Risks

26 pages, 2575 KiB

Open AccessArticle

Comparing the Effectiveness of Machine Learning and Deep Learning Models in Student Credit Scoring: A Case Study in Vietnam

by Nguyen Thi Hong Thuy, Nguyen Thi Vinh Ha, Nguyen Nam Trung, Vu Thi Thanh Binh, Nguyen Thu Hang and Vu The Binh

Risks 2025, 13(5), 99; https://doi.org/10.3390/risks13050099 - 20 May 2025

Abstract

In emerging markets like Vietnam, where student borrowers often lack traditional credit histories, accurately predicting loan eligibility remains a critical yet underexplored challenge. While machine learning and deep learning techniques have shown promise in credit scoring, their comparative performance in the context of [...] Read more.

In emerging markets like Vietnam, where student borrowers often lack traditional credit histories, accurately predicting loan eligibility remains a critical yet underexplored challenge. While machine learning and deep learning techniques have shown promise in credit scoring, their comparative performance in the context of student loans has not been thoroughly investigated. This study aims to evaluate and compare the predictive effectiveness of four supervised learning models—such as Random Forest, Gradient Boosting, Support Vector Machine, and Deep Neural Network (implemented with PyTorch version 2.6.0)—in forecasting student credit eligibility. Primary data were collected from 1024 university students through structured surveys covering academic, financial, and personal variables. The models were trained and tested on the same dataset and evaluated using a comprehensive set of classification and regression metrics. The findings reveal that each model exhibits distinct strengths. Deep Learning achieved the highest classification accuracy (85.55%), while random forest demonstrated robust performance, particularly in providing balanced results across classification metrics. Gradient Boosting was effective in recall-oriented tasks, and support vector machine demonstrated strong precision for the positive class, although its recall was lower compared to other models. The study highlights the importance of aligning model selection with specific application goals, such as prioritizing accuracy, recall, or interpretability. It offers practical implications for financial institutions and universities in developing machine learning and deep learning tools for student loan eligibility prediction. Future research should consider longitudinal data, behavioral factors, and hybrid modeling approaches to further optimize predictive performance in educational finance. Full article

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24 pages, 2318 KiB

Open AccessFeature PaperArticle

Historical Perspectives in Volatility Forecasting Methods with Machine Learning

by Zhiang Qiu, Clemens Kownatzki, Fabien Scalzo and Eun Sang Cha

Risks 2025, 13(5), 98; https://doi.org/10.3390/risks13050098 - 20 May 2025

Abstract

Volatility forecasting for financial institutions plays a pivotal role across a wide range of domains, such as risk management, option pricing, and market making. For instance, banks can incorporate volatility forecasts into stress testing frameworks to ensure they are holding sufficient capital during [...] Read more.

Volatility forecasting for financial institutions plays a pivotal role across a wide range of domains, such as risk management, option pricing, and market making. For instance, banks can incorporate volatility forecasts into stress testing frameworks to ensure they are holding sufficient capital during extreme market conditions. However, volatility forecasting is challenging because volatility can only be estimated, and different factors influence volatility, ranging from macroeconomic indicators to investor sentiments. While recent works show promising advances in machine learning and artificial intelligence for volatility forecasting, a comprehensive assessment of current statistical and learning-based methods is lacking. Thus, this paper aims to provide a comprehensive survey of the historical evolution of volatility forecasting with a comparative benchmark of key landmark models, such as implied volatility, GARCH, LSTM, and Transformer. We open-source our benchmark code to further research in learning-based methods for volatility forecasting. Full article

(This article belongs to the Special Issue Volatility Modeling in Financial Market)

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22 pages, 503 KiB

Open AccessArticle

Breaking Barriers: Gender Diversity, ESG, and Corporate Misconduct in the GCC Region

by Laila Aladwey, Mohamed Fawzy Mohamed Elsayed and Ahmed Diab

Risks 2025, 13(5), 97; https://doi.org/10.3390/risks13050097 - 15 May 2025

Viewed by 155

Abstract

Our study explores how ESG performance affects corporate misconduct (CM) in Gulf Cooperation Council (GCC) firms and whether having more women on corporate boards influences this relationship. Using logistic regression and using data collected from GCC firms, we analyse the moderating effect of [...] Read more.

