Risks

18 pages, 1360 KB

Open AccessArticle

Quantile-Based Safe Haven Analysis and Risk Interactions Between Green and Dirty Energy Futures

by Erginbay Uğurlu

Risks 2025, 13(8), 159; https://doi.org/10.3390/risks13080159 - 20 Aug 2025

Viewed by 333

This study investigates whether green assets can serve as safe havens for dirty assets in the context of carbon and energy futures markets. Using daily data from April 2021 to June 2025, the analysis focuses on four key instruments: carbon emissions futures and [...] Read more.

This study investigates whether green assets can serve as safe havens for dirty assets in the context of carbon and energy futures markets. Using daily data from April 2021 to June 2025, the analysis focuses on four key instruments: carbon emissions futures and crude oil futures, EUA futures, and natural gas futures. The study applies two main approaches—a conditional value-at-risk (CVaR)-based relative risk ratio (RRR) analysis and dynamic conditional correlation (DCC-GARCH) modeling—to assess tail risk mitigation and time-varying correlations. The results show that while green assets do not consistently act as safe havens during extreme market downturns, they can reduce the portfolio tail risk beyond certain allocation thresholds. Natural gas futures demonstrate significant volatility but offer diversification benefits when their portfolio weight exceeds 40%. EUA futures, although highly correlated with carbon emissions futures, show limited safe haven behavior. The findings challenge the assumption that green assets inherently provide downside protection and highlight the importance of strategic allocation. This research contributes to the literature by extending safe haven theory to environmental futures and offering empirical insights into the risk dynamics between green and dirty assets. Full article

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

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25 pages, 2948 KB

Open AccessArticle

Financial Mechanisms of Corporate Bankruptcy: Are They Different or Similar Across Crises?

by Katsuyuki Tanaka, Takuo Higashide, Takuji Kinkyo and Shigeyuki Hamori

Risks 2025, 13(8), 158; https://doi.org/10.3390/risks13080158 - 20 Aug 2025

Viewed by 309

Abstract

One primary objective of the early warning system literature is to construct more accurate financial vulnerability prediction models and investigate the mechanisms and key factors that differentiate healthy from vulnerable financial states. Despite the importance of identifying and predicting financial vulnerabilities, existing research [...] Read more.

One primary objective of the early warning system literature is to construct more accurate financial vulnerability prediction models and investigate the mechanisms and key factors that differentiate healthy from vulnerable financial states. Despite the importance of identifying and predicting financial vulnerabilities, existing research does not fully explain whether—and how—the financial behavior associated with corporate bankruptcy differs across crises. This study investigates (1) whether the financial mechanisms of corporate bankruptcy differ across three crises—the Global Financial Crisis, the European debt crisis, and the COVID-19 crisis; (2) whether these crises differ from tranquil periods before the Global Financial Crisis and after the European debt crisis; and (3) how these differences manifest. To conduct this analysis, we introduce a unique framework based on a random forest model, utilizing a corporate bankruptcy dataset spanning 2002–2023. The results show that the bankruptcy mechanisms during the Global Financial Crisis and the European debt crisis are not significantly different, whereas the COVID-19 crisis exhibits distinct characteristics. Additionally, we find that “Credit period days,” “Collection period days,” “Gross margin,” and “Solvency ratio (asset-based)” are key financial factors distinguishing these events. Full article

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33 pages, 732 KB

Open AccessArticle

Perceptions of Greenwashing and Purchase Intentions: A Model of Gen Z Responses to ESG-Labeled Digital Advertising

by Stefanos Balaskas, Ioannis Stamatiou, Kyriakos Komis and Theofanis Nikolopoulos

Risks 2025, 13(8), 157; https://doi.org/10.3390/risks13080157 - 19 Aug 2025

Viewed by 782

Abstract

This research examines the cognitive and psychological mechanisms underlying young adults’ reactions to ESG-labeled online advertisements, specifically resistance to persuasion and purchase intention. Based on dual-process theories of persuasion and digital literacy theory, we develop and test a structural equation model (SEM) of [...] Read more.

