Risks

31 pages, 1823 KiB

Open AccessArticle

Volatility Modeling and Tail Risk Estimation of Financial Assets: Evidence from Gold, Oil, Bitcoin, and Stocks for Selected Markets

by Yilin Zhu, Shairil Izwan Taasim and Adrian Daud

Risks 2025, 13(7), 138; https://doi.org/10.3390/risks13070138 (registering DOI) - 20 Jul 2025

Abstract

As investment portfolios become increasingly diversified and financial asset risks grow more complex, accurately forecasting the risk of multiple asset classes through mathematical modeling and identifying their heterogeneity has emerged as a critical topic in financial research. This study examines the volatility and [...] Read more.

As investment portfolios become increasingly diversified and financial asset risks grow more complex, accurately forecasting the risk of multiple asset classes through mathematical modeling and identifying their heterogeneity has emerged as a critical topic in financial research. This study examines the volatility and tail risk of gold, crude oil, Bitcoin, and selected stock markets. Methodologically, we propose two improved Value at Risk (VaR) forecasting models that combine the autoregressive (AR) model, Exponential Generalized Autoregressive Conditional Heteroskedasticity (EGARCH) model, Extreme Value Theory (EVT), skewed heavy-tailed distributions, and a rolling window estimation approach. The model’s performance is evaluated using the Kupiec test and the Christoffersen test, both of which indicate that traditional VaR models have become inadequate under current complex risk conditions. The proposed models demonstrate superior accuracy in predicting VaR and are applicable to a wide range of financial assets. Empirical results reveal that Bitcoin and the Chinese stock market exhibit no leverage effect, indicating distinct risk profiles. Among the assets analyzed, Bitcoin and crude oil are associated with the highest levels of risk, gold with the lowest, and stock markets occupy an intermediate position. The findings offer practical implications for asset allocation and policy design. Full article

32 pages, 4535 KiB

Open AccessArticle

A Novel Stochastic Copula Model for the Texas Energy Market

by Sudeesha Warunasinghe and Anatoliy Swishchuk

Risks 2025, 13(7), 137; https://doi.org/10.3390/risks13070137 - 16 Jul 2025

Viewed by 189

Abstract

The simulation of wind power, electricity load, and natural gas prices will allow commodity traders to see the future movement of prices in a more probabilistic manner. The ability to observe possible paths for wind power, electricity load, and natural gas prices enables [...] Read more.

The simulation of wind power, electricity load, and natural gas prices will allow commodity traders to see the future movement of prices in a more probabilistic manner. The ability to observe possible paths for wind power, electricity load, and natural gas prices enables traders to obtain valuable insights for placing their trades on electricity prices. Since the above processes involve a seasonality factor, the seasonality component was modeled using a truncated Fourier series, and the random component was modeled using stochastic differential equations (SDE). It is evident from the literature that all the above processes are mean-reverting processes; thus, three mean-reverting Ornstein–Uhlenbeck (OU) processes were considered the model for wind power, the electricity load, and natural gas prices. Industry experts believe there is a correlation between wind power, the electricity load, and natural gas prices. For example, when wind power is higher and the electricity load is lower, natural gas prices are relatively low. The novelty of this study is the incorporation of the correlation structure between processes into the mean-reverting OU process using a copula function. Thus, the study utilized a vine copula and integrated it into the simulation. The study was conducted for the Texas energy market and used daily time scales for the simulations, and it was able to conclude that the proposed novel mean-reverting OU process outperforms the classical mean-reverting process in the case of wind power and the electricity load. Full article

(This article belongs to the Special Issue Stochastic Modeling and Computational Statistics in Finance)

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27 pages, 792 KiB

Open AccessArticle

The Role of Human Capital in Explaining Asset Return Dynamics in the Indian Stock Market During the COVID Era

by Eleftherios Thalassinos, Naveed Khan, Mustafa Afeef, Hassan Zada and Shakeel Ahmed

Risks 2025, 13(7), 136; https://doi.org/10.3390/risks13070136 - 11 Jul 2025

Viewed by 740

Abstract

Over the past decade, multifactor models have shown enhanced capability compared to single-factor models in explaining asset return variability. Given the common assertion that higher risk tends to yield higher returns, this study empirically examines the augmented human capital six-factor model’s performance on [...] Read more.

