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Risks
Volume 13
Issue 11
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Risks, Volume 13, Issue 11 (November 2025) – 21 articles

Cover Story (view full-size image): This paper introduces a new hybrid model, HAR-RV-CARMA, which combines the Heterogeneous Autoregressive model for Realized Volatility (HAR-RV) with the Continuous Autoregressive Moving Average (CARMA) model. The key innovation of this study lies in the use of a Kalman filter-based dynamic state weighting mechanism to optimally combine the predictive capabilities of both models while mitigating overfitting. The proposed model is applied to five major Covered Call Exchange-Traded Funds (ETFs), QYLD, XYLD, RYLD, JEPI, and JEPQ, utilizing daily realized volatility data from 2019 to 2024. View this paper
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19 pages, 1317 KB  
Article
Metaheuristics for Portfolio Optimization: Application of NSGAII, SPEA2, and PSO Algorithms
by Ameni Ben Hadj Abdallah, Rihab Bedoui and Heni Boubaker
Risks 2025, 13(11), 227; https://doi.org/10.3390/risks13110227 - 19 Nov 2025
Viewed by 420
Abstract
This work looks for the optimal allocation of different assets, namely, the G7 stock indices, commodities (gold and WTI crude oil), cryptocurrencies (Bitcoin and Ripple), and S&P Green Bond, over four periods: before the COVID-19 crisis, during the COVID-19 crisis and before the [...] Read more.
This work looks for the optimal allocation of different assets, namely, the G7 stock indices, commodities (gold and WTI crude oil), cryptocurrencies (Bitcoin and Ripple), and S&P Green Bond, over four periods: before the COVID-19 crisis, during the COVID-19 crisis and before the Russia–Ukraine war, during the COVID-19 crisis and Russia–Ukraine war, and after the COVID-19 pandemic and during the Russia–Ukraine war. Metaheuristics, Non-dominated Sorting Genetic Algorithm (NSGAII), Strength Pareto Evolutionary Algorithm (SPEA2), and Particle Swarm Optimization (PSO) are applied to find the best allocation. The results reveal that there a significant preference for the S&P Green Bond during the four periods of study according to three algorithms, thanks to its portfolio diversification abilities. During the COVID-19 pandemic and the geopolitical crisis, the most optimal portfolio was Nikkei 225 because of its quick recovery from the pandemic and poor reliance on the Russia–Ukraine markets, while WTI crude oil and both dirty and clean cryptocurrencies were poor contributors to the investment portfolio because these assets are sensitive to geopolitical problems. After the end of the pandemic and during the ongoing Russia–Ukraine war, the three algorithms obtained remarkably different results: the NSGAII portfolio was invested in various assets, 32% of the SPEA2 portfolio was allocated to the S&P Green Bond, and half of the PSO portfolio was allocated to the S&P Green Bond too. This may be due to changes in investors’ preferences to protect their fortune and to diversify their portfolio during the war. From a risk-averse perspective, NSGAII does not underestimate the risk, while in terms of forecasting accuracy, PSO is an adequate algorithm. In terms of time, NSGAII is the fastest algorithm, while SPEA2 requires more time than the NSGAII and PSO algorithms. Our results have important implications for both investors and risk managers in terms of portfolio and risk management decisions, and they highlight the factors that influence investment choices during health and geopolitical crises. Full article
(This article belongs to the Special Issue Artificial Intelligence Risk Management)
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31 pages, 636 KB  
Article
A Quantitative Analysis of Sustainable Finance Preferences: Choice Patterns, Personality Traits and Gender in SDG 7 Investments
by Carlos Díaz-Caro, Francisco-Javier Fragoso Martínez, Eva Crespo-Cebada and Ángel-Sabino Mirón Sanguino
Risks 2025, 13(11), 226; https://doi.org/10.3390/risks13110226 - 18 Nov 2025
Viewed by 685
Abstract
The analysis carried out in this work shows that sustainable investment decisions aimed at SDG 7 are mainly driven by objective financial attributes, especially the level of risk and the type of providing institution. The empirical analysis is based on 873 valid responses, [...] Read more.