This research examines the cognitive and psychological mechanisms underlying young adults’ reactions to ESG-labeled online advertisements, specifically resistance to persuasion and purchase intention. Based on dual-process theories of persuasion and digital literacy theory, we develop and test a structural equation model (SEM) of perceived greenwashing, online advertising literacy, source credibility, persuasion knowledge, and advertising skepticism as predictors of behavioral intention. Data were gathered from 690 Greek consumers between the ages of 18–35 years through an online survey. All the direct effects hypothesized were statistically significant, while advertising skepticism was the strongest direct predictor of purchase intention. Mediation tests indicated that persuasion knowledge and skepticism partially mediated perceptions of greenwashing, literacy, and credibility effects, in favor of a complementary dual-route process of ESG message evaluation. Multi-group comparisons revealed significant moderation effects across gender, age, education, ESG familiarity, influencer trust, and ad-avoidance behavior. Most strikingly, women evidenced stronger resistance effects via persuasion knowledge, whereas younger users and those with lower familiarity with ESG topics were more susceptible to skepticism and greenwashing. Education supported the processing of source credibility and digital literacy cues, underlining the contribution of informational capital to persuasion resilience. The results provide theoretical contributions to digital persuasion and resistance with practical implications for marketers, educators, and policymakers seeking to develop ethical ESG communication. Future research is invited to broaden cross-cultural understanding, investigate emotional mediators, and incorporate experimental approaches to foster consumer skepticism and trust knowledge in digital sustainability messages. Full article

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

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16 pages, 667 KB

Open AccessArticle

Law Enforcement Impersonation Bank-Related Scams in South Africa: Perceived Vulnerability and Mitigative Strategies

by Ishmael Obaeko Iwara

Risks 2025, 13(8), 156; https://doi.org/10.3390/risks13080156 - 18 Aug 2025

Viewed by 542

Abstract

Bank scams involving the impersonation of law enforcement personnel and financial service providers continue to proliferate across South Africa, leading to substantial economic loss and psychological harm to certain individuals in the country. The persistence of this cyber-enabled fraud indicates a significant lacuna [...] Read more.

Bank scams involving the impersonation of law enforcement personnel and financial service providers continue to proliferate across South Africa, leading to substantial economic loss and psychological harm to certain individuals in the country. The persistence of this cyber-enabled fraud indicates a significant lacuna in understanding the systemic vulnerabilities that perpetrators exploit. Specifically, there is a pressing need to examine why these scams remain successful despite existing security measures, identify the key parameters that influence individuals’ susceptibility to deception, and assess the adequacy of current preventive measures. This study navigates these notable concerns using an exploratory case study qualitative research design. Through a non-probabilistic sampling strategy, seven participants were identified to engage in discourse and contributed insights into the subject matter. A nuanced analysis identified an effective monitoring system, heightened public awareness, and stringent penalties as mitigative strategies. Subsequent studies may examine the resultant strategies broadly for wider application. Full article

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22 pages, 1833 KB

Open AccessArticle

Survival Analysis for Credit Risk: A Dynamic Approach for Basel IRB Compliance

by Fernando L. Dala, Manuel L. Esquível and Raquel M. Gaspar

Risks 2025, 13(8), 155; https://doi.org/10.3390/risks13080155 - 15 Aug 2025

Viewed by 367

Abstract

This paper uses survival analysis as a tool to assess credit risk in loan portfolios within the framework of the Basel Internal Ratings-Based (IRB) approach. By modeling the time to default using survival functions, the methodology allows for the estimation of default probabilities [...] Read more.