Our study explores how ESG performance affects corporate misconduct (CM) in Gulf Cooperation Council (GCC) firms and whether having more women on corporate boards influences this relationship. Using logistic regression and using data collected from GCC firms, we analyse the moderating effect of board gender diversity (BGD) on the relationship between ESG and CM. Our findings show that strong ESG performance reduces CM, and greater BGD further decreases misconduct. Moreover, gender-diverse boards strengthen the link between ESG and lower CM rates. This study contributes to the literature by examining how BGD influences the ESG-CM relationship in the GCC region. The current findings are valuable for investors, businesses, and policymakers. Investors should prioritize companies with strong ESG practices and diverse boards to minimize the risks they might face. Businesses should integrate female directors on boards to enhance ethical practices. Policymakers can promote corporate responsibility by incentivizing gender diversity and ESG adoption, which is crucial for a more transparent and accountable business environment. Full article

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

25 pages, 3880 KiB

Open AccessArticle

The Role of Digital Financial Services in Narrowing the Gender Gap in Low–Middle-Income Economies: A Bayesian Machine Learning Approach

by Alicia Fernanda Galindo-Manrique and Nuria Patricia Rojas-Vargas

Risks 2025, 13(5), 96; https://doi.org/10.3390/risks13050096 - 14 May 2025

Viewed by 155

Abstract

Women in emerging economies face unique constraints rooted in cultural norms, socio-economic disparities, and limited access to education and technology. Narrowing the digital gender gap by ensuring access to financial services may reduce the economic inequalities for women in these countries. This study [...] Read more.

Women in emerging economies face unique constraints rooted in cultural norms, socio-economic disparities, and limited access to education and technology. Narrowing the digital gender gap by ensuring access to financial services may reduce the economic inequalities for women in these countries. This study examines the influence of digital finance in narrowing the gender gap, guided by the research question: To what extent do digital financial services contribute to narrowing the gender gap in access to and usage of financial services in low-and middle-income economies? Gender inclusion was measured by the ratio of accounts owned by women over the total number of accounts. Digital financial inclusion was constructed based on eight components: mobile money account, storing money in financial institutions, Internet access, mobile phone owned, savings, savings in financial institutions, making or receiving a digital payment, and mobile phone or use of the Internet for shopping. A Bayesian regression approach was computed using the Global Findex Database data for 73 countries classified as low and lower-middle-income economies from 2011 to 2022. The Machine Learning approach evaluates the model’s ability to predict women’s autonomy and the role of digital finance. The results show that digital financial services would reduce the gender gap in low-income economies while augmenting the number of open accounts, especially for women. The results aid in the establishment of policies to reduce the gender gap. These results are relevant to the UNSDG agenda, mainly Goal 5 and Goal 10. Full article

(This article belongs to the Special Issue Applied Econometrics and International Finance: Analysis, Modeling, and Development)

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22 pages, 426 KiB

Open AccessArticle

Uncovering Systemic Risk in ASEAN Corporations: A Framework Based on Graph Theory and Hidden Models

by Marc Cortés Rufé, Jordi Martí Pidelaserra and Cecilia Kindelán Amorrich

Risks 2025, 13(5), 95; https://doi.org/10.3390/risks13050095 - 13 May 2025

Viewed by 192

Abstract

In the context of an ever-evolving global economy, ASEAN companies face dynamic systemic risk that reshapes their financial interrelationships. This study examines the transmission of these risks using advanced graph theory techniques, particularly the measurement of eigenvector centrality based on Euclidean distances, combined [...] Read more.

