Risks

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

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

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

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

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

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

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

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

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

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

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

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, 1009 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

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)

14 pages, 537 KiB

Open AccessArticle

Non-Uniqueness of Best-Of Option Prices Under Basket Calibration

by Mohammed Ahnouch, Lotfi Elaachak and Abderrahim Ghadi

Risks 2025, 13(6), 117; https://doi.org/10.3390/risks13060117 - 18 Jun 2025

Abstract

This paper demonstrates that perfectly calibrating a multi-asset model to observed market prices of all basket call options is insufficient to uniquely determine the price of a best-of call option. Previous research on multi-asset option pricing has primarily focused on complete market settings [...] Read more.

This paper demonstrates that perfectly calibrating a multi-asset model to observed market prices of all basket call options is insufficient to uniquely determine the price of a best-of call option. Previous research on multi-asset option pricing has primarily focused on complete market settings or assumed specific parametric models, leaving fundamental questions about model risk and pricing uniqueness in incomplete markets inadequately addressed. This limitation has critical practical implications: derivatives practitioners who hedge best-of options using basket-equivalent instruments face fundamental distributional uncertainty that compounds the well-recognized non-linearity challenges. We establish this non-uniqueness using convex analysis (extreme ray characterization demonstrating geometric incompatibility between payoff structures), measure theory (explicit construction of distinct equivalent probability measures), and geometric analysis (payoff structure comparison). Specifically, we prove that the set of equivalent probability measures consistent with observed basket prices contains distinct measures yielding different best-of option prices, with explicit no-arbitrage bounds

[a_{K}, b_{K}]

quantifying this uncertainty. Our theoretical contribution provides the first rigorous mathematical foundation for several empirically observed market phenomena: wide bid-ask spreads on extremal options, practitioners’ preference for over-hedging strategies, and substantial model reserves for exotic derivatives. We demonstrate through concrete examples that substantial model risk persists even with perfect basket calibration and equivalent measure constraints. For risk-neutral pricing applications, equivalent martingale measure constraints can be imposed using optimal transport theory, though this requires additional mathematical complexity via Schrödinger bridge techniques while preserving our fundamental non-uniqueness results. The findings establish that additional market instruments beyond basket options are mathematically necessary for robust exotic derivative pricing. Full article

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28 pages, 824 KiB

Open AccessArticle

Advancing Credit Rating Prediction: The Role of Machine Learning in Corporate Credit Rating Assessment

by Nazário Augusto de Oliveira and Leonardo Fernando Cruz Basso

Risks 2025, 13(6), 116; https://doi.org/10.3390/risks13060116 - 17 Jun 2025

Abstract

Accurate corporate credit ratings are essential for financial risk assessment; yet, traditional methodologies relying on manual evaluation and basic statistical models often fall short in dynamic economic conditions. This study investigated the potential of machine-learning (ML) algorithms as a more precise and adaptable [...] Read more.

Accurate corporate credit ratings are essential for financial risk assessment; yet, traditional methodologies relying on manual evaluation and basic statistical models often fall short in dynamic economic conditions. This study investigated the potential of machine-learning (ML) algorithms as a more precise and adaptable alternative for credit rating predictions. Using a seven-year dataset from S&P Capital IQ Pro, corporate credit ratings across 20 countries were analyzed, leveraging 51 financial and business risk variables. The study evaluated multiple ML models, including Logistic Regression, Support Vector Machines, Decision Trees, Random Forest, Gradient Boosting (GB), and Neural Networks, using rigorous data pre-processing, feature selection, and validation techniques. Results indicate that Artificial Neural Networks (ANN) and GB consistently outperform traditional models, particularly in capturing non-linear relationships and complex interactions among predictive factors. This study advances financial risk management by demonstrating the efficacy of ML-driven credit rating systems, offering a more accurate, efficient, and scalable solution. Additionally, it provides practical insights for financial institutions aiming to enhance their risk assessment frameworks. Future research should explore alternative data sources, real-time analytics, and model explainability to facilitate regulatory adoption. Full article

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

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11 pages, 509 KiB

Open AccessArticle

Do Regulations and Insurance Knowledge Affect Insurance Demand? Evidence from Bicycle Insurance in Japan

by Yoshihiro Asai

Risks 2025, 13(6), 115; https://doi.org/10.3390/risks13060115 - 17 Jun 2025

Abstract

Several empirical studies have attempted to clarify whether differences in regulations affect people’s behavior. However, due to a lack of data, few have attempted to clarify whether these differences affect the purchase of insurance. Therefore, in this study, I conducted a survey of [...] Read more.

Several empirical studies have attempted to clarify whether differences in regulations affect people’s behavior. However, due to a lack of data, few have attempted to clarify whether these differences affect the purchase of insurance. Therefore, in this study, I conducted a survey of consumers in Japan and analyzed the characteristics of those who bought bicycle insurance. My findings are as follows: First, users tend to purchase bicycle insurance in prefectures where it is compulsory. Second, bicycle users with a high level of insurance knowledge tend to purchase insurance. Third, consumers who use bicycles more frequently tend to purchase insurance. Full article

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

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25 pages, 486 KiB

Open AccessArticle

The Impact of ESG on the Financial Performance of Johannesburg Stock Exchange-Listed Companies

by Wilfreda Indira Chawarura, Mabutho Sibanda and Kuziva Mamvura

Risks 2025, 13(6), 114; https://doi.org/10.3390/risks13060114 - 17 Jun 2025

Abstract

The relationship between ESG and firm performance is complex and tends to yield mixed results globally. In South Africa, ESG implementation is still in its infancy stage due to economic and developmental challenges. Despite these challenges, the JSE introduced sustainability disclosure guidelines in [...] Read more.