This paper uses survival analysis as a tool to assess credit risk in loan portfolios within the framework of the Basel Internal Ratings-Based (IRB) approach. By modeling the time to default using survival functions, the methodology allows for the estimation of default probabilities and the dynamic evaluation of portfolio performance. The model explicitly accounts for right censoring and demonstrates strong predictive accuracy. Furthermore, by incorporating additional information about the portfolio’s loss process, we show how to empirically estimate key risk measures—such as Value at Risk (VaR) and Expected Shortfall (ES)—that are sensitive to the age of the loans. Through simulations, we illustrate how loss distributions and the corresponding risk measures evolve over the loans’ life cycles. Our approach emphasizes the significant dependence of risk metrics on loan age, illustrating that risk profiles are inherently dynamic rather than static. Using a real-world dataset of 10,479 loans issued by Angolan commercial banks, combined with assumptions regarding loss processes, we demonstrate the practical applicability of the proposed methodology. This approach is particularly relevant for emerging markets with limited access to advanced credit risk modeling infrastructure. Full article

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

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35 pages, 2736 KB

Open AccessArticle

The Implementation of ESG Indicators in the Balanced Scorecard—Case Study of LGOs

by Stavros Garefalakis, Erasmia Angelaki, Kostantinos Spinthiropoulos, George Tsamis and Alexandros Garefalakis

Risks 2025, 13(8), 154; https://doi.org/10.3390/risks13080154 - 15 Aug 2025

Viewed by 380

Abstract

This study investigates how Environmental, Social, and Governance (ESG) principles can be effectively integrated into the Balanced Scorecard (BSc) framework within local government organizations (LGOs) to enhance strategic planning and sustainability performance. Addressing a gap in the literature on ESG–BSc integration in the [...] Read more.

This study investigates how Environmental, Social, and Governance (ESG) principles can be effectively integrated into the Balanced Scorecard (BSc) framework within local government organizations (LGOs) to enhance strategic planning and sustainability performance. Addressing a gap in the literature on ESG–BSc integration in the public sector, particularly in the Greek context, the study employs a dual-method approach. First, a bibliometric analysis of 3053 academic publications (1993–2025) was conducted using Scopus data to assess the evolution and thematic focus of ESG and BSc research. Second, a structured questionnaire—comprising both closed- and open-ended questions—was administered to 17 administrative staff members of a Greek LGO in 2024. This expert sample provided insights into strategic planning practices, ESG awareness, and performance management barriers. The findings reveal low levels of ESG–BSc application, a limited strategic capacity, and institutional resistance. In response, the study proposes a novel, context-sensitive ESG-integrated BSc model tailored for small municipalities, emphasizing stakeholder participation, operational simplicity, and the alignment with national sustainability policies. The model serves as a practical tool to support public sector performance measurement, bridging the gap between sustainability goals and local governance strategy. Full article

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32 pages, 5167 KB

Open AccessFeature PaperArticle

Limiting Loss Distribution of Default and Prepayment for Loan Portfolios and Its Application in RMBS

by Chenxi Xia, Xin Zang, Lan Bu, Qinhan Duan and Jingping Yang

Risks 2025, 13(8), 153; https://doi.org/10.3390/risks13080153 - 15 Aug 2025

Viewed by 352

Abstract

This paper studies the joint distribution of the default and prepayment losses for a large portfolio of loans, based on a bottom-up approach. The repayment behaviors of loans in the portfolio are determined by both systematic and idiosyncratic risk factors and are conditionally [...] Read more.

This paper studies the joint distribution of the default and prepayment losses for a large portfolio of loans, based on a bottom-up approach. The repayment behaviors of loans in the portfolio are determined by both systematic and idiosyncratic risk factors and are conditionally independent given the systematic factors. The joint two-dimensional limit distributions of the portfolio default and prepayment losses are obtained, including the strong law of large numbers and the central limit theorem. A numerical study for the portfolio losses is performed for some simplified models. Finally, we conduct the empirical analysis on the residential mortgage-backed security (RMBS) based on Freddie Mac’s dataset. The empirical results reveal the impacts of different factors on the default and prepayment behaviors, and the distributions of the portfolio losses are simulated based on empirical estimation results to show its difference with the log-normal distributions. Full article

(This article belongs to the Special Issue Applied Financial and Actuarial Risk Analytics)

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19 pages, 407 KB

Open AccessArticle

Does IFRS Adoption Improve Analysts’ Earnings Forecasts? Evidence from Saudi Arabia

by Taoufik Elkemali

Risks 2025, 13(8), 152; https://doi.org/10.3390/risks13080152 - 14 Aug 2025

Viewed by 536

Abstract

This study explores how IFRS adoption is associated with analysts’ forecast accuracy, optimism, and dispersion in Saudi Arabia. Drawing on data from publicly listed firms from 2013 to 2020, we assess changes in forecasting behavior surrounding the IFRS transition, accounting for firm-specific and [...] Read more.