Over the past decade, multifactor models have shown enhanced capability compared to single-factor models in explaining asset return variability. Given the common assertion that higher risk tends to yield higher returns, this study empirically examines the augmented human capital six-factor model’s performance on thirty-two portfolios of non-financial firms sorted by size, value, profitability, investment, and labor income growth in the Indian market over the period July 2010 to June 2023. Moreover, the current study extends the Fama and French five-factor model by incorporating a human capital proxy by labor income growth as an additional factor thereby proposing an augmented six-factor asset pricing model (HC6FM). The Fama and MacBeth two-step estimation methodology is employed for the empirical analysis. The results reveal that small-cap portfolios yield significantly higher returns than large-cap portfolios. Moreover, all six factors significantly explain the time-series variation in excess portfolio returns. Our findings reveal that the Indian stock market experienced heightened volatility during the COVID-19 pandemic, leading to a decline in the six-factor model’s efficiency in explaining returns. Furthermore, Gibbons, Ross, and Shanken (GRS) test results reveal mispricing of portfolio returns during COVID-19, with a stronger rejection of portfolio efficiency across models. However, the HC6FM consistently shows lower pricing errors and better performance, specifically during and after the pandemic era. Overall, the results offer important insights for policymakers, investors, and portfolio managers in optimizing portfolio selection, particularly during periods of heightened market uncertainty. Full article

(This article belongs to the Special Issue Selected Papers from International Conference on Applied Business and Economics (ICABE 2025))

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19 pages, 2703 KiB

Open AccessArticle

Identifying Risk Regimes in a Sectoral Stock Index Through a Multivariate Hidden Markov Framework

by Akara Kijkarncharoensin

Risks 2025, 13(7), 135; https://doi.org/10.3390/risks13070135 - 9 Jul 2025

Viewed by 269

Abstract

This study explores the presence of hidden market regimes in a sector-specific stock index within the Thai equity market. The behavior of such indices often deviates from broader macroeconomic trends, making it difficult for conventional models to detect regime changes. To overcome this [...] Read more.

This study explores the presence of hidden market regimes in a sector-specific stock index within the Thai equity market. The behavior of such indices often deviates from broader macroeconomic trends, making it difficult for conventional models to detect regime changes. To overcome this limitation, the study employs a multivariate Gaussian mixture hidden Markov model, which enables the identification of unobservable states based on daily and intraday return patterns. These patterns include open-to-close, open-to-high, and low-to-open returns. The model is estimated using various specifications, and the best-performing structure is chosen based on the Akaike Information Criterion and the Bayesian Information Criterion. The final model reveals three statistically distinct regimes that correspond to bullish, sideways, and bearish conditions. Statistical tests, particularly the Kruskal–Wallis method, confirm that return distributions, trading volume, and open interest differ significantly across these regimes. Additionally, the analysis incorporates risk measures, including expected shortfall, maximum drawdown, and the coefficient of variation. The results indicate that the bearish regime carries the highest risk, whereas the bullish regime is relatively stable. These findings offer practical insights for regime-aware portfolio management in sectoral equity markets. Full article

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32 pages, 406 KiB

Open AccessArticle

Unmasking Greenwashing in Finance: A PROMETHEE II-Based Evaluation of ESG Disclosure and Green Accounting Alignment

by George Sklavos, Georgia Zournatzidou, Konstantina Ragazou and Nikolaos Sariannidis

Risks 2025, 13(7), 134; https://doi.org/10.3390/risks13070134 - 9 Jul 2025

Viewed by 333

Abstract

This study examines the degree of alignment between the actual environmental performance and the ESG disclosures of 365 listed financial institutions in Europe for the fiscal year 2024. Although ESG reporting has become a standard practice in the financial sector, there are still [...] Read more.

This study examines the degree of alignment between the actual environmental performance and the ESG disclosures of 365 listed financial institutions in Europe for the fiscal year 2024. Although ESG reporting has become a standard practice in the financial sector, there are still concerns that the quality of the disclosure may not accurately reflect substantive environmental action, which increases the risk of greenwashing. This study addresses this issue by incorporating both ESG disclosure indicators and green accounting metrics into a multi-criteria decision-making framework. This framework is supported by entropy-based weighting to assure objectivity in criterion importance, as outlined in the PROMETHEE II method. The Greenwashing Risk Index (GWI) is a groundbreaking innovation that quantifies the discrepancy between an institution’s classification based on ESG transparency and its performance in green accounting indicators, including environmental penalties, provisions, and resource usage. The results indicate that there is a substantial degree of variation in the performance of ESGs among institutions, with a significant portion of them exhibiting high disclosure scores but insufficient environmental substance. These discrepancies indicate that reputational sustainability may not be operationally sustained. The results have significant implications for regulatory supervision, sustainable finance policy, and ESG rating methodologies. The framework that has been proposed provides a replicable, evidence-based tool for identifying institutions that are at risk of greenwashing and facilitates the implementation of more accountable ESG evaluation practices in the financial sector. Full article