The analysis carried out in this work shows that sustainable investment decisions aimed at SDG 7 are mainly driven by objective financial attributes, especially the level of risk and the type of providing institution. The empirical analysis is based on 873 valid responses, balanced by gender and income levels, which enables us to capture heterogeneity in sustainable investment preferences. This study contributes to the literature by jointly examining personality traits and gender as explanatory factors of willingness to pay for investments aligned with SDG 7. In the general model, strong risk aversion—particularly to high risk—and a positive valuation of cooperatives stand out over factors such as explicit reference to SDG 7 or personality traits, which are not significant. Gender segmentation reveals substantial differences: women display a much higher risk aversion and a greater willingness to pay for investing in cooperatives and, to a lesser extent, in sustainable institutions; in this group, extraversion is negatively associated with the choice of SDG 7 funds. For men, risk remains key but with lower penalization, and provider type carries more moderate weight; no relevant link with personality traits is detected. Thus, the gender effect hypothesis is fully confirmed, while the personality hypothesis is partially supported. These results suggest that the design of sustainable financial products should be a WTP adapted to differentiate demographic and behavioral profiles in order to mobilize private capital toward the energy transition. Full article
25 pages, 2075 KB  
Article
Structural Changes in Persistence of Mortality
by Wanying Fu, Barry R. Smith and Patrick Brewer
Risks 2025, 13(11), 225; https://doi.org/10.3390/risks13110225 - 18 Nov 2025
Viewed by 295
Abstract
Recent researchers have observed that long-memory is prevalent in mortality data. Related to a quantifiable measure of persistence, it is an important characteristic of mortality dynamics. However, prior researchers did not consider potential change in the persistence degree and assumed it is constant. [...] Read more.
Recent researchers have observed that long-memory is prevalent in mortality data. Related to a quantifiable measure of persistence, it is an important characteristic of mortality dynamics. However, prior researchers did not consider potential change in the persistence degree and assumed it is constant. This article for the first time considers change in the persistence of mortality and demonstrates that mortality data displays obvious and substantial such changes. We apply a test of Martins and Rodrigues, a tool that has already been demonstrated to be effective in macroeconomics research, to detect the change in persistence in mortality time series for the first time. Our approach considers changes both in persistence and also in trend, separately, for each single-age mortality time series. Our results show that these two types of structural changes are very different in the aspects of age clustering and the time points of breaks. In experiments on simulated data, our model presents the best accuracy in the estimation of persistence degree compared to two control models. Full article
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16 pages, 460 KB  
Article
Estimating Corporate Bond Market Volatility Using Asymmetric GARCH Models
by Elroi Hadad, Amit Malka Fridman and Rami Yosef
Risks 2025, 13(11), 224; https://doi.org/10.3390/risks13110224 - 10 Nov 2025
Viewed by 1257
Abstract
This study investigates the volatility of the Israeli corporate bond market, where corporate bonds are traded on a Limit Order Book (LOB) exchange with high retail trading activity. Using data from the Tel-Bond 20 and Tel-Bond 60 indices, we estimate various asymmetric GARCH [...] Read more.
This study investigates the volatility of the Israeli corporate bond market, where corporate bonds are traded on a Limit Order Book (LOB) exchange with high retail trading activity. Using data from the Tel-Bond 20 and Tel-Bond 60 indices, we estimate various asymmetric GARCH models to capture the dynamics of bond returns. Our findings highlight a leverage effect, where negative shocks have a more significant impact on volatility than positive shocks, underscoring the importance of investor sentiment. The GJR model with a Student’s t-distribution best captures serial correlation, persistence of conditional volatility, and asymmetric volatility clustering. These results have significant implications for risk management, portfolio allocation, and regulatory policies, emphasizing the need for robust volatility forecasting models in transparent and active corporate bond markets. Full article
(This article belongs to the Special Issue Volatility Modeling in Financial Market)
16 pages, 1252 KB  
Article
HAR-RV-CARMA: A Kalman Filter-Weighted Hybrid Model for Enhanced Volatility Forecasting
by Chigozie Andy Ngwaba
Risks 2025, 13(11), 223; https://doi.org/10.3390/risks13110223 - 6 Nov 2025
Viewed by 1023
Abstract
This paper introduces a new hybrid model, HAR-RV-CARMA, which combines the Heterogeneous Autoregressive model for Realized Volatility (HAR-RV) with the Continuous Autoregressive Moving Average (CARMA) model. The key innovation of this study lies in the use of a Kalman filter-based dynamic state weighting [...] Read more.
