable risks, particularly their potential misuse in financial crimes, including money laundering. This study explores the perceived risks associated with cryptocurrencies in the context of money laundering, drawing on insights from a survey conducted among 50 financial sector professionals. A quantitative research design was employed, using a structured online questionnaire to assess participants’ awareness, investment behavior, and perceptions of the role of cryptocurrencies in illicit finance and financial system security. The results reveal a complex perspective: while 70% of respondents acknowledged the potential for cryptocurrencies to facilitate money laundering, 60% expressed support for their wider adoption. Notably, statistically significant correlations emerged between active investment in cryptocurrencies and the belief that they could enhance financial market security and reduce laundering risks. However, self-reported knowledge levels and general awareness did not show a significant relationship with perceived risk. The findings underscore the importance of a balanced approach to regulation, one that fosters innovation while mitigating illicit finance risks. The study recommends increased investment in user education, the development of blockchain analytics, the adoption of global regulatory standards and enhanced international cooperation to ensure the responsible evolution of the cryptocurrency ecosystem. Full article

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19 pages, 1172 KB

Open AccessArticle

The Illusion of Control: How Knowledge and Expertise Misclassify Uncertainty as Risk

by Alessio Faccia, Pythagoras Petratos and Francesco Manni

Risks 2025, 13(10), 188; https://doi.org/10.3390/risks13100188 - 1 Oct 2025

This study explores the critical yet often misunderstood distinction between risk and uncertainty. The research examines how knowledge and expertise can contribute to an illusion of control in uncertain environments, leading decision-makers to misclassify uncertainty as risk. This misclassification can lead to inadequate [...] Read more.

This study explores the critical yet often misunderstood distinction between risk and uncertainty. The research examines how knowledge and expertise can contribute to an illusion of control in uncertain environments, leading decision-makers to misclassify uncertainty as risk. This misclassification can lead to inadequate management of unforeseen events and suboptimal decision-making outcomes. The study introduces a novel matrix framework that categorises decision-making environments into four distinct quadrants based on knowledge, expertise, risk, and uncertainty. The framework helps decision-makers navigate the trade-off between risk and uncertainty, guiding them in assessing their current position and informing their decisions. Key findings reveal that expertise, while essential, can lead decision-makers to treat uncertainty as risk. The matrix offers guidance on how to better manage risk and uncertainty. Full article

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33 pages, 1881 KB

Open AccessArticle

Which Sectoral CDS Can More Effectively Hedge Conventional and Islamic Dow Jones Indices? Evidence from the COVID-19 Outbreak and Bubble Crypto Currency Periods

by Rania Zghal, Fredj Amine Dammak, Semia Souai, Nejib Hachicha and Ahmed Ghorbel

Risks 2025, 13(10), 187; https://doi.org/10.3390/risks13100187 - 28 Sep 2025

In this study, we aim to provide a comprehensive analysis of the risk management potential of sectoral Credit Default Swaps (CDSs) within financial portfolios. Our objectives are threefold: (i) to investigate the safe haven properties of sectoral CDSs; (ii) to assess their hedging [...] Read more.

In this study, we aim to provide a comprehensive analysis of the risk management potential of sectoral Credit Default Swaps (CDSs) within financial portfolios. Our objectives are threefold: (i) to investigate the safe haven properties of sectoral CDSs; (ii) to assess their hedging effectiveness and evaluate the diversification benefits of incorporating sectoral CDSs into both conventional and Islamic stock market portfolios; and (iii) to compare these findings with those obtained from alternative assets such as the VSTOXX, gold, and Bitcoin indices. To achieve this, we estimate time-varying hedge ratios using a range of multivariate GARCH (MGARCH) models and subsequently compute hedging effectiveness metrics. Conditional correlations derived from the Asymmetric Dynamic Conditional Correlation (ADCC) model are employed in linear regression analyses to assess safe haven characteristics. This methodology is applied across different subperiods to capture the impact of the crypto currency bubble and the COVID-19 pandemic on hedging performance. Full article

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31 pages, 4131 KB

Open AccessArticle

Emerging Risks in the Fintech-Driven Digital Banking Environment: A Bibliometric Review of China and India

by William Gaviyau and Jethro Godi

Risks 2025, 13(10), 186; https://doi.org/10.3390/risks13100186 - 26 Sep 2025

The digital revolution is transforming the financial services sector. Risk is not static; emerging risks continue to pose threats to the financial services sector which influences financial stability and consumer protection regulation mandates. This novel study presents a comparative bibliometric analysis of China [...] Read more.