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

19 pages, 3291 KiB

Open AccessArticle

Predicting High-Cost Healthcare Utilization Using Machine Learning: A Multi-Service Risk Stratification Analysis in EU-Based Private Group Health Insurance

by Eslam Abdelhakim Seyam

Risks 2025, 13(7), 133; https://doi.org/10.3390/risks13070133 - 8 Jul 2025

Viewed by 203

Abstract

Healthcare cost acceleration and resource allocation issues have worsened across European health systems, where a small group of patients drives excessive healthcare spending. The prediction of high-cost utilization patterns is important for the sustainable management of healthcare and focused intervention measures. The aim [...] Read more.

Healthcare cost acceleration and resource allocation issues have worsened across European health systems, where a small group of patients drives excessive healthcare spending. The prediction of high-cost utilization patterns is important for the sustainable management of healthcare and focused intervention measures. The aim of our study was to derive and validate machine learning algorithms for high-cost healthcare utilization prediction based on detailed administrative data and by comparing three algorithmic methods for the best risk stratification performance. The research analyzed extensive insurance beneficiary records which compile data from health group collective funds operated by non-life insurers across EU countries, across multiple service classes. The definition of high utilization was equivalent to the upper quintile of overall health expenditure using a moderate cost threshold. The research applied three machine learning algorithms, namely logistic regression using elastic net regularization, the random forest, and support vector machines. The models used a comprehensive set of predictor variables including demographics, policy profiles, and patterns of service utilization across multiple domains of healthcare. The performance of the models was evaluated using the standard train–test methodology and rigorous cross-validation procedures. All three models demonstrated outstanding discriminative ability by achieving area under the curve values at near-perfect levels. The random forest achieved the best test performance with exceptional metrics, closely followed by logistic regression with comparable exceptional performance. Service diversity proved to be the strongest predictor across all models, while dentistry services produced an extraordinarily high odds ratio with robust confidence intervals. The group of high utilizers comprised approximately one-fifth of the sample but demonstrated significantly higher utilization across all service classes. Machine learning algorithms are capable of classifying patients eligible for the high utilization of healthcare services with nearly perfect discriminative ability. The findings justify the application of predictive analytics for proactive case management, resource planning, and focused intervention measures across private group health insurance providers in EU countries. Full article

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32 pages, 1628 KiB

Open AccessFeature PaperArticle

The Mack Chain Ladder and Data Granularity for Preserved Development Periods

by Greg Taylor

Risks 2025, 13(7), 132; https://doi.org/10.3390/risks13070132 - 7 Jul 2025

Viewed by 151

Abstract

This paper is concerned with the choice of data granularity for the application of the Mack chain ladder model to forecast a loss reserve. It is a sequel to a related paper by Taylor, which considers the same question for the EDF chain [...] Read more.

This paper is concerned with the choice of data granularity for the application of the Mack chain ladder model to forecast a loss reserve. It is a sequel to a related paper by Taylor, which considers the same question for the EDF chain ladder model. As in the earlier paper, it considers the question as to whether a decrease in the time unit leads to an increase or decrease in the variance of the loss reserve estimate. The question of whether a Mack chain ladder that is valid for one time unit (here called mesh size) remains so for another is investigated. The conditions under which the model does remain valid are established. There are various ways in which the mesh size of a data triangle may be varied, two of them of particular interest. The paper examines one of these, namely that in which development periods are preserved. Two versions of this are investigated: 1. the aggregation of development periods without change to accident periods; 2. the aggregation of accident periods without change to development periods. Taylor found that, in the case of the Poisson chain ladder, an increase in mesh size always increases the variance of the loss reserve estimate (subject to mild technical conditions). The case of the Mack chain ladder is more nuanced in that an increase in variance is not always guaranteed. Whether or not an increase or decrease occurs depends on the numerical values of certain of the age-to-age factors actually observed. The threshold values of the age-to-age factors at which an increase transitions to a decrease in variance are calculated. In the case of a change in the mesh of development periods, but with no change to accident periods, these values are computed for one particular data set, where it is found that variance always increases. It is conjectured that data sets in which this does not happen would be relatively rare. The situation is somewhat different when changes in mesh size over accident periods are considered. Here, the question of an increase or decrease in variance is more complex, and, in general terms, the occurrence of an increase in variance with increased mesh size is less likely. Full article

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15 pages, 2189 KiB

Open AccessArticle

AI Risk Management: A Bibliometric Analysis

by Adelaide Emma Bernardelli and Paolo Giudici

Risks 2025, 13(7), 131; https://doi.org/10.3390/risks13070131 - 7 Jul 2025

Viewed by 360

Abstract

The growth of Artificial Intelligence applications requires the development of risk management models that can balance opportunities with risks. This paper contributes to the development of Artificial Intelligence risk management models by means of a thorough bibliometric analysis. The analysis highlights the need [...] Read more.