This paper introduces a new hybrid model, HAR-RV-CARMA, which combines the Heterogeneous Autoregressive model for Realized Volatility (HAR-RV) with the Continuous Autoregressive Moving Average (CARMA) model. The key innovation of this study lies in the use of a Kalman filter-based dynamic state weighting mechanism to optimally combine the predictive capabilities of both models while mitigating overfitting. The proposed model is applied to five major Covered Call Exchange-Traded Funds (ETFs), QYLD, XYLD, RYLD, JEPI, and JEPQ, utilizing daily realized volatility data from 2019 to 2024. Model performance is evaluated against standalone HAR-RV and CARMA models using metrics such as Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), Quasi-Likelihood (QLIKE), Akaike Information Criterion (AIC), and Bayesian Information Criterion (BIC). Additionally, the study assesses directional accuracy and conducts a Diebold-Mariano test to compare forecast performance against the standalone models statistically. Empirical results suggest that the HAR-RV-CARMA hybrid model significantly outperforms both HAR-RV and CARMA in volatility forecasting across all evaluation criteria. It achieves lower forecast errors, superior goodness-of-fit, and higher directional accuracy, with Diebold-Mariano test outcomes rejecting the null hypothesis of equal predictive ability at significant levels. These findings highlight the effectiveness of dynamic model weighting in improving predictive accuracy and offer a strong framework for volatility modeling in financial markets. Full article
(This article belongs to the Special Issue Risk Management in Financial and Commodity Markets)
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26 pages, 7986 KB  
Article
Volatility Spillovers and Market Decoupling: Evidence from BRICS and China’s Green Sector
by Darko B. Vuković, Dmitrii Leonidovich Fefelov, Michael Frömmel and Elena Moiseevna Rogova
Risks 2025, 13(11), 222; https://doi.org/10.3390/risks13110222 - 6 Nov 2025
Viewed by 925
Abstract
The global economic importance of green tech is rising. Yet the role of the green financial sector in the propagation of volatility is still unclear. Although the existing literature often characterizes green assets as stable, the new risks, particularly US–China trade tensions that [...] Read more.
The global economic importance of green tech is rising. Yet the role of the green financial sector in the propagation of volatility is still unclear. Although the existing literature often characterizes green assets as stable, the new risks, particularly US–China trade tensions that target the green sector directly, may uncover potential vulnerabilities. As China’s green sector has attained global leadership, its interconnections with other major economies require a closer examination, especially within the BRICS block. Applying the Bayesian VAR with Minnesota Ridge prior and a TVP-VAR model-based connectedness approach on a dataset of 1880 observations spanning from 2016 to 2025, we identified that volatility in China’s green sector peaked during the COVID-19 pandemic and resurged in early 2025 amid trade tensions. Uniquely, this study also finds that, despite the intensification of political and economic relations between BRICS members, the interconnectedness of their financial markets has been weakening, suggesting their long-term decoupling and regionalization. From 2016 to 2024, green indices remained historically peripheral, with limited, stable ties to the Nasdaq and SSE. In 2025, short shock-driven transmitter episodes have emerged and indicate an incipient integration rather than a permanent regime change. Full article
(This article belongs to the Special Issue Applied Econometrics and International Finance: Analysis, Modeling, and Development)
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17 pages, 1265 KB  
Article
The Business Cycle’s Impact on Volatility Forecasting: Recapturing Intrinsic Jump Components
by Son-Nan Chen and Pao-Peng Hsu
Risks 2025, 13(11), 221; https://doi.org/10.3390/risks13110221 - 5 Nov 2025
Viewed by 487
Abstract
This study investigates the leverage effect and realized volatility (RV) of stocks in the presence of asymmetric jumps across economic expansions and contractions. We extend the heterogeneous autoregressive-realized volatility (HAR-RV) model by incorporating a two-period Markov regime-switching model to capture Taiwan’s economic expansion [...] Read more.
This study investigates the leverage effect and realized volatility (RV) of stocks in the presence of asymmetric jumps across economic expansions and contractions. We extend the heterogeneous autoregressive-realized volatility (HAR-RV) model by incorporating a two-period Markov regime-switching model to capture Taiwan’s economic expansion and contraction. Using Taiwan’s COVID-19 insurance-oversold events as a case–control setting, we compare the asymmetric jump risk effects on RV and realized semivariance (RSV). The results reveal that business cycle (BC) effects offset jump risk impacts, rendering intrinsic jump components statistically insignificant when BC information is omitted. During contraction periods, asymmetric jumps generate stronger negative RSV shocks, amplifying the leverage effect. Moreover, the predictive accuracy of RV critically depends on the prevailing business cycle state. By incorporating BC effects into the model, we recapture significant jump components and enhance volatility forecasting performance. Full article
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17 pages, 591 KB  
Article
Extending Approximate Bayesian Computation to Non-Linear Regression Models: The Case of Composite Distributions
by Mostafa S. Aminzadeh and Min Deng
Risks 2025, 13(11), 220; https://doi.org/10.3390/risks13110220 - 5 Nov 2025
Viewed by 360
Abstract
Modeling loss data is a crucial aspect of actuarial science. In the insurance industry, small claims occur frequently, while large claims are rare. Traditional heavy-tail distributions, such as Weibull, Log-Normal, and Inverse Gaussian distributions, are not suitable for describing insurance data, which often [...] Read more.
