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)
Latest Articles
Beyond the Rating: How Disagreement Among ESG Agencies Affects Bond Credit Spreads
Risks 2025, 13(10), 206; https://doi.org/10.3390/risks13100206 - 21 Oct 2025
Abstract
Based on data from Chinese corporate bonds issued between 2014 and 2023, this study examines how ESG rating disagreement affects credit spreads. The results indicate that such disagreement significantly increases spreads through financial risk and information asymmetry channels, though this effect is mitigated
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Based on data from Chinese corporate bonds issued between 2014 and 2023, this study examines how ESG rating disagreement affects credit spreads. The results indicate that such disagreement significantly increases spreads through financial risk and information asymmetry channels, though this effect is mitigated by higher bond ratings. The impact is more pronounced in developed regions, highly marketized areas, less polluted and less competitive industries, non-Big Four audited firms, small enterprises, and state-owned enterprises. Increases in credit spreads are mainly driven by environmental and social rating disagreements, with the governance dimension playing a limited role.
Full article
(This article belongs to the Special Issue Climate Risk in Financial Markets and Institutions)
Open AccessArticle
Study on the Nonlinear Volatility Correlation Characteristics Between China’s Carbon and Energy Markets
by
Tian Zhang and Shaohui Zou
Risks 2025, 13(10), 205; https://doi.org/10.3390/risks13100205 - 17 Oct 2025
Abstract
The energy sector, as a major source of carbon emissions, has a significant impact on the operation of the carbon market and the management of carbon emissions. With the introduction of the “dual carbon” goals, the Chinese government has actively implemented measures to
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The energy sector, as a major source of carbon emissions, has a significant impact on the operation of the carbon market and the management of carbon emissions. With the introduction of the “dual carbon” goals, the Chinese government has actively implemented measures to reduce carbon emissions, making the carbon market an important tool for emission reduction. Therefore, characterizing the inter-market relationships helps enhance decision-making for market participants and promotes sustainable economic development. This study selects the price of the Chinese carbon emission trading market, which began trading on 16 July 2021, as a representative of the carbon market price. In terms of energy market selection, the prices of electricity, new energy, and coal are chosen as representatives of the energy market. From the perspective of the nonlinear dependency structure between market prices, a “carbon ↔ electricity ↔ new energy ↔ coal market” multi-to-multi interaction model is constructed, and the MSVAR model is employed to study the nonlinear dependency characteristics between market prices under interactive influences. The results show that there is a significant nonlinear dependency structure between the four market prices, especially between the carbon market and the new energy market. These market prices exhibit different behavioral characteristics under different states, with non-stationary states being the most common. There is a strong positive correlation between the electricity market and new energy market prices, while the relationship between the carbon market and other market prices is relatively weaker. The relevant conclusions provide valuable insights for policymakers and investors, helping them better understand and predict future market dynamics.
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(This article belongs to the Special Issue How Does Green Finance Affect Net-Zero Targets, Sustainable Production, and Consumption: Principles, Risks, and Strategies)
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The Association of Financial Knowledge, Attitude, and Behavior with Investment Loss Tolerance: Evidence from Japan
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Manaka Yamaguchi, Kota Ogura, Yuzuha Himeno, Asahi Shiiku, Hibiki Nagahama, Honoka Nabeshima, Yu Kuramoto, Mostafa Saidur Rahim Khan and Yoshihiko Kadoya
Risks 2025, 13(10), 204; https://doi.org/10.3390/risks13100204 - 15 Oct 2025
Abstract
Investment loss tolerance refers to an investor’s willingness to hold financial instruments after experiencing value declines and is considered essential to long-term investment success. Financial literacy, comprising financial knowledge, attitude, and behavior, has been widely identified as a key factor in promoting rational
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Investment loss tolerance refers to an investor’s willingness to hold financial instruments after experiencing value declines and is considered essential to long-term investment success. Financial literacy, comprising financial knowledge, attitude, and behavior, has been widely identified as a key factor in promoting rational financial decisions. A recent study by Homma et al. suggests that the three components can help prevent panic selling during market crises, such as the COVID-19 pandemic. However, that study relies on binary behavioral indicators within crisis-specific contexts, limiting the generalizability of their findings. To address these gaps, the present study quantitatively measures investment loss tolerance using a generalized hypothetical loss scenario and investigates the associations of financial literacy components. Using a large-scale dataset of 161,223 active investors from one of Japan’s largest online securities firms, we conducted ordered probit and probit regression analyses while controlling for demographic, socioeconomic, and psychological factors. The results reveal that financial knowledge, attitude, and behavior all have statistically significant positive effects on investment loss tolerance. These findings indicate that financial literacy enhances investors’ capacity to withstand losses and discourages premature asset liquidation, even outside crisis-specific contexts. The evidence supports policies aimed at improving financial literacy to foster more resilient investor behavior and promote long-term financial well-being.