The growth of Artificial Intelligence applications requires the development of risk management models that can balance opportunities with risks. This paper contributes to the development of Artificial Intelligence risk management models by means of a thorough bibliometric analysis. The analysis highlights the need to develop a quantitative AI risk management framework. Full article

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

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16 pages, 1792 KiB

Open AccessArticle

The Russia–Ukraine Conflict and Stock Markets: Risk and Spillovers

by Maria Leone, Alberto Manelli and Roberta Pace

Risks 2025, 13(7), 130; https://doi.org/10.3390/risks13070130 - 4 Jul 2025

Viewed by 423

Abstract

Globalization and the spread of technological innovations have made world markets and economies increasingly unified and conditioned by international trade, not only for sales markets but above all for the supply of raw materials necessary for the functioning of the production complex of [...] Read more.

Globalization and the spread of technological innovations have made world markets and economies increasingly unified and conditioned by international trade, not only for sales markets but above all for the supply of raw materials necessary for the functioning of the production complex of each country. Alongside oil and gold, the main commodities traded include industrial metals, such as aluminum and copper, mineral products such as gas, electrical and electronic components, agricultural products, and precious metals. The conflict between Russia and Ukraine tested the unification of markets, given that these are countries with notable raw materials and are strongly dedicated to exports. This suggests that commodity prices were able to influence the stock markets, especially in the countries most closely linked to the two belligerents in terms of import-export. Given the importance of industrial metals in this period of energy transition, the aim of our study is to analyze whether Industrial Metals volatility affects G7 stock markets. To this end, the BEKK-GARCH model is used. The sample period spans from 3 January 2018 to 17 September 2024. The results show that lagged shocks and volatility significantly and positively influence the current conditional volatility of commodity and stock returns during all periods. In fact, past shocks inversely influence the current volatility of stock indices in periods when external events disrupt financial markets. The results show a non-linear and positive impact of commodity volatility on the implied volatility of the stock markets. The findings suggest that the war significantly affected stock prices and exacerbated volatility, so investors should diversify their portfolios to maximize returns and reduce risk differently in times of crisis, and a lack of diversification of raw materials is a risky factor for investors. Full article

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

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15 pages, 1398 KiB

Open AccessArticle

A Profitability and Risk Decomposition Analysis of the Open Economy Insurance Sector

by Zdeněk Zmeškal, Dana Dluhošová, Karolina Lisztwanová and Iveta Ratmanová

Risks 2025, 13(7), 129; https://doi.org/10.3390/risks13070129 - 2 Jul 2025

Viewed by 168

Abstract

The objective of this paper is to analyse profitability and risk through the return on equity (ROE) measure of the open economy insurance sector in a non-stable economic period with an economic shock chain, during the years 2018–2022, characterised by an [...] Read more.

The objective of this paper is to analyse profitability and risk through the return on equity (ROE) measure of the open economy insurance sector in a non-stable economic period with an economic shock chain, during the years 2018–2022, characterised by an overheating economy, the Covid pandemic, the war in Ukraine, and a high-inflation wave. The ROE pyramid decomposition structure is proposed, along with the detailed CARAMEL version. A static and risk (dynamic) decomposition deviation analysis is used. The yearly non-stable drivers of insurance sector profitability deviation were confirmed. Despite this, the most influential were the earnings ratio deviations in either increasing or decreasing ROE alternatives. Solvency positively influenced the ROE deviation. It turned out that earnings and asset quality enormously increase the risk of the insurance sector. Conversely, risk is decreased mainly by liquidity and management. Simultaneously, significant, influential factors were identified. The results can serve as a background for carrying out operations, strategic analysis, and decision-making. Full article

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15 pages, 3091 KiB

Open AccessArticle

Domain Knowledge Preservation in Financial Machine Learning: Evidence from Autocallable Note Pricing

by Mohammed Ahnouch, Lotfi Elaachak and Erwan Le Saout

Risks 2025, 13(7), 128; https://doi.org/10.3390/risks13070128 - 1 Jul 2025

Viewed by 263

Abstract

Machine learning applications in finance commonly employ feature decorrelation techniques developed for generic statistical problems. We investigate whether this practice appropriately addresses the unique characteristics of financial data, where correlations often encode fundamental economic relationships rather than statistical noise. Using autocallable structured notes [...] Read more.