Modeling loss data is a crucial aspect of actuarial science. In the insurance industry, small claims occur frequently, while large claims are rare. Traditional heavy-tail distributions, such as Weibull, Log-Normal, and Inverse Gaussian distributions, are not suitable for describing insurance data, which often exhibit skewness and fat tails. The literature has explored classical and Bayesian inference methods for the parameters of composite distributions, such as the Exponential–Pareto, Weibull–Pareto, and Inverse Gamma–Pareto distributions. These models effectively separate small to moderate losses from significant losses using a threshold parameter. This research aims to introduce a new composite distribution, the Gamma–Pareto distribution with two parameters, and employ a numerical computational approach to find the maximum likelihood estimates (MLEs) of its parameters. A novel computational approach for a nonlinear regression model where the loss variable is distributed as the Gamma–Pareto and depends on multiple covariates is proposed. The maximum likelihood (ML) and Approximate Bayesian Computation (ABC) methods are used to estimate the regression parameters. The Fisher information matrix, along with a multivariate normal distribution as the prior distribution, is utilized through the ABC method. Simulation studies indicate that the ABC method outperforms the ML method in terms of accuracy. Full article
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39 pages, 877 KB  
Article
Determinants of Internal Control System Effectiveness: Evidence from Greek Listed Companies
by Vasileios Giannopoulos, Antonios Lymperopoulos, Spyridon Kariofyllas and Charalampos Kariofyllas
Risks 2025, 13(11), 219; https://doi.org/10.3390/risks13110219 - 5 Nov 2025
Viewed by 1499
Abstract
This paper examines the interrelationship between Corporate Governance (CG), Internal Control System (ICS), and Organizational Performance (OP), with a particular focus on the effectiveness of the ICS in relation to the quality of its components. Drawing on recent literature and empirical evidence, the [...] Read more.
This paper examines the interrelationship between Corporate Governance (CG), Internal Control System (ICS), and Organizational Performance (OP), with a particular focus on the effectiveness of the ICS in relation to the quality of its components. Drawing on recent literature and empirical evidence, the study demonstrates that strong governance frameworks—characterized by board independence, effective audit committees, and proactive risk management—are closely linked to robust internal control environments. Together, these mechanisms enhance transparency, reduce risks, and foster stakeholder trust. The analysis further highlights that governance and internal control are evolving beyond compliance, increasingly serving as strategic levers for creating sustainable value. The findings underscore important implications for practitioners and policymakers. Organizations are encouraged to strengthen internal controls, invest in audit and risk management capacity, and embed ethical and sustainability considerations into governance structures. Regulators, in turn, should support frameworks that promote both accountability and innovation. Overall, the study contributes to a deeper understanding of how governance and control mechanisms can secure organizational resilience and drive long-term performance in a rapidly changing business environment. Full article
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13 pages, 661 KB  
Article
The Asymmetric Effects of Geopolitical Risks on Vietnam’s Exports
by Loc Dong Truong, Ngoc Thao Nguyen and Dung Tri Nguyen
Risks 2025, 13(11), 218; https://doi.org/10.3390/risks13110218 - 4 Nov 2025
Viewed by 706
Abstract
This study is devoted to investigating the asymmetric effects of geopolitical risks (GPRs) on Vietnam’ exports during the period from January 2010 to December 2024. Using a nonlinear Autoregressive Distributed Lag (NARDL) bounds testing model, the study documented that in the short-run, GPRs [...] Read more.
This study is devoted to investigating the asymmetric effects of geopolitical risks (GPRs) on Vietnam’ exports during the period from January 2010 to December 2024. Using a nonlinear Autoregressive Distributed Lag (NARDL) bounds testing model, the study documented that in the short-run, GPRs have asymmetric effects on Vietnam’s exports. Specifically, negative changes in GPRs have a significantly negative influence on the exports while positive changes in the GPRs have no significant effects on exports. In the long-run, the same effects of GPRs on exports are also found from the NARDL model. Specifically, negative changes in GPRs have a significantly adverse effect on exports, while positive changes in GPRs have no significant influence on exports in the long-run. Moreover, the empirical findings reveal that, in the long-run, the real exchange rate (RER) has a significantly positive impact on exports, suggesting that the depreciation of the VND (Vietnamese Dong) boosts Vietnam’s exports. Finally, the findings obtained from the error correction model show that 34.82 percent of the divergence from the long-run equilibrium caused by a shock in month n will be corrected and adjusted back toward equilibrium in month n + 1. Full article
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28 pages, 4723 KB  
Article
Global Uncertainty and BRICS+ Equity Markets: Spillovers from VIX, Geopolitical Risk, and U.S. Macro-Financial Shocks
by Chourouk Kasraoui, Amal Khmiri, Catalin Gheorghe and Ahmed Jeribi
Risks 2025, 13(11), 217; https://doi.org/10.3390/risks13110217 - 4 Nov 2025
Viewed by 1119
Abstract
This paper investigates how global uncertainty and macro-financial shocks transmitted to BRICS+ equity markets between April 2016 and July 2025. A vector autoregressive (VAR) framework, complemented by Granger-causality tests, variance decompositions, and impulse response functions, is employed to examine four key drivers: U.S. [...] Read more.