This study explores how IFRS adoption is associated with analysts’ forecast accuracy, optimism, and dispersion in Saudi Arabia. Drawing on data from publicly listed firms from 2013 to 2020, we assess changes in forecasting behavior surrounding the IFRS transition, accounting for firm-specific and macroeconomic factors. We argue that IFRS is expected to support more transparent financial statements, reduce risk and uncertainty, and offer a standardized and detailed reporting framework that influences analysts’ predictive performance. The findings reveal more accurate forecasts and a decline in both optimism and dispersion following IFRS adoption, suggesting enhanced financial reporting quality and reduced uncertainty. These associations underscore IFRS’s potential role in refining analysts’ earnings predictions and promoting stock market transparency. Full article

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

20 pages, 639 KB

Open AccessFeature PaperArticle

AI-Powered Reduced-Form Model for Default Rate Forecasting

by Jacopo Giacomelli

Risks 2025, 13(8), 151; https://doi.org/10.3390/risks13080151 - 13 Aug 2025

Viewed by 392

Abstract

This study aims to combine deep and recurrent neural networks with a reduced-form portfolio model to predict future default rates across economic sectors. The industry-specific forecasts for Italian default rates produced with the proposed approach demonstrate its effectiveness, achieving significant levels of explained [...] Read more.

This study aims to combine deep and recurrent neural networks with a reduced-form portfolio model to predict future default rates across economic sectors. The industry-specific forecasts for Italian default rates produced with the proposed approach demonstrate its effectiveness, achieving significant levels of explained variance. The results obtained show that enhancing a reduced-form model by integrating it with neural networks is possible and practical for multivariate forecasting of future default frequencies. In our analysis, we utilize the recently proposed RecessionRisk⁺, a reduced-form latent-factor model developed for default and recession risk management applications as an improvement of the well-known CreditRisk⁺ model. The model has been empirically verified to exhibit some predictive power concerning future default rates. However, the theoretical framework underlying the model does not provide the elements necessary to define a proper estimator for forecasting the target default rates, leaving space for the application of a neural network framework to retrieve the latent information useful for default rate forecasting purposes. Among the neural network models tested in combination with RecessionRisk⁺, the best results are obtained with shallow LSTM networks. Full article

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38 pages, 2503 KB

Open AccessArticle

Volatility Spillovers Between the U.S. and Romanian Markets: The BET–SFT-500 Dynamic Under Political Uncertainty

by Kamer-Ainur Aivaz, Lavinia Mastac, Dorin Jula, Diane Paula Corina Vancea, Cristina Duhnea and Elena Condrea

Risks 2025, 13(8), 150; https://doi.org/10.3390/risks13080150 - 13 Aug 2025

Viewed by 436

Abstract

This paper analyzes the volatility relationship between the Romanian BET index and the U.S. SFT-500 index during the period 2019–2024, with a particular focus on the impact of political and geopolitical shocks. The study investigates whether financial markets in emerging economies react symmetrically [...] Read more.