Machine learning applications in finance commonly employ feature decorrelation techniques developed for generic statistical problems. We investigate whether this practice appropriately addresses the unique characteristics of financial data, where correlations often encode fundamental economic relationships rather than statistical noise. Using autocallable structured notes as a laboratory, we demonstrate that preserving natural financial correlations outperforms conventional orthogonalization approaches. Our analysis covers autocallable notes with quarterly coupon payments, dual barrier structure, and embedded down-and-in up-and-out put options, priced using analytical methods with automatic differentiation for Greeks’ computation. Across neural networks, gradient boosting, and hybrid architectures, basic financial features achieve superior performance compared to decorrelated alternatives, with RMSE improvements ranging from 43% to 191%. The component-wise analysis reveals complex interactions between autocall mechanisms and higher-order sensitivities, particularly affecting vanna and volga patterns near barrier levels. These findings provide empirical evidence that financial machine learning benefits from domain-specific feature engineering principles that preserve economic relationships. Across all tested architectures, basic features consistently outperformed orthogonalized alternatives, with the largest improvements observed in CatBoost. Full article

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21 pages, 699 KiB

Open AccessArticle

Stock Market Hype: An Empirical Investigation of the Impact of Overconfidence on Meme Stock Valuation

by Richard Mawulawoe Ahadzie, Peterson Owusu Junior, John Kingsley Woode and Dan Daugaard

Risks 2025, 13(7), 127; https://doi.org/10.3390/risks13070127 - 1 Jul 2025

Viewed by 434

Abstract

This study investigates the relationship between overconfidence and meme stock valuation, drawing on panel data from 28 meme stocks listed from 2019 to 2024. The analysis incorporates key financial indicators, including Tobin’s Q ratio, market capitalization, return on assets, leverage, and volatility. A [...] Read more.

This study investigates the relationship between overconfidence and meme stock valuation, drawing on panel data from 28 meme stocks listed from 2019 to 2024. The analysis incorporates key financial indicators, including Tobin’s Q ratio, market capitalization, return on assets, leverage, and volatility. A range of overconfidence proxies is employed, including changes in trading volume, turnover rate, changes in outstanding shares, and alternative measures of excessive trading. We observe a significant positive relationship between overconfidence (as measured by changes in trading volume) and firm valuation, suggesting that investor biases contribute to notable pricing distortions. Leverage has a significant negative relationship with firm valuation. In contrast, market capitalization has a significant positive relationship with firm valuation, implying that meme stock investors respond to both speculative sentiment and traditional firm fundamentals. Robustness checks using alternative proxies reveal that turnover rate and changes in the number of shares are negatively related to valuation. This shows the complex dynamics of meme stocks, where psychological factors intersect with firm-specific indicators. However, results from a dynamic panel model estimated using the Dynamic System Generalized Method of Moments (GMM) show that the turnover rate has a significantly positive relationship with firm valuation. These results offer valuable insights into the pricing behavior of meme stocks, revealing how investor sentiment impacts periodic valuation adjustments in speculative markets. Full article

(This article belongs to the Special Issue Theoretical and Empirical Asset Pricing)

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15 pages, 544 KiB

Open AccessArticle

Gender Diverse Boardrooms and Earnings Manipulation: Does Democracy Matter?

by Evangelos G. Varouchas, Stavros E. Arvanitis and Christos Floros

Risks 2025, 13(7), 126; https://doi.org/10.3390/risks13070126 - 30 Jun 2025

Viewed by 276

Abstract

We investigate the influence of boardroom gender diversity on earnings management. Drawing on a sample of European firms over the 2010–2023 period, we document an inverted U-shaped nexus between boardroom gender heterogeneity and earnings manipulation. Moreover, we also find that the Democracy Index [...] Read more.

We investigate the influence of boardroom gender diversity on earnings management. Drawing on a sample of European firms over the 2010–2023 period, we document an inverted U-shaped nexus between boardroom gender heterogeneity and earnings manipulation. Moreover, we also find that the Democracy Index moderates the curvilinear nexus by flattening the inverted U-curve and shifting the inflection point leftward. Our findings are consistent across various measures of earnings management and different econometric approaches, offering valuable insights for European policymakers. Full article

(This article belongs to the Special Issue Sustainable Corporate Governance and Corporate Risks)

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38 pages, 518 KiB

Open AccessArticle

Credit Risk Assessment Using Fuzzy Inhomogeneous Markov Chains Within a Fuzzy Market

by P.-C.G. Vassiliou

Risks 2025, 13(7), 125; https://doi.org/10.3390/risks13070125 - 28 Jun 2025

Viewed by 262

Abstract

In the present study, we model the migration process and the changes in the market environment. The migration process is being modeled as an

F

-inhomogeneous semi-Markov process with fuzzy states. The evolution of the migration process takes place within a stochastic market [...] Read more.