In the context of an ever-evolving global economy, ASEAN companies face dynamic systemic risk that reshapes their financial interrelationships. This study examines the transmission of these risks using advanced graph theory techniques, particularly the measurement of eigenvector centrality based on Euclidean distances, combined with a hidden model that incorporates macroeconomic variables, such as GDP. The research focuses on identifying critical nodes within the corporate network, evaluating their contagion potential—both in terms of reinforcing resilience and amplifying vulnerabilities—and analyzing the influence of external factors on the network’s structure and behavior. The findings offer an innovative framework for managing systemic risk and provide strategic guidelines for the formulation of economic policies in emerging ASEAN markets. Full article

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

16 pages, 1027 KiB

Open AccessArticle

The Determinants of Reward-Based Crowdfunding Success in Africa

by Lenny Phulong Mamaro, Athenia Bongani Sibindi and Ntwanano Jethro Godi

Risks 2025, 13(5), 94; https://doi.org/10.3390/risks13050094 - 12 May 2025

Viewed by 119

Abstract

This study focused on investigating the factors that drive reward-based crowdfunding in Africa, particularly considering the increasing limitations that entrepreneurs face in accessing traditional financial resources globally, by analysing 215 crowdfunding projects from prominent platforms like Kickstarter, IndieGoGo, and Fundraised. The research aimed [...] Read more.

This study focused on investigating the factors that drive reward-based crowdfunding in Africa, particularly considering the increasing limitations that entrepreneurs face in accessing traditional financial resources globally, by analysing 215 crowdfunding projects from prominent platforms like Kickstarter, IndieGoGo, and Fundraised. The research aimed to identify the key drivers of crowdfunding success. The results from an econometric logistic regression analysis revealed that while images, longer campaign durations, and videos positively influenced crowdfunding, they did not significantly contribute to achieving success. In contrast, the number of backers showed a positive and significant impact on outcomes, whereas the targeted funding amount negatively influenced the success. Notably, the presence of spelling errors was found to have a positive, though statistically insignificant, relationship with crowdfunding success. These findings enhance the existing literature on crowdfunding and offer valuable insights into concepts such as information asymmetry and signalling theory within the context of reward-based crowdfunding. Full article

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25 pages, 4566 KiB

Open AccessFeature PaperArticle

How Do Asymmetric Oil Prices and Economic Policy Uncertainty Shapes Stock Returns Across Oil Importing and Exporting Countries? Evidence from Instrumental Variable Quantile Regression Approach

by Aman Bilal, Shakeel Ahmed, Hassan Zada, Eleftherios Thalassinos and Muhammad Hassaan Nawaz

Risks 2025, 13(5), 93; https://doi.org/10.3390/risks13050093 - 9 May 2025

Viewed by 396

Abstract

This study employs asymmetric quantile regression to investigate the asymmetric impact of WTI crude oil prices and economic policy uncertainty (EPU) on stock market returns from May 2014 to December 2024 in oil-importing (China, India, Germany, Italy, Japan, USA, and South Korea) and [...] Read more.

This study employs asymmetric quantile regression to investigate the asymmetric impact of WTI crude oil prices and economic policy uncertainty (EPU) on stock market returns from May 2014 to December 2024 in oil-importing (China, India, Germany, Italy, Japan, USA, and South Korea) and oil-exporting (Saudi Arabia, Russia, Iraq, Canada, and the United Arab Emirates) countries. The findings reveal that an increase in oil prices significantly impacts the returns of all countries. For oil-importing countries, an increase in oil prices consistently exhibits a positive impact, with insignificant effects in lower and medium quantiles and significant effects in higher quantiles. Conversely, a decrease in oil prices generally decreases stock market returns across all quantiles. This study offers valuable insights for investors to manage risks and improve the predictability of oil price fluctuations. It also provides strategies and policy implications for capitalists and decision-makers. By addressing contemporary issues and using up-to-date data, the study supports financial institutions and portfolio managers in formulating effective strategies. Full article

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29 pages, 1872 KiB

Open AccessArticle

Responding to Climate Policy Risk Through the Dynamic Role of Green Innovation: Evidence from Carbon Information Disclosure in Emerging Markets

by Runyu Liu, Mara Ridhuan Che Abdul Rahman and Ainul Huda Jamil

Risks 2025, 13(5), 92; https://doi.org/10.3390/risks13050092 - 9 May 2025

Viewed by 229

Abstract

This study investigates how firms in emerging markets respond to climate policy risk, with a particular focus on the dynamic role of green innovation in shaping carbon information disclosure. Using a difference-in-differences (DID) framework, we examine the impact of China’s 2018 carbon reporting [...] Read more.