This paper investigates how global uncertainty and macro-financial shocks transmitted to BRICS+ equity markets between April 2016 and July 2025. A vector autoregressive (VAR) framework, complemented by Granger-causality tests, variance decompositions, and impulse response functions, is employed to examine four key drivers: U.S. financial market volatility (VIX), geopolitical risk (GPRD), U.S. inflation expectations (T5YIE), and the U.S. term spread (T10Y3M). The findings show that the VIX functions both as a recipient and a transmitter of shocks, amplifying volatility across BRICS+ markets, with India, Brazil, and the Gulf states acting as important nodes in the global contagion network. By contrast, geopolitical risk shocks have only short-lived effects on both U.S. yields and emerging equity markets. Shocks to U.S. inflation expectations and yield-curve dynamics transmit quickly to BRICS+ markets but dissipate within a few days, underscoring efficient market adjustment. Overall, the evidence points to a multipolar structure of global contagion in which BRICS+ markets exert growing influence alongside the United States. These results offer important implications for risk management, portfolio diversification, and policy coordination under heightened uncertainty. Full article
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20 pages, 404 KB  
Article
Who Responds to Estate Recovery? Survey Evidence from Switzerland on Long-Term Care Insurance and Informal Care Decisions
by Laura Iveth Aburto Barrera, Christophe Courbage and Joël Wagner
Risks 2025, 13(11), 216; https://doi.org/10.3390/risks13110216 - 4 Nov 2025
Viewed by 345
Abstract
Estate recovery is a policy whereby the state recovers public long-term care (LTC) benefits from the estates of deceased beneficiaries. Using data from a Swiss survey, this study examines which individuals are most responsive to estate recovery in their decisions to purchase LTC [...] Read more.
Estate recovery is a policy whereby the state recovers public long-term care (LTC) benefits from the estates of deceased beneficiaries. Using data from a Swiss survey, this study examines which individuals are most responsive to estate recovery in their decisions to purchase LTC insurance and provide informal care. Generalized linear models with a binomial logit specification assess the role of bequest motives, concern about future dependence, beliefs about financial responsibility, and demographic factors. Results show the important role of the bequest motive in shaping the impact of estate recovery on informal care decisions, but not on LTC insurance decisions. In contrast, concerns about future dependence influence both types of decisions. Younger individuals and those who believe that financing LTC is a citizen’s responsibility are more sensitive to estate recovery when purchasing LTC insurance. Conversely, individuals who feel legally obligated to help their dependent parents or who provide assistance due to high professional care costs are more likely to report estate recovery as a relevant factor in caregiving decisions. These findings provide valuable information for more targeted LTC policy design by identifying individuals most responsive to estate recovery in their decisions to purchase LTC insurance and provide informal care. Full article
20 pages, 526 KB  
Article
Chain Ladder Under Aggregation of Calendar Periods
by Greg Taylor
Risks 2025, 13(11), 215; https://doi.org/10.3390/risks13110215 - 3 Nov 2025
Viewed by 239
Abstract
The chain ladder model is defined by a set of assumptions about the claim array to which it is applied. It is, in practice, applied to claim arrays whose data relate to different frequencies, e.g., yearly, quarterly, monthly, weekly, etc. There is sometimes [...] Read more.
The chain ladder model is defined by a set of assumptions about the claim array to which it is applied. It is, in practice, applied to claim arrays whose data relate to different frequencies, e.g., yearly, quarterly, monthly, weekly, etc. There is sometimes a tacit assumption that one can shift between these frequencies at will, and that the model will remain applicable. It is not obvious that this is the case. One needs to check whether a model whose assumptions hold for annual data will continue to hold for a quarterly (for example) representation of the same data. The present paper studies this question in the case of preservation of calendar periods, i.e., (in the example) annual calendar periods are dissected into quarters. The study covers the two most common forms of chain ladder model, namely the Tweedie chain ladder and Mack chain ladder. The conclusion is broadly, if not absolutely, negative. Certain parameter sets can indeed be found for which the chain ladder structure is maintained under a change in data frequency. However, while it may be technically possible to maintain the chain ladder model under such a change to the data, it is not possible in any reasonable, practical sense. Full article
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30 pages, 1354 KB  
Article
Driving Behavior and Insurance Pricing: A Framework for Analysis and Some Evidence from Italian Data Using Zero-Inflated Poisson (ZIP) Models
by Paola Fersini, Michele Longo and Giuseppe Melisi
Risks 2025, 13(11), 214; https://doi.org/10.3390/risks13110214 - 3 Nov 2025
Viewed by 1054
Abstract
Usage-Based Insurance (UBI), also referred to as telematics-based insurance, has been experiencing a growing global diffusion. In addition to being well established in countries such as Italy, the United States, and the United Kingdom, UBI adoption is also accelerating in emerging markets such [...] Read more.