In the present study, we model the migration process and the changes in the market environment. The migration process is being modeled as an

F

-inhomogeneous semi-Markov process with fuzzy states. The evolution of the migration process takes place within a stochastic market environment with fuzzy states, the transitions of which are being modeled as an

F

-inhomogeneous semi-Markov process. We prove a recursive relation from which we could find the survival probabilities of the bonds or debts as functions of the basic parameters of the two

F

-inhomogeneous semi-Markov processes. The asymptotic behavior of the survival probabilities is being found under certain easily met conditions in closed analytic form. Finally, we provide maximum likelihood estimators for the basic parameters of the proposed models. Full article

27 pages, 636 KiB

Open AccessFeature PaperArticle

Risk-Adjusted Estimation and Graduation of Transition Intensities for Disability and Long-Term Care Insurance: A Multi-State Model Approach

by Beatriz A. Curioso, Gracinda R. Guerreiro and Manuel L. Esquível

Risks 2025, 13(7), 124; https://doi.org/10.3390/risks13070124 - 27 Jun 2025

Viewed by 255

Abstract

This paper introduces a methodology for estimating transition intensities in a multi-state model for disability and long-term care insurance. We propose a novel framework that integrates observable risk factors, such as demographic (age and sex), lifestyle (smoking and exercise habits) and health-related variables [...] Read more.

This paper introduces a methodology for estimating transition intensities in a multi-state model for disability and long-term care insurance. We propose a novel framework that integrates observable risk factors, such as demographic (age and sex), lifestyle (smoking and exercise habits) and health-related variables (body mass index), into the estimation and graduation of transition intensities, using a parametric approach based on the Gompertz–Makeham law and generalised linear models. The model features four states—autonomous, dead, and two intermediate states representing varying disability levels—providing a detailed view of disability/lack of autonomy progression. To illustrate the proposed framework, we simulate a dataset with individual risk profiles and model trajectories, mirroring Portugal’s demographic composition. This allows us to derive a functional form (as a function of age) for the transition intensities, stratified by relevant risk factors, thus enabling precise risk differentiation. The results offer a robust basis for developing tailored pricing structures in the Portuguese market, with broader applications in actuarial science and insurance. By combining granular disability modelling with risk factor integration, our approach enhances accuracy in pricing structure and risk assessment. Full article

(This article belongs to the Special Issue Advancements in Actuarial Mathematics and Insurance Risk Management)

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21 pages, 511 KiB

Open AccessArticle

Determinants of Banking Profitability in Angola: A Panel Data Analysis with Dynamic GMM Estimation

by Eurico Lionjanga Cangombe, Luís Gomes Almeida and Fernando Oliveira Tavares

Risks 2025, 13(7), 123; https://doi.org/10.3390/risks13070123 - 27 Jun 2025

Viewed by 387

Abstract

This study aims to analyze the determinants of bank profitability in Angola by employing panel data econometric models, specifically, the Generalized Method of Moments (GMM), to assess the impact of internal and external factors on the financial indicators ROE, ROA, and NIM for [...] Read more.

This study aims to analyze the determinants of bank profitability in Angola by employing panel data econometric models, specifically, the Generalized Method of Moments (GMM), to assess the impact of internal and external factors on the financial indicators ROE, ROA, and NIM for the period 2016 to 2023. The results reveal that credit risk, operational efficiency, and liquidity are critical determinants of banking performance. Effective credit risk management and cost optimization are essential for the sector’s stability. Banking concentration presents mixed effects, enhancing net interest income while potentially undermining efficiency. Economic growth supports profitability, whereas inflation exerts a negative influence. The COVID-19 pandemic worsened asset quality, increased credit risk, and led to a rise in non-performing loans and provisions. Reforms implemented by the National Bank of Angola have contributed to strengthening the banking system’s resilience through restructuring and regulatory improvements. The rise of digitalization and fintech presents opportunities to enhance financial inclusion and efficiency, although their success relies on advancing financial literacy. This study contributes to the literature by providing updated empirical evidence on the factors influencing bank profitability within an emerging economy’s distinctive institutional and economic context. Full article

20 pages, 2000 KiB

Open AccessArticle

Breaking the Mortality Curve: Investment-Driven Acceleration in Life Expectancy and Insurance Innovation

by David M. Dror

Risks 2025, 13(7), 122; https://doi.org/10.3390/risks13070122 - 26 Jun 2025

Viewed by 294

Abstract

Capital investment in longevity science—research targeting the biological processes of aging through interventions like cellular reprogramming, AI-driven drug discovery, and biological age monitoring—may create significant divergence between traditional actuarial projections and emerging mortality improvements. This paper examines how accelerating investment in life extension [...] Read more.