This study investigates how firms in emerging markets respond to climate policy risk, with a particular focus on the dynamic role of green innovation in shaping carbon information disclosure. Using a difference-in-differences (DID) framework, we examine the impact of China’s 2018 carbon reporting policy, which represents an institutionally significant but non-mandatory regulatory intervention, on the disclosure behaviors of A-share listed firms from 2013 to 2022. The results show that the policy significantly increased firms’ attention to carbon information disclosure, especially among those with limited green innovation capacity. In contrast, firms with stronger innovation capabilities exhibited more stable disclosure practices, suggesting a buffering effect against regulatory uncertainty. Further analysis reveals that the moderating effect of green innovation changes over time, as innovation-oriented firms gradually adjust their disclosure strategies in response to evolving policy expectations. These findings highlight green innovation as a key internal resource that enables firms to strategically adapt to climate policy risks. This study contributes to the literature on climate risk management and corporate sustainability by providing empirical evidence on how dynamic capabilities shape disclosure outcomes and risk management strategies under changing regulatory conditions. Full article

(This article belongs to the Special Issue Climate Change and Economic Impact: Mitigating Risks and Capitalizing on Emerging Opportunities)

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16 pages, 2181 KiB

Open AccessArticle

Achievement of Islamic Finance Objectives: Evidence from the UAE Islamic Banking Industry

by Muhammad Hanif

Risks 2025, 13(5), 91; https://doi.org/10.3390/risks13050091 - 8 May 2025

Viewed by 571

Abstract

The study documents the achievements of the Islamic Banking Services Industry (IBSI) in light of Islamic finance objectives (including commercial performance, financial stability, and wealth distribution). A balance sheet analysis of IBSI in the United Arab Emirates (UAE) for 33 quarters (2013 Q4–2021 [...] Read more.

The study documents the achievements of the Islamic Banking Services Industry (IBSI) in light of Islamic finance objectives (including commercial performance, financial stability, and wealth distribution). A balance sheet analysis of IBSI in the United Arab Emirates (UAE) for 33 quarters (2013 Q4–2021 Q3) is conducted, focusing on sources and uses of funds, as well as documentation of commercial performance. The findings suggest that the UAE IBSI has remained successful in achieving its micro/primary objectives (commercial performance) and made progress towards partial achievement of its macro/intermediate objectives (financial stability and equitable wealth distribution). While evidence suggests achievements in the area of financial stability, the aspect of equity in wealth distribution requires more focus. The study recommends that regulators develop a legal framework focusing on the business models for IBSI, aimed at achieving broader economic objectives. It is also recommended that managers of UAE IBSI include profit and loss-sharing contracts in deposit collection, financing and investment portfolios. The contribution to the literature includes the documentation of findings on the achievements of UAE IBSI in financial performance, as well as its broader economic objectives within the Islamic financial system. Full article

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11 pages, 431 KiB

Open AccessArticle

Structural Exchange Rate Modeling: The Case of a Small Open Economy

by Anton Kuzmin

Risks 2025, 13(5), 90; https://doi.org/10.3390/risks13050090 - 8 May 2025

Viewed by 179

Abstract

A new mathematical structural model of the exchange rate and a new nonlinear multifactorial dependence of the exchange rate dynamics on a wide system of fundamental economic factors are the main results of the work. The model includes a wide system of economic [...] Read more.