The digital revolution is transforming the financial services sector. Risk is not static; emerging risks continue to pose threats to the financial services sector which influences financial stability and consumer protection regulation mandates. This novel study presents a comparative bibliometric analysis of China and India in examining the effect of trends on the scholarly research outputs discussing the emerging risks in the fintech-driven digital banking environment. Furthermore, the mapping presents the geographical dynamics of Asia, followed by country-level perspectives. The period of study was from 2015 to 2024. Leveraging the Scopus database, data was extracted based on a specified query using the SPAR 4 SLR protocol. Analysis was performed on 162 articles from an initial list of 1257 articles using Scival and Vos viewer tools. Performance indicator metrics and science mapping enabled the answering of research questions. The findings revealed that research output is inclined towards India rather than China; this is despite China domiciling some big tech firms. Comparatively, India dominates when it comes to performance analysis metrics compared to China. The scientific mapping depicted in both countries shows the multifaceted effects of fintech on banking, including trends in user acceptance, competition, emerging risks, technological innovation, and financial stability. The strong connections in both countries across clusters highlight how fintech research is multi-disciplinary, spanning consumer behavior, finance, economics, and financial technology. This study provides a foundation on which a robust risk management framework, which is customized to digital banking existence, can be developed in the face of emerging risks. Full article

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19 pages, 995 KB

Open AccessArticle

Exploring the Nature and Dynamics of Monetary–Fiscal Policy Interactions in South Africa

by Amanda Mavundla, Simiso Msomi and Malibongwe Cyprian Nyati

Risks 2025, 13(10), 185; https://doi.org/10.3390/risks13100185 - 26 Sep 2025

Understanding the nature of monetary and fiscal policy interactions has gained more importance over the years, especially within the context of the global financial crisis and the recent COVID-19 pandemic. This study uses a Time-Varying Parameter Vector Autoregressive (TVP-VAR) model and a Markov [...] Read more.

Understanding the nature of monetary and fiscal policy interactions has gained more importance over the years, especially within the context of the global financial crisis and the recent COVID-19 pandemic. This study uses a Time-Varying Parameter Vector Autoregressive (TVP-VAR) model and a Markov Switching Dynamic Regression (MSDR) framework to explore the dynamics of monetary–fiscal policy interactions in South Africa. The analysis employs time series data from 1994 to 2023 and tests the dynamic response of key macroeconomic variables to positive monetary and fiscal policy shocks. Furthermore, the MSDR framework is utilised to analyse how policy behaviour evolves during regime change. The TVP-VAR results show that fiscal expansions led to a positive response in GDP over time, a stable interest rate reaction post-COVID-19, and a consistently negative CPI response, contradicting conventional theory. The MSDR analysis reveals a dominant regime where monetary policy is active and fiscal policy is passive, with a positive interaction between interest rates and government spending, likely reflecting South Africa’s high debt environment. These findings underscore the importance of understanding policy interactions’ landscape to inform policy decisions better and minimise sub-optimal policy outcomes. Full article

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Assessing the Risk of Earnings Management Through the Lens of Individual Moral Philosophy: Insights from Accounting Professionals

by Anna Misztal and Michał Comporek

Risks 2025, 13(10), 184; https://doi.org/10.3390/risks13100184 - 25 Sep 2025

This study explores how individual moral philosophies influence accountants’ ethical perceptions of earnings management risk, addressing the broader question of how moral reasoning interacts with the cultural environment in shaping financial reporting decisions. Although accounting standards such as IFRS/IAS aim to harmonize reporting, [...] Read more.

This study explores how individual moral philosophies influence accountants’ ethical perceptions of earnings management risk, addressing the broader question of how moral reasoning interacts with the cultural environment in shaping financial reporting decisions. Although accounting standards such as IFRS/IAS aim to harmonize reporting, cultural, and institutional factors can lead professionals to interpret and apply them differently, making ethical perceptions context-dependent. Building on positive accounting theory and Forsyth’s model of personal moral philosophy, we conducted a scenario-based survey among Polish accounting professionals, using an extended set of earnings management scenarios developed by Bruns and Merchant and modified by Jooste. Our results indicate that subjectivists demonstrate greater ethical sensitivity to earnings-altering behavior, while absolutists exhibit the least. We also examined ethical evaluations across different types of earnings management practices, including income-increasing versus income-decreasing, accrual-based versus real earnings management, and multi-year versus single-year manipulations. Understanding how different moral orientations influence the perception of managerial interventions in reported figures can help executives foster an organizational culture that promotes the provision of reliable and accurate information to stakeholders. Study limitations include sample size and scope, suggesting the need for future research incorporating broader demographics and contextual variables. Full article

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