Capital investment in longevity science—research targeting the biological processes of aging through interventions like cellular reprogramming, AI-driven drug discovery, and biological age monitoring—may create significant divergence between traditional actuarial projections and emerging mortality improvements. This paper examines how accelerating investment in life extension technologies affects mortality improvement trajectories beyond conventional actuarial assumptions, building on the comprehensive investment landscape analysis documented in “Investors in Longevity” supported by venture capital databases, industry reports, and regulatory filings. We introduce an Investment-Adjusted Mortality Model (IAMM) that incorporates capital allocation trends as leading indicators of mortality improvement acceleration. Under high-investment scenarios (annual funding of USD 15+ billion in longevity technologies), current insurance products may significantly underestimate longevity risk, creating potential solvency challenges. Our statistical analysis demonstrates that investment-driven mortality improvements—actual reductions in death rates resulting from new anti-aging interventions—could exceed traditional projections by 18–31% by 2040. We validate our model by backtesting historical data, showing improved predictive performance (35% reduction in MAPE) compared to traditional Lee–Carter approaches during periods of significant medical technology advancement. Based on these findings, we propose modified insurance structures, including dynamic mortality-linked products and biological age underwriting, quantifying their effectiveness in reducing longevity risk exposure by 42–67%. These results suggest the need for actuarial science to incorporate investment dynamics in response to the changing longevity investment environment detailed in “Investors in Longevity”. The framework presented provides both theoretically grounded and empirically tested tools for incorporating investment dynamics into mortality projections and insurance product design, addressing gaps in current risk management approaches for long-term mortality exposure. Full article

(This article belongs to the Special Issue Advancements in Actuarial Mathematics and Insurance Risk Management)

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18 pages, 1209 KiB

Open AccessFeature PaperArticle

Does Political Risk Affect the Efficiency of the Exchange-Traded Fund Market?—Entropy-Based Analysis Before and After the 2025 U.S. Presidential Inauguration

by Joanna Olbryś

Risks 2025, 13(7), 121; https://doi.org/10.3390/risks13070121 - 26 Jun 2025

Viewed by 293

Abstract

The aim of this research is to thoroughly investigate the influence of the 2025 Donald Trump Presidential Inauguration on informational efficiency of the U.S. exchange-traded fund market in the context of political risk. The data set includes daily observations for twenty U.S. Exchange-Traded [...] Read more.

The aim of this research is to thoroughly investigate the influence of the 2025 Donald Trump Presidential Inauguration on informational efficiency of the U.S. exchange-traded fund market in the context of political risk. The data set includes daily observations for twenty U.S. Exchange-Traded Funds (ETFs). The whole sample comprises the period from 20 October 2024 to 20 April 2025. Since the Presidential Inauguration of Donald Trump took place on 20 January 2025, two sub-samples of an equal length are analyzed: (1) the period before the 2025 U.S. Presidential Inauguration from 20 October 2024 to 19 January 2025 and (2) the period after the 2025 U.S. Presidential Inauguration from 20 January 2025 to 20 April 2025. Since the whole sample period is not long (six months), to estimate market efficiency, modified Shannon entropy based on symbolic encoding with two thresholds is used. The empirical findings are visualized by symbol-sequence histograms. The proposed research hypothesis states that the U.S. ETF market’s informational efficiency, as measured by entropy, substantially decreased during the turbulent period after the Donald Trump Presidential Inauguration compared to the period before the Inauguration. The results unambiguously confirm the research hypothesis and indicate that political risk could affect the informational efficiency of markets. To the best of the author’s knowledge, this is the first study exploring the influence of the Donald Trump Presidential Inauguration on the informational efficiency of the U.S. ETF market. Full article

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

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18 pages, 699 KiB

Open AccessArticle

Systemic Risk and Commercial Bank Stability in the Middle East and North Africa (MENA) Region

by Rim Jalloul and Mahfuzul Haque

Risks 2025, 13(7), 120; https://doi.org/10.3390/risks13070120 - 24 Jun 2025

Viewed by 430

Abstract

Using panel data spanning 2004–2023 of 21 countries in the MENA (Middle East and North Africa) region, we measure systemic risk and assess its influence on key banking sector performance indicators, including financial stability (proxied by commercial bank branches per 100,000 adults), providing [...] Read more.