A new mathematical structural model of the exchange rate and a new nonlinear multifactorial dependence of the exchange rate dynamics on a wide system of fundamental economic factors are the main results of the work. The model includes a wide system of economic factors based on the balance of payments. The proposed mathematical modeling methodology (International Flows Equilibrium Exchange Rate; IFEER) makes it possible to include additional fundamental factors in the model. This is the main theoretical achievement of this study, which also has a clear practical application. The constructed model made it possible to reveal the mechanism and structure of dynamic pricing of the exchange rate from updated and improved positions. The period of the last completed major financial and economic crisis in recent Russian history was chosen for conducting empirical research. During the period under review, the proposed model (applied to the dynamics of the Russian ruble against the US dollar exchange rate) showed its sufficiently high practical applicability, which is confirmed by the calculated quality indicators. Full article

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32 pages, 1601 KiB

Open AccessArticle

Assessing Vertical Equity in Defined Benefit Pension Plans: An Application to Switzerland

by Tanja Kirn and Gijs Dekkers

Risks 2025, 13(5), 89; https://doi.org/10.3390/risks13050089 - 8 May 2025

Viewed by 254

Abstract

This paper establishes a theoretical link between actuarial neutrality and the Oaxaca–Blinder decomposition to empirically assess vertical equity in public defined-benefit schemes. We demonstrate how this approach can be generalized to non-linear functions, point systems, and notional accounts. We use an aligned dynamic [...] Read more.

This paper establishes a theoretical link between actuarial neutrality and the Oaxaca–Blinder decomposition to empirically assess vertical equity in public defined-benefit schemes. We demonstrate how this approach can be generalized to non-linear functions, point systems, and notional accounts. We use an aligned dynamic microsimulation model to apply this method to the first pillar of the Swiss pension system and highlight the following three key effects: (1) the impact of the accrual rate on vertical equity; (2) the assessment of actuarial neutrality through the comparison of migrants with the non-migrant population; and (3) vertical equity across marital statuses. Our findings indicate that changing societal trends, such as increased migration, female labor participation, and the rise in non-marital unions, may alter the extent of vertical equity. This has significant implications for actuarial risk management, as a higher degree of vertical equity is associated with increased pension expenses, thereby raising the financial sustainability risk of the pension system. Future research should explore these dynamics to ensure that pension systems remain both equitable and financially sustainable in the face of evolving societal trends. Full article

(This article belongs to the Special Issue Risk Analysis in Insurance and Pensions)

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21 pages, 450 KiB

Open AccessArticle

Life Insurance Completeness: A Path to Hedging Mortality and Achieving Financial Optimization

by Jaime A. Londoño

Risks 2025, 13(5), 88; https://doi.org/10.3390/risks13050088 - 6 May 2025

Viewed by 257

Abstract

This paper explores optimal consumption and investment strategies for agents facing mortality risk within a complete financial market. Departing from traditional frameworks, we leverage state-dependent utility theory, discounted by the state–price process, to compare consumption streams and utilize life insurance as a strategic [...] Read more.

This paper explores optimal consumption and investment strategies for agents facing mortality risk within a complete financial market. Departing from traditional frameworks, we leverage state-dependent utility theory, discounted by the state–price process, to compare consumption streams and utilize life insurance as a strategic hedging instrument. To model the ability of insurance companies to hedge the mortality risk of consumer pools, we introduce the concept of life insurance completeness, allowing individuals to achieve optimal consumption even in scenarios involving negative wealth. Our model relaxes the stringent integrability conditions commonly imposed in the literature, offering a more economically grounded approach to valuation and hedging. We derive a general solution to the optimization problem using martingale techniques under minimal assumptions, demonstrating that life insurance primarily serves as a mortality risk hedge rather than a bequest motive. This perspective resolves longstanding theoretical and empirical challenges, notably the annuity puzzle, by illustrating that optimal consumption and investment, in the absence of labor income, do not necessitate annuities or other life insurance policies. Our key contributions include (1) extending valuation frameworks to encompass prepaid insurance and less restrictive integrability criteria, (2) establishing life insurance completeness for effective mortality risk hedging, (3) demonstrating the feasibility of optimal consumption under negative wealth and state-dependent preferences, and (4) offering a resolution to the annuity puzzle that aligns with empirical observations. Full article

24 pages, 2160 KiB

Open AccessArticle

Deciphering the Risk–Return Dynamics of Pharmaceutical Companies Using the GARCH-M Model

by Arvinder Kaur and Kavita Chavali

Risks 2025, 13(5), 87; https://doi.org/10.3390/risks13050087 - 1 May 2025

Viewed by 217

Abstract

This study focuses on the precise forecasting of stock price movement to determine returns, diversify risk, and demystify existing opportunities. It also aims to gauge the difference in terms of the stock volatility of various pharma companies before and during the pandemic era. [...] Read more.