Usage-Based Insurance (UBI), also referred to as telematics-based insurance, has been experiencing a growing global diffusion. In addition to being well established in countries such as Italy, the United States, and the United Kingdom, UBI adoption is also accelerating in emerging markets such as Japan, South Africa, and Brazil. In Japan, telematics insurance has shown significant growth in recent years, with a steadily increasing subscription rate. In South Africa, UBI adoption ranks among the highest worldwide, with market penetration placing the country among the top three globally, just after the United States and Italy. In Brazil, UBI adoption is expanding, supported by government initiatives promoting road safety and innovation in the insurance sector. According to a MarketsandMarkets report of February 2025, the global UBI market is expected to grow from USD 43.38 billion in 2023 to USD 70.46 billion by 2030, with a compound annual growth rate (CAGR) of 7.2% over the forecast period. This growth is driven by the increasing adoption of both electric and internal combustion vehicles equipped with integrated telematics systems, which enable insurers to collect data on driving behavior and to tailor insurance premiums accordingly. In this paper, we analyze a large dataset consisting of trips recorded over five years from 100,000 policyholders across the Italian territory through the installation of black-box devices. Using univariate and multivariate statistical analyses, as well as Generalized Linear Models (GLMs) with Zero-Inflated Poisson distribution, we examine claims frequency and assess the relevance of various synthetic indicators of driving behavior, with the aim of identifying those that are most significant for insurance pricing. Full article
(This article belongs to the Special Issue Innovations in Non-Life Insurance Pricing and Reserving)
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36 pages, 1661 KB  
Article
Nature Finance: Bridging Natural and Financial Capital Through Robust Impact Measurement
by Friedrich Sayn-Wittgenstein, Frederic de Mariz and Christina Leijonhufvud
Risks 2025, 13(11), 213; https://doi.org/10.3390/risks13110213 - 3 Nov 2025
Viewed by 933
Abstract
Global biodiversity decreased by 69% from 1970 to 2022, representing a key risk to economic activity. However, the link between nature, biodiversity and finance has received little attention within the field of sustainable finance. This paper attempts to fill this gap. Nature finance [...] Read more.
Global biodiversity decreased by 69% from 1970 to 2022, representing a key risk to economic activity. However, the link between nature, biodiversity and finance has received little attention within the field of sustainable finance. This paper attempts to fill this gap. Nature finance aims to avoid biodiversity loss and promote nature-positive activities, such as the conservation and protection of biodiversity through market-based solutions with the proper measurement of impact. Measuring biodiversity impact remains a challenge for most companies and banks, with a fragmented landscape of nature frameworks. We conduct a bibliometric analysis of the literature on biodiversity finance and analyze a unique market dataset of five global investment funds as well as all corporate bonds issued in Brazil, the country with the largest biodiversity assets. First, we find that the literature on nature finance is recent with a tipping point in 2020, with the three most common concepts being ecosystem services, nature-based solutions and circular economy. Second, we find that sovereigns and two corporate sectors (food production, pulp & paper) represent the vast majority of issuers that currently incorporate biodiversity considerations into funding structures, suggesting an opportunity to expand accountability for biodiversity impacts across a greater number of sectors. Third, we find a disconnect between science and finance. Out of a catalogue of 158 biodiversity metrics proposed by the IFC, just 33 have been used in bond issuances and 32 by fund managers, suggesting an opportunity for technical assistance for companies and to simplify catalogs to create a common language. Lack of consensus around metrics, complexity, and cost explain this gap. Fourth, we identify a distinction between liquid markets and illiquid markets in their application of biodiversity impact management and measurement. Illiquid markets, such as private equity, bilateral lending, voluntary carbon markets or investment funds can develop complex bespoke mechanisms to measure nature, leveraging detailed catalogues of metrics. Liquid markets, including bonds, exhibit a preference for simpler metrics such as preserved areas or forest cover. Full article
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28 pages, 1343 KB  
Article
Understanding Reverse Mortgage Acceptance in Spain with Explainable Machine Learning and Importance–Performance Map Analysis
by Jorge de Andrés-Sánchez and Laura González-Vila Puchades
Risks 2025, 13(11), 212; https://doi.org/10.3390/risks13110212 - 2 Nov 2025
Viewed by 503
Abstract
In developed countries such as Spain, where the population is increasingly aging, retirement planning and longevity risk represent major societal challenges. In Spain, in particular, a significant proportion of household wealth is concentrated in real estate, primarily in the form of owner-occupied housing. [...] Read more.