Using panel data spanning 2004–2023 of 21 countries in the MENA (Middle East and North Africa) region, we measure systemic risk and assess its influence on key banking sector performance indicators, including financial stability (proxied by commercial bank branches per 100,000 adults), providing evidence from the emerging market context. One of the key findings of the study is the pivotal role played by financial access in promoting banking stability. In particular, the density and outreach of commercial banking branches were shown to have a stabilizing effect on the banking system. Also, findings reveal that systemic risk significantly undermines bank stability and operational efficiency while constraining financial depth. The study contributes to the literature by offering empirical evidence on the adverse effects of systemic risk in a region characterized by financial volatility and structural vulnerabilities. These findings align with existing global evidence that links financial development with reduced systemic risk, yet they also offer new empirical insights that are contextually relevant to the MENA region. The findings provide actionable recommendations for policymakers. Regulatory authorities in the MENA region should consider strategies that not only enhance the robustness of financial institutions but also promote inclusive access to banking services. The dual focus on institutional soundness and outreach could serve as a cornerstone for sustainable financial stability. Tailored policies that encourage branch expansion in underserved areas, coupled with incentives for inclusive banking practices, may yield long-term benefits by reducing the concentration of risk and improving the responsiveness of the financial system to external shocks. Full article

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

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18 pages, 570 KiB

Open AccessArticle

Copula Modeling of COVID-19 Excess Mortality

by Jonas Asplund and Arkady Shemyakin

Risks 2025, 13(7), 119; https://doi.org/10.3390/risks13070119 - 24 Jun 2025

Viewed by 319

Abstract

COVID-19’s effects on mortality are hard to quantify. Issues with attribution can cause problems with resulting conclusions. Analyzing excess mortality addresses this concern and allows for the analysis of broader effects of the pandemic. We propose separate ARIMA models to analyze excess mortality [...] Read more.

COVID-19’s effects on mortality are hard to quantify. Issues with attribution can cause problems with resulting conclusions. Analyzing excess mortality addresses this concern and allows for the analysis of broader effects of the pandemic. We propose separate ARIMA models to analyze excess mortality for several countries. For the model of joint excess mortality, we suggest vine copulas with Bayesian pair copula selection. This is a new methodology and after its discussion we offer an illustration. The present study examines weekly mortality data from 2019 to 2022 in the USA, Canada, France, Germany, Norway, and Sweden. Previously proposed ARIMA models have low lags and no residual autocorrelation. Only Norway’s residuals exhibited normality, while the remaining residuals suggest skewed Student t-distributions as a plausible fit. A vine copula model was then developed to model the association between the ARIMA residuals for different countries, with the countries farther apart geographically exhibiting weak or no association. The validity of fitted distributions and resulting vine copula was checked using 2023 data. Goodness of fit tests suggest that the fitted distributions were suitable, except for the USA, and that the vine copula used was also valid. We conclude that the time series models of COVID-19 excess mortality are viable. Overall, the suggested methodology seems suitable for creating joint forecasts of pandemic mortality for several countries or geographical regions. Full article

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21 pages, 1012 KiB

Open AccessFeature PaperArticle

Advanced Operator Theory for Energy Market Trading: A New Framework

by Michele Bufalo and Viviana Fanelli

Risks 2025, 13(7), 118; https://doi.org/10.3390/risks13070118 - 20 Jun 2025

Viewed by 166

Abstract

This paper analyzes a parabolic operator

L

that generalizes several well-known operators commonly used in financial mathematics. We establish the existence and uniqueness of the Feller semigroup associated with

L

and derive its explicit analytical representation. The theoretical framework developed in this study [...] Read more.

This paper analyzes a parabolic operator

L

that generalizes several well-known operators commonly used in financial mathematics. We establish the existence and uniqueness of the Feller semigroup associated with

L

and derive its explicit analytical representation. The theoretical framework developed in this study provides a robust foundation for modeling stochastic processes relevant to financial markets. Furthermore, we apply these findings to energy market trading by developing specialized simulation algorithms and forecasting models. These methodologies were tested across all assets comprising the S&P 500 Energy Index, evaluating their predictive accuracy and effectiveness in capturing market dynamics. The empirical analysis demonstrated the practical advantages of employing generalized semigroups in modeling non-Gaussian market behaviors and extreme price fluctuations. Full article

(This article belongs to the Special Issue Financial Derivatives and Hedging in Energy Markets)

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Risks, Volume 13, Issue 7 (July 2025) – 21 articles

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