Full article
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
Abstract
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
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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.
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(This article belongs to the Special Issue Market-Consistent Actuarial Valuation and Risk-Based Capital Assessment)
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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
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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
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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.
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Presidential Partisanship and Sectoral ETF Performance in U.S. Equity Markets
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Xiaoli Wang and Claire Guo
Risks 2025, 13(10), 201; https://doi.org/10.3390/risks13100201 - 14 Oct 2025
Abstract
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
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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
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
Abstract
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
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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.
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(This article belongs to the Special Issue Computational Methods and Models in the Financial Risk Management Process)
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The Impact of Enterprise Risk Management on Firm Competitiveness: The Mediating Role of Competitive Advantage in the Omani Insurance Industry
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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
Abstract
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
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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.
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(This article belongs to the Special Issue ESG and Greenwashing in Financial Institutions: Meet Risk with Action)
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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
Abstract
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
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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.
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(This article belongs to the Special Issue Artificial Intelligence Risk Management)
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Symmetric Positive Semi-Definite Fourier Estimator of Spot Covariance Matrix with High Frequency Data
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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
Abstract
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
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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)
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
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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
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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.
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The BTC Price Prediction Paradox Through Methodological Pluralism
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Mariya Paskaleva and Ivanka Vasenska
Risks 2025, 13(10), 195; https://doi.org/10.3390/risks13100195 - 4 Oct 2025
Abstract
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),
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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.
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(This article belongs to the Special Issue Portfolio Theory, Financial Risk Analysis and Applications)
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Resilience in Jordan’s Stock Market: Sectoral Volatility Responses to Financial, Political, and Health Crises
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Abdulrahman Alnatour
Risks 2025, 13(10), 194; https://doi.org/10.3390/risks13100194 - 4 Oct 2025
Abstract
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
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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 models for each sector–crisis window and summarize volatility dynamics by persistence , 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.
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(This article belongs to the Special Issue Risk Analysis in Financial Crisis and Stock Market)
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The Cannabis Conundrum: Persistent Negative Alphas and Portfolio Risks
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Davinder K. Malhotra and Sheetal Gupta
Risks 2025, 13(10), 193; https://doi.org/10.3390/risks13100193 - 3 Oct 2025
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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
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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.
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The Italian Actuarial Climate Index: A National Implementation Within the Emerging European Framework
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Barbara Rogo, José Garrido and Stefano Demartis
Risks 2025, 13(10), 192; https://doi.org/10.3390/risks13100192 - 3 Oct 2025
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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
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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.
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(This article belongs to the Special Issue Climate Change and Financial Risks)
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Examining Strategies to Manage Climate Risks of PPP Infrastructure Projects
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Isaac Akomea-Frimpong and Andrew Victor Kabenlah Blay Jnr
Risks 2025, 13(10), 191; https://doi.org/10.3390/risks13100191 - 3 Oct 2025
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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
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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.
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(This article belongs to the Special Issue Climate Risk in Financial Markets and Institutions)
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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
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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
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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.
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(This article belongs to the Special Issue Volatility Modeling in Financial Market)
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Cryptocurrencies as a Tool for Money Laundering: Risk Assessment and Perception of Threats Based on Empirical Research
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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
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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
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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.
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The Illusion of Control: How Knowledge and Expertise Misclassify Uncertainty as Risk
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Alessio Faccia, Pythagoras Petratos and Francesco Manni
Risks 2025, 13(10), 188; https://doi.org/10.3390/risks13100188 - 1 Oct 2025
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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
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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.
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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
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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
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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.
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