In developed countries such as Spain, where the population is increasingly aging, retirement planning and longevity risk represent major societal challenges. In Spain, in particular, a significant proportion of household wealth is concentrated in real estate, primarily in the form of owner-occupied housing. For this reason, one emerging financial product in the retirement savings space is the reverse mortgage (RM). This study examines the determinants of acceptance of this financial product using survey data collected from Spanish individuals. The intention to take out an RM is explained through performance expectancy (PE), effort expectancy (EE), social influence (SI), bequest motive (BM), financial literacy (FL), and risk (RK). The analysis applies machine learning techniques: decision tree regression is used to visualize variable interactions that lead to acceptance; random forest to improve predictive capability; and Shapley Additive Explanations (SHAP) to estimate the relative importance of predictors. Finally, Importance–Performance Map Analysis (IPMA) is employed to identify the variables that merit greater attention in the acceptance of RMs. SHAP values indicate that PE and SI are the most influential predictors of intention to use RMs, followed by BM and EE with moderate importance, whereas the positive influence of RK and FL is more reduced. The IPMA highlights PE and SI as the most strategic drivers, and RK and BM act as relevant barriers to the widespread adoption of RMs. Full article
(This article belongs to the Special Issue Innovations in Annuities and Longevity Risk Management)
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18 pages, 1645 KB  
Article
Impact of Stalled Life Expectancy on Health and Economic Inactivity in the UK and the Case for Prevention
by Leslie D. Mayhew
Risks 2025, 13(11), 211; https://doi.org/10.3390/risks13110211 - 2 Nov 2025
Viewed by 602
Abstract
We use partial life expectancy to show the existence in the UK of an asymmetric relationship between life span and health span in five-year age brackets over the life course. Using comparable data from other advanced economies, we investigate why years of improvement [...] Read more.
We use partial life expectancy to show the existence in the UK of an asymmetric relationship between life span and health span in five-year age brackets over the life course. Using comparable data from other advanced economies, we investigate why years of improvement in life expectancy after 2010 have come to a halt, and what would have happened if austerity and the COVID pandemic had not occurred. We find that the UK does worse than other countries except for the US. We show that deprivation is a major source of disparities between health and life span and is a key contributing factor. A one-year decrease in life expectancy leads to a 2.5-year reduction in health expectancy, resulting in a 21-year disparity between health and life span in the most deprived area. The resultant gap places a considerable burden on public finances and slows economic growth. Impacts include lower economic activity rates, higher healthcare costs, greater immigration, and upward financial pressures on the state pension. The unresolved policy issue is how to slow the current trend, given the rapidly ageing UK population. Full article
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32 pages, 2861 KB  
Article
A Bibliometric Analysis on Network-Based Systemic Risk
by Joan Sebastián Rojas Rincón, Julio César Acosta-Prado and José Ever Castellanos Narciso
Risks 2025, 13(11), 210; https://doi.org/10.3390/risks13110210 - 2 Nov 2025
Viewed by 1421
Abstract
The vulnerability of the global financial system to systemic risk-related adverse events has become more evident in recent years, as shown by the 2008 financial crisis and the global pandemic. This study examines systemic risk and its contributing factors using network analysis to [...] Read more.
The vulnerability of the global financial system to systemic risk-related adverse events has become more evident in recent years, as shown by the 2008 financial crisis and the global pandemic. This study examines systemic risk and its contributing factors using network analysis to understand how contagion occurs. To achieve this, a bibliometric analysis was conducted using a cluster analysis of publications from 2020 to 2025. The bibliometric analysis covered 1642 papers related to systemic risk and financial transmission networks. The CiteSpace software was used to identify seven thematic clusters. The results show the relevance of topological analysis in explaining the connection between institutions and the spread of risk. There is also a clear tradition in the literature of applying the DY spillover index, which captures the temporal dynamics of systemic connectivity. Multilayer networks stand out as a trend in recent studies, as they have the potential to represent different types of relationships simultaneously between nodes. Finally, the literature pays attention to systemic connectivity problems during crises, which can amplify volatility and generate forced asset sales, highlighting the need to use advanced VAR-type models to anticipate risk transmission and guide macroprudential management. Full article
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21 pages, 791 KB  
Article
Negative Emotions and Decision-Making Paralysis Among Individual Investors: A Qualitative Approach
by Alain Finet, Kevin Kristoforidis and Julie Laznicka
Risks 2025, 13(11), 209; https://doi.org/10.3390/risks13110209 - 30 Oct 2025
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Abstract
The association between emotions and decision-making is evident. Our article aims to demonstrate, for individual investors, the development of negative emotional charges on stock markets in a perceived negative trend. The research question concerns how negative emotions may be associated with specific behavioral [...] Read more.