This paper analyzes the volatility relationship between the Romanian BET index and the U.S. SFT-500 index during the period 2019–2024, with a particular focus on the impact of political and geopolitical shocks. The study investigates whether financial markets in emerging economies react symmetrically or asymmetrically to external shocks originating from mature markets, especially during periods of political uncertainty. The research period includes four major systemic events: the COVID-19 pandemic, the military conflict in Ukraine, the 2024 U.S. presidential elections, and the 2024 Romanian elections, all of which generated significant volatility in global markets. The methodological approach combines time series econometrics with the Impulse Indicator Saturation (IIS) technique to identify structural breaks and outliers, without imposing exogenous assumptions about the timing of events. The econometric model includes autoregressive and lagged exogenous variables to estimate the influence of the SFT-500 index on the BET index, while IIS variables capture unanticipated political and economic shocks. Additionally, a Fractionally Integrated GARCH (FIGARCH) specification is applied to model the persistence of volatility over time, capturing the long-memory behavior often observed in emerging markets like Romania. The results confirm a statistically significant but partial synchronization between the two markets, with lagged and contemporaneous effects from the SFT-500 index on the BET index. Volatility in Romania is markedly higher and longer-lasting during domestic political episodes, confirming that local factors are a primary source of market instability. For investors, this underscores the need to embed political risk metrics into emerging market portfolios. For policymakers, it highlights how stronger institutions and transparent governance can dampen election- and crisis-related turbulence. Full article

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

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26 pages, 498 KB

Open AccessArticle

What Determines Digital Financial Literacy? Evidence from a Large-Scale Investor Study in Japan

by Sumeet Lal, Aliyu Ali Bawalle, Mostafa Saidur Rahim Khan and Yoshihiko Kadoya

Risks 2025, 13(8), 149; https://doi.org/10.3390/risks13080149 - 12 Aug 2025

Viewed by 1527

Abstract

The digitalization of financial systems has intensified risks such as cyber fraud, data breaches, and financial exclusion, particularly for individuals with low digital financial literacy (DFL). As digital finance becomes ubiquitous, DFL has emerged as a critical competency. However, the determinants of DFL [...] Read more.

The digitalization of financial systems has intensified risks such as cyber fraud, data breaches, and financial exclusion, particularly for individuals with low digital financial literacy (DFL). As digital finance becomes ubiquitous, DFL has emerged as a critical competency. However, the determinants of DFL remain insufficiently explored. This study aims to validate a comprehensive, theory-driven model that identifies the key sociodemographic, economic, and psychological factors that influence DFL acquisition among investors. Drawing on six established learning and behavioral theories—we analyze data from 158,169 active account holders in Japan through ordinary least squares regression. The results show that higher levels of DFL are associated with being male, younger or middle-aged, highly educated, and unemployed and having greater household income and assets. In contrast, being married, having children, holding a myopic view of the future, and high risk aversion are linked to lower DFL. Interaction effects show a stronger income–DFL association for males and a diminishing return for reduced education with age. Robustness checks using a probit model with a binary DFL measure confirmed the OLS results. These findings highlight digital inequalities and behavioral barriers that shape DFL acquisition. This study contributes a validated framework for identifying at-risk groups and supports future interventions to enhance inclusive digital financial capabilities in increasingly digital economies. Full article

12 pages, 1125 KB

Open AccessArticle

Algorithmic Trading System with Adaptive State Model of a Binary-Temporal Representation

by Michal Dominik Stasiak

Risks 2025, 13(8), 148; https://doi.org/10.3390/risks13080148 - 4 Aug 2025

Viewed by 457

Abstract

In this paper a new state model is introduced, an adaptative state model in a binary temporal representation (ASMBRT) as well as its application in constructing an algorithmic trading system. The presented model uses the binary temporal representation, which allows for a precise [...] Read more.

In this paper a new state model is introduced, an adaptative state model in a binary temporal representation (ASMBRT) as well as its application in constructing an algorithmic trading system. The presented model uses the binary temporal representation, which allows for a precise analysis of exchange rates without losing any informative value of the data. The basis of the model is the trajectory analysis for the ensuing changes in price quotations and dependencies between the duration of each change. The main advantage of the model is to eliminate the threshold analysis, used in existing state models. This solution allows for a more accurate identification of investor behavior patterns, which translates into a reduction of investment risk. In order to verify obtained results in practice, the paper presents a concept of creating an algorithmic trading system and an analysis of its financial effectiveness for the exchange rate most popular among investors, namely EUR/USD. Full article

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

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26 pages, 20835 KB

Open AccessFeature PaperArticle

Reverse Mortgages and Pension Sustainability: An Agent-Based and Actuarial Approach

by Francesco Rania

Risks 2025, 13(8), 147; https://doi.org/10.3390/risks13080147 - 4 Aug 2025

Viewed by 511

Abstract

Population aging poses significant challenges to the sustainability of pension systems. This study presents an integrated methodological approach that uniquely combines actuarial life-cycle modeling with agent-based simulation to assess the potential of Reverse Mortgage Loans (RMLs) as a dual lever for enhancing retiree [...] Read more.