The relationship between ESG and firm performance is complex and tends to yield mixed results globally. In South Africa, ESG implementation is still in its infancy stage due to economic and developmental challenges. Despite these challenges, the JSE introduced sustainability disclosure guidelines in 2022 to enhance ESG adoption in South Africa. Thus, the study seeks to understand the impact of ESG and firm size on the financial performance of JSE-listed firms in South Africa. The study utilised the JSE Top 40 firms for the period from 2002 to 2022. Furthermore, the study employed a two-step System Generalised Method of Moments, to estimate the impact of total ESG and individual dimensions of ESG on firm financial performance. Additionally, the study examined the moderating effects of firm size on the relationship between financial performance and ESG. The results revealed a positive and significant relationship between total ESG and firm financial performance. However, the findings regarding individual ESG dimensions and firm performance are mixed. Firm size has a moderating effect on the relationship between ESG and firm financial performance. The implication of these findings for South Africa is increased foreign direct investment from green investors and listed firms seriously considering ESG in their operations. Full article

22 pages, 4044 KiB

Open AccessArticle

Dynamic Portfolio Optimization with Diversification Analysis and Asset Selection Amidst High Correlation Using Cryptocurrencies and Bank Equities

by Hamdan Bukenya Ntare, John Weirstrass Muteba Mwamba and Franck Adekambi

Risks 2025, 13(6), 113; https://doi.org/10.3390/risks13060113 - 16 Jun 2025

Abstract

There has been growing interest among investors to include cryptocurrencies in their portfolios because of their diversification potential. However, the diversification role of cryptocurrencies when added to South African bank equities is yet to be determined. This study rigorously evaluates asset co-movement and [...] Read more.

There has been growing interest among investors to include cryptocurrencies in their portfolios because of their diversification potential. However, the diversification role of cryptocurrencies when added to South African bank equities is yet to be determined. This study rigorously evaluates asset co-movement and diversification benefits of integrating cryptocurrencies into South African bank equity portfolios. Using advanced financial engineering techniques, including multi-asset particle swarm optimizer (MA-PSO), random optimizer, and a static equal-weighted portfolio (EWP) model, this study analyzed the dynamic portfolio performance and diversification of cryptocurrencies in the 2017–2024 period. The portfolio performance of the three methods is also compared with the results from the traditional one-period mean–variance optimization (MVO) method. The findings underscore the superiority of dynamic models over static EWP in assessing the impact of cryptocurrency inclusion in bank equity portfolios. While pre-COVID-19 studies identified cryptocurrencies as effective hedges against market downturns, this protective role appears attenuated in the post-COVID-19 era. The dynamic MA-PSO model emerges as the optimal approach, delivering better-diversified portfolios. Consequently, South African portfolio managers must carefully evaluate investor risk tolerance before incorporating cryptocurrencies, with regulators imposing stringent guidelines to mitigate potential losses. Full article

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20 pages, 2552 KiB

Open AccessArticle

Evaluation of Perpetual American Put Options with General Payoff

by Luca Anzilli and Lucianna Cananà

Risks 2025, 13(6), 112; https://doi.org/10.3390/risks13060112 - 13 Jun 2025

Abstract

In this paper, we study perpetual American put options with a generalized standard put payoff and establish sufficient conditions for the existence and uniqueness of the solution to the associated pricing problem. As a key tool, we express the Black–Scholes operator in terms [...] Read more.

In this paper, we study perpetual American put options with a generalized standard put payoff and establish sufficient conditions for the existence and uniqueness of the solution to the associated pricing problem. As a key tool, we express the Black–Scholes operator in terms of elasticity. This formulation enables us to demonstrate that the considered pricing problem admits a unique solution when the payoff function exhibits strictly decreasing elasticity with respect to the underlying asset. Furthermore, this approach allows us to derive closed-form solutions for option pricing. Full article

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

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20 pages, 641 KiB

Open AccessArticle

Stochastic Uncertainty of Institutional Quality and the Corporate Capital Structure in the G8 and MENA Countries

by Tarek Eldomiaty, Islam Azzam, Jasmine Fouad, Hussein Mowafak Sadek and Marwa Anwar Sedik

Risks 2025, 13(6), 111; https://doi.org/10.3390/risks13060111 - 12 Jun 2025

Abstract

This paper examines the impacts of observed versus uncertain (stochastic) institutional quality of corporate debt financing. This paper compares the impacts of two distinct levels of institutional quality in developed and developing economies. World governance indicators (WGIs) are used as proxies for institutional [...] Read more.

This paper examines the impacts of observed versus uncertain (stochastic) institutional quality of corporate debt financing. This paper compares the impacts of two distinct levels of institutional quality in developed and developing economies. World governance indicators (WGIs) are used as proxies for institutional quality. Stochastic Geometric Brownian Motion (GBM) is used to quantify the institutional uncertainty of WGIs. The results of GLS estimates using a sample of 309 nonfinancial listed firms in G8 countries and 373 nonfinancial listed firms in MENA countries covering the years 2016–2022 show (a) positive (negative) stochastic impacts of voice and accountability (government effectiveness and political stability) on debt financing in the G8 and MENA regions; (b) although potential improvements in institutional quality are shared concerns among G8 and MENA countries, the former outperforms the latter in terms of creditors’ contract protection and enforcement, paving the way for public policy makers in the MENA region to enhance regulations that protect debt contractual obligations; (c) macroeconomic variables have sporadic impacts; GDP growth is significant in G8 but not in MENA countries; (d) the negative impacts of inflation rates are consistent in both regions; and (e) unemployment plays a negative signaling role in the G8 region only. This paper contributes to the related literature by examining the uncertain impact of institutional quality on corporate debt financing. This paper offers implications for policy makers, directing them to focus on institutional endeavors in a way that helps companies secure the debt financing required to support investment growth. Full article

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