The association between emotions and decision-making is evident. Our article aims to demonstrate, for individual investors, the development of negative emotional charges on stock markets in a perceived negative trend. The research question concerns how negative emotions may be associated with specific behavioral responses. Our results indicate a four-phase process involving, first, decisional “nonchalance”; second, decisional hesitation; third, partial disengagement; and, finally, decisional paralysis. The first phase appears related to the lack of experience of the individual investor, the second phase corresponds to the uncertainty related to stock market operations, while the last two phases seem to coincide with a deteriorating decision-making environment and the accumulation of negative experiences, resulting from financial expectations not being met. Emotional paralysis raises questions about the possibility of individual investors renewing their investment strategies. These results come from a qualitative approach based on experimental finance and supported by the analysis of data from semi-structured interviews. Our study proposes a new four-phase model (nonchalance, hesitation, partial disengagement, and paralysis) that delineates the emotional and behavioral trajectories of individual investors during a perceived bear market. Our qualitative perspective also contributes to existing literature by highlighting the underexplored phase of “nonchalance”. Full article
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24 pages, 4033 KB  
Article
A Novel Federated Transfer Learning Framework for Credit Card Fraud Detection Under Heterogeneous Data Conditions
by Yutong Chen, Kai Zhang, Hangyu Zhu and Zihao Qiu
Risks 2025, 13(11), 208; https://doi.org/10.3390/risks13110208 - 29 Oct 2025
Viewed by 808
Abstract
The exponential growth of e-commerce and advancements in financial technology have escalated credit card fraud into a major threat, resulting in billions of dollars in global losses annually. This necessitates the development of sophisticated fraud detection systems capable of real-time anomaly interception to [...] Read more.
The exponential growth of e-commerce and advancements in financial technology have escalated credit card fraud into a major threat, resulting in billions of dollars in global losses annually. This necessitates the development of sophisticated fraud detection systems capable of real-time anomaly interception to safeguard financial activities. While federated learning frameworks have been employed to address data privacy concerns in financial applications, existing approaches often fail to account for the heterogeneity in data distributions across different institutions, such as banks, which hinders collaborative model training. In response, this paper introduces the FED-SPFD model, an innovative federated learning framework designed to detect credit card fraud amidst multi-party heterogeneous data. The model employs a share–private segmentation approach to distinguish shared from private data attributes, facilitating unified feature representation learning. It aligns disparate shared features through local sufficient statistics, thus preventing privacy breaches without directly sharing sample data. Additionally, the integration of a “private autoencoder + standard Gaussian alignment” mechanism stabilizes the training process by ensuring consistent private feature distributions. The efficacy of the FED-SPFD model is demonstrated using a real-world dataset from Kaggle, showcasing significant improvements in recall rate compared to state-of-the-art methodologies. Comprehensive evaluation through ablation studies further validates the framework’s robust contributions to accurate and privacy-preserving fraud detection. Practically, this work offers policymakers a compliant cross-institutional risk collaboration paradigm and provides financial institutions with a privacy-protective solution to enhance fraud detection without data sharing violations. Full article
(This article belongs to the Special Issue Computational Methods and Models in the Financial Risk Management Process)
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20 pages, 821 KB  
Article
Tracking Pillar 2 Adjustments Through Macroeconomic Factors: Insights from PCA and BVAR
by Bojan Baškot, Milan Lazarević, Ognjen Erić and Dalibor Tomaš
Risks 2025, 13(11), 207; https://doi.org/10.3390/risks13110207 - 29 Oct 2025
Viewed by 817
Abstract
This paper investigates the systemic macroeconomic determinants of Pillar 2 Requirements (P2R) imposed by the European Central Bank (ECB) under the Single Supervisory Mechanism (SSM). While P2R is formally calibrated at the individual bank level through the Supervisory Review and Evaluation Process (SREP), [...] Read more.
This paper investigates the systemic macroeconomic determinants of Pillar 2 Requirements (P2R) imposed by the European Central Bank (ECB) under the Single Supervisory Mechanism (SSM). While P2R is formally calibrated at the individual bank level through the Supervisory Review and Evaluation Process (SREP), we explore the extent to which common macro-financial shocks influence supervisory capital expectations across banks. Using a panel dataset covering euro area banks between 2021 and 2025, we match bank-level P2R data with country-level macroeconomic indicators. Those variables include real GDP growth, HICP inflation and index levels, government fiscal balance, euro yield curve spreads, net turnover, FDI inflows, construction and industrial production indices, the price-to-income ratio in real estate, and trade balance measures. We apply Principal Component Analysis (PCA) to extract latent variables related to the macroeconomic factors from a broad set of variables, which are then introduced into a Bayesian Vector Autoregression (BVAR) model to assess their dynamic impact on P2R. Our results identify three principal components that capture general macroeconomic cycles, sector-specific real activity, and financial/external imbalances. The impulse response analysis shows that sectoral and external shocks have a more immediate and statistically significant influence on P2R adjustments than broader macroeconomic trends. These findings clearly support the use of systemic macro-financial conditions in supervisory decision-making and support the integration of anticipating macro-prudential analysis into capital requirement frameworks. Full article
(This article belongs to the Special Issue Empirical Corporate Finance and Banking: Risk Management in an Uncertain World)
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