Population aging poses significant challenges to the sustainability of pension systems. This study presents an integrated methodological approach that uniquely combines actuarial life-cycle modeling with agent-based simulation to assess the potential of Reverse Mortgage Loans (RMLs) as a dual lever for enhancing retiree welfare and supporting pension system resilience under demographic and financial uncertainty. We explore Reverse Mortgage Loans (RMLs) as a potential financial instrument to support retirees while alleviating pressure on public pensions. Unlike prior research that treats individual decisions or policy outcomes in isolation, our hybrid model explicitly captures feedback loops between household-level behavior and system-wide financial stability. To test our hypothesis that RMLs can improve individual consumption outcomes and bolster systemic solvency, we develop a hybrid model combining actuarial techniques and agent-based simulations, incorporating stochastic housing prices, longevity risk, regulatory capital requirements, and demographic shifts. This dual-framework enables a structured investigation of how micro-level financial decisions propagate through market dynamics, influencing solvency, pricing, and adoption trends. Our central hypothesis is that reverse mortgages, when actuarially calibrated and macroprudentially regulated, enhance individual financial well-being while preserving long-run solvency at the system level. Simulation results indicate that RMLs can improve consumption smoothing, raise expected utility for retirees, and contribute to long-term fiscal sustainability. Moreover, we introduce a dynamic regulatory mechanism that adjusts capital buffers based on evolving market and demographic conditions, enhancing system resilience. Our simulation design supports multi-scenario testing of financial robustness and policy outcomes, providing a transparent tool for stress-testing RML adoption at scale. These findings suggest that, when well-regulated, RMLs can serve as a viable supplement to traditional retirement financing. Rather than offering prescriptive guidance, this framework provides insights to policymakers, financial institutions, and regulators seeking to integrate RMLs into broader pension strategies. Full article

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

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17 pages, 2439 KB

Open AccessArticle

Monte Carlo-Based VaR Estimation and Backtesting Under Basel III

by Yueming Cheng

Risks 2025, 13(8), 146; https://doi.org/10.3390/risks13080146 - 1 Aug 2025

Viewed by 737

Abstract

Value-at-Risk (VaR) is a key metric widely applied in market risk assessment and regulatory compliance under the Basel III framework. This study compares two Monte Carlo-based VaR models using publicly available equity data: a return-based model calibrated to historical portfolio volatility, and a [...] Read more.

Value-at-Risk (VaR) is a key metric widely applied in market risk assessment and regulatory compliance under the Basel III framework. This study compares two Monte Carlo-based VaR models using publicly available equity data: a return-based model calibrated to historical portfolio volatility, and a CAPM-style factor-based model that simulates risk via systematic factor exposures. The two models are applied to a technology-sector portfolio and evaluated under historical and rolling backtesting frameworks. Under the Basel III backtesting framework, both initially fall into the red zone, with 13 VaR violations. With rolling-window estimation, the return-based model shows modest improvement but remains in the red zone (11 exceptions), while the factor-based model reduces exceptions to eight, placing it into the yellow zone. These results demonstrate the advantages of incorporating factor structures for more stable exception behavior and improved regulatory performance. The proposed framework, fully transparent and reproducible, offers practical relevance for internal validation, educational use, and model benchmarking. Full article

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20 pages, 1104 KB

Open AccessArticle

Smile-Consistent Spread Skew

by Dan Pirjol

Risks 2025, 13(8), 145; https://doi.org/10.3390/risks13080145 - 31 Jul 2025

Viewed by 330

Abstract

We study the shape of the Bachelier-implied volatility of a spread option on two assets following correlated local volatility models. This includes the limiting case of spread options on two correlated Black–Scholes (BS) assets. We give an analytical result for the at-the-money (ATM) [...] Read more.