This study focuses on the precise forecasting of stock price movement to determine returns, diversify risk, and demystify existing opportunities. It also aims to gauge the difference in terms of the stock volatility of various pharma companies before and during the pandemic era. The prediction of stock market volatility and associated risks is demonstrated by using the GARCH-M model. A sample is collected by clustering daily closing and opening prices from the official websites of the top ten pharmaceutical companies listed on the Bombay Stock Exchange for ten years, from 2012 to 2023. It is evident when using the GARCH-M model, which indicates pharma stock volatility clustering before the COVID-19 pandemic, that a significant relationship is present between risk and return and that these could cause future volatility and significant price movements. Before the COVID-19 pandemic, investors had time to adjust to market conditions, as the volatility was constant but less sensitive to transient shocks. Though it passed faster than ever, the COVID-19 pandemic produced significant market instability. The findings suggest that, especially before the COVID-19 pandemic, the high GARCH(-1) coefficients held Merton’s ICAPM, which maintains that past volatility shapes future returns. This sort of activity is compatible with the way financial markets usually operate. The findings suggest that volatility rose after the COVID-19 pandemic, but this was more because of changes in government policies and vaccines than because of regular market forces. Pricing patterns are dominated by stock interventions, liquidity constraints, and sentiments during a crisis period when volatility becomes irrelevant. Appropriate decision-making by individual investors, portfolio managers, and policymakers regarding the stock market is possible through effective prediction based on time-series analysis. The GARCH-M model is compatible with predicting future stock price changes efficiently. This study uniquely applies the GARCH-M model to the Indian pharmaceutical sector, offering valuable insights into stock volatility and risk–return dynamics, particularly during the COVID-19 pandemic. Full article

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

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20 pages, 502 KiB

Open AccessArticle

Cooperative Game Theory of Hierarchies: One Approach to Solving the Low-Risk Puzzle?

by Tobias Hiller

Risks 2025, 13(5), 86; https://doi.org/10.3390/risks13050086 - 30 Apr 2025

Viewed by 256

Abstract

In this article, we extend the application of cooperative game theory to the so-called low-risk puzzle. Specifically, we apply concepts that consider hierarchies on the assets in the allocation of portfolio risk. These hierarchies have not previously been considered in portfolio risk allocation [...] Read more.

In this article, we extend the application of cooperative game theory to the so-called low-risk puzzle. Specifically, we apply concepts that consider hierarchies on the assets in the allocation of portfolio risk. These hierarchies have not previously been considered in portfolio risk allocation using cooperative game theory. We demonstrate our idea through a simulation study. Our results show that considering hierarchies can contribute to solving the low-risk puzzle. Our findings may advance further developments in portfolio theory. Full article

(This article belongs to the Special Issue Portfolio Theory, Financial Risk Analysis and Applications)

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23 pages, 4798 KiB

Open AccessArticle

Rating the Impact of Risks in Banking on Performance: Utilizing the Adaptive Neural Network-Based Fuzzy Inference System (ANFIS)

by Riyadh Mehdi, Ibrahim Elsiddig Ahmed and Elfadil A. Mohamed

Risks 2025, 13(5), 85; https://doi.org/10.3390/risks13050085 - 30 Apr 2025

Viewed by 883

Abstract

This study aims to rate the impact of the three major risks (credit, capital adequacy, and liquidity) on three financial performance measures (return on equity (ROE), earnings per share (EPS), and price-earnings ratio (PER)). This study stands out as one of the few [...] Read more.