We study the shape of the Bachelier-implied volatility of a spread option on two assets following correlated local volatility models. This includes the limiting case of spread options on two correlated Black–Scholes (BS) assets. We give an analytical result for the at-the-money (ATM) skew of the spread-implied volatility, which depends only on the components’ ATM volatilities and skews. We also compute the ATM convexity of the implied spread option for the case when the assets follow correlated BS models. The results are extracted from the short-maturity asymptotics for basket options obtained previously by Avellaneda, Boyer-Olson, Busca and Friz and, thus, become exact in the short-maturity limit. Numerical testing of the short-maturity analytical results under the Black–Scholes model and in a local volatility model show good agreement for strikes sufficiently close to the ATM point. Numerical experiments suggest that a linear approximation for the spread Bachelier volatility constructed from the ATM spread volatility and skew gives a good approximation for the spread volatility for highly correlated assets. Full article

(This article belongs to the Special Issue Financial Derivatives and Their Applications)

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27 pages, 525 KB

Open AccessArticle

An Analytical Review of Cyber Risk Management by Insurance Companies: A Mathematical Perspective

by Maria Carannante and Alessandro Mazzoccoli

Risks 2025, 13(8), 144; https://doi.org/10.3390/risks13080144 - 31 Jul 2025

Viewed by 602

Abstract

This article provides an overview of the current state-of-the-art in cyber risk and cyber risk management, focusing on the mathematical models that have been created to help with risk quantification and insurance pricing. We discuss the main ways that cyber risk is measured, [...] Read more.

This article provides an overview of the current state-of-the-art in cyber risk and cyber risk management, focusing on the mathematical models that have been created to help with risk quantification and insurance pricing. We discuss the main ways that cyber risk is measured, starting with vulnerability functions that show how systems react to threats and going all the way up to more complex stochastic and dynamic models that show how cyber attacks change over time. Next, we examine cyber insurance, including the structure and main features of the cyber insurance market, as well as the growing role of cyber reinsurance in strategies for transferring risk. Finally, we review the mathematical models that have been proposed in the literature for setting the prices of cyber insurance premiums and structuring reinsurance contracts, analysing their advantages, limitations, and potential applications for more effective risk management. The aim of this article is to provide researchers and professionals with a clear picture of the main quantitative tools available and to point out areas that need further research by summarising these contributions. Full article

16 pages, 899 KB

Open AccessArticle

Public Funding, ESG Strategies, and the Risk of Greenwashing: Evidence from Greek Financial and Public Institutions

by Kyriaki Efthalitsidou, Vasileios Kanavas, Paschalis Kagias and Nikolaos Sariannidis

Risks 2025, 13(8), 143; https://doi.org/10.3390/risks13080143 - 29 Jul 2025

Viewed by 528

Abstract

The increasing pressure for environmental, social, and governance (ESG) accountability in publicly funded institutions has raised concerns about the authenticity and efficiency of ESG implementation. This study investigates the relationship between public ESG funding, disclosure quality, and organizational efficiency across Greek public and [...] Read more.

The increasing pressure for environmental, social, and governance (ESG) accountability in publicly funded institutions has raised concerns about the authenticity and efficiency of ESG implementation. This study investigates the relationship between public ESG funding, disclosure quality, and organizational efficiency across Greek public and financial entities. Using a mixed-methods approach—data envelopment analysis (DEA), qualitative ESG content scoring, and bibliometric mapping—we reveal that symbolic compliance remains prevalent, often decoupled from actual sustainability outcomes. Our DEA findings show that technical efficiency is strongly associated with reporting clarity, the use of verifiable metrics, and governance integration, rather than the mere volume of funding. The qualitative analysis further confirms that many disclosures reflect reputational signaling rather than impact-oriented transparency. Bibliometric results highlight a systemic underrepresentation of the public sector in ESG scholarship, particularly in Southern Europe, underscoring the need for regionally grounded empirical studies. This study provides practical implications for improving ESG accountability in publicly funded institutions and contributes a novel approach that integrates efficiency, content, and bibliometric analysis in the ESG context. Full article

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

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14 pages, 379 KB

Open AccessArticle

Overconfidence and Investment Loss Tolerance: A Large-Scale Survey Analysis of Japanese Investors

by Honoka Nabeshima, Mostafa Saidur Rahim Khan and Yoshihiko Kadoya

Risks 2025, 13(8), 142; https://doi.org/10.3390/risks13080142 - 23 Jul 2025

Viewed by 783

Abstract

Accepting a certain degree of investment loss risk is essential for long-term portfolio management. However, overconfidence bias within financial literacy can prompt excessively risky behavior and amplify susceptibility to other cognitive biases. These tendencies can undermine investment loss tolerance beyond the baseline level [...] Read more.

Accepting a certain degree of investment loss risk is essential for long-term portfolio management. However, overconfidence bias within financial literacy can prompt excessively risky behavior and amplify susceptibility to other cognitive biases. These tendencies can undermine investment loss tolerance beyond the baseline level shaped by sociodemographic, economic, psychological, and cultural factors. This study empirically examines the association between overconfidence and investment loss tolerance, which is measured by the point at which respondents indicate they would sell their investments in a hypothetical loss scenario. Using a large-scale dataset of 161,765 active investors from one of Japan’s largest online securities firms, we conduct ordered probit and ordered logit regression analyses, controlling for a range of sociodemographic, economic, and psychological variables. Our findings reveal that overconfidence is statistically significantly and negatively associated with investment loss tolerance, indicating that overconfident investors are more prone to prematurely liquidating assets during market downturns. This behavior reflects an impulse to avoid even modest losses. The findings suggest several possible practical strategies to mitigate the detrimental effects of overconfidence on long-term investment behavior. Full article

17 pages, 1363 KB

Open AccessArticle

Navigating Risk in Crypto Markets: Connectedness and Strategic Allocation

by Nader Naifar

Risks 2025, 13(8), 141; https://doi.org/10.3390/risks13080141 - 23 Jul 2025

Viewed by 1417

Abstract

This study examined the dynamic interconnectedness and portfolio implications within the cryptocurrency ecosystem, focusing on five representative digital assets across the core functional categories: Layer 1 cryptocurrencies (Bitcoin (BTC) and Ethereum (ETH)), decentralized finance (Uniswap (UNI)), stablecoins (Dai), and crypto infrastructure tokens (Maker [...] Read more.

This study examined the dynamic interconnectedness and portfolio implications within the cryptocurrency ecosystem, focusing on five representative digital assets across the core functional categories: Layer 1 cryptocurrencies (Bitcoin (BTC) and Ethereum (ETH)), decentralized finance (Uniswap (UNI)), stablecoins (Dai), and crypto infrastructure tokens (Maker (MKR)). Using the Extended Joint Connectedness Approach within a Time-Varying Parameter VAR framework, the analysis captured time-varying spillovers of return shocks and revealed a heterogeneous structure of systemic roles. Stablecoins consistently acted as net absorbers of shocks, reinforcing their defensive profile, while governance tokens, such as MKR, emerged as persistent net transmitters of systemic risk. Foundational assets like BTC and ETH predominantly absorbed shocks, contrary to their perceived dominance. These systemic roles were further translated into portfolio design, where connectedness-aware strategies, particularly the Minimum Connectedness Portfolio, demonstrated superior performance relative to traditional variance-based allocations, delivering enhanced risk-adjusted returns and resilience during stress periods. By linking return-based systemic interdependencies with practical asset allocation, the study offers a unified framework for understanding and managing crypto network risk. The findings carry practical relevance for portfolio managers, algorithmic strategy developers, and policymakers concerned with financial stability in digital asset markets. Full article

(This article belongs to the Special Issue Cryptocurrency Pricing and Trading)

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Risks, Volume 13, Issue 8 (August 2025) – 19 articles

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