This study aims to rate the impact of the three major risks (credit, capital adequacy, and liquidity) on three financial performance measures (return on equity (ROE), earnings per share (EPS), and price-earnings ratio (PER)). This study stands out as one of the few in its field, and the only one focusing on banks in the Middle East and Africa, to employ the adaptive neural network-based fuzzy inference system (ANFIS) that combines neural networks and fuzzy logic systems. The significance of this study lies in its comprehensive coverage of major risks and performance variables and its application of highly technical, sophisticated, and precise AI techniques (ANFIS). The main findings indicate that credit risk, as measured by the non-performing loans (NPL) has significant impact on both ROE and EPS. Liquidity risk comes second in importance for ROE and EPS, with the loan-deposit ratio (LDR) being the dominant component. In contrast, liquidity risk is the most significant determinant of PER, followed by capital adequacy. Our results also show that CAR, LDR, and NPL are the most significant risk components of capital adequacy, liquidity, and credit risks, respectively. The study contributes to business knowledge by applying the ANFIS technique as an accurate predictor of risk rating. Future research will explore the relationship between risks and macroeconomic indicators and differences among countries. Full article

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14 pages, 362 KiB

Open AccessArticle

Optimizing Moral Hazard Management in Health Insurance Through Mathematical Modeling of Quasi-Arbitrage

by Lianlian Zhou, Anshui Li and Jue Lu

Risks 2025, 13(5), 84; https://doi.org/10.3390/risks13050084 - 28 Apr 2025

Viewed by 221

Abstract

Moral hazard in health insurance arises when insured individuals are incentivized to over-utilize healthcare services, especially when they face low out-of-pocket costs. While existing literature primarily addresses moral hazard through qualitative studies, this paper introduces a quantitative approach by developing a mathematical model [...] Read more.

Moral hazard in health insurance arises when insured individuals are incentivized to over-utilize healthcare services, especially when they face low out-of-pocket costs. While existing literature primarily addresses moral hazard through qualitative studies, this paper introduces a quantitative approach by developing a mathematical model based on quasi-arbitrage conditions. The model optimizes health insurance design, focusing on the transition from Low-Deductible Health Plans (LDHPs) to High-Deductible Health Plans (HDHPs), and seeks to mitigate moral hazard by aligning the interests of both insurers and insured. Our analysis demonstrates how setting appropriate deductible levels and offering targeted premium reductions can encourage insured to adopt HDHPs while maintaining insurer profitability. The findings contribute to the theoretical framework of moral hazard mitigation in health insurance and offer actionable insights for policy design. Full article

(This article belongs to the Special Issue Financial Risk, Actuarial Science, and Applications of AI Techniques)

16 pages, 498 KiB

Open AccessArticle

Can Unrealistic Optimism Among Consumers Precipitate Economic Recessions?

by Hyun-Soo Doh and Jiahao Pan

Risks 2025, 13(5), 83; https://doi.org/10.3390/risks13050083 - 26 Apr 2025

Viewed by 298

Abstract

This paper examines the macroeconomic implications of unrealistic optimism, a psychological bias that has been largely overlooked in economic models. While traditional models often link optimism to speculative bubbles and excessive risk taking, this study challenges that view by demonstrating that unrealistic optimism [...] Read more.

This paper examines the macroeconomic implications of unrealistic optimism, a psychological bias that has been largely overlooked in economic models. While traditional models often link optimism to speculative bubbles and excessive risk taking, this study challenges that view by demonstrating that unrealistic optimism may rather accelerate recessions. Specifically, we develop a model in which consumers, under the influence of unrealistic optimism, believe that negative aggregate shocks will affect others but not themselves. This misjudgment leads to a premature fall in output prices, reducing production and triggering recessions. Additionally, we show that government intervention, when optimally timed, can mitigate the adverse effects of unrealistic optimism, offering important policy implications for stabilizing economies. By highlighting the possibility of optimism-induced downturns, this paper provides new insights into behavioral macroeconomics and offers a novel perspective on policy design. Full article

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28 pages, 2632 KiB

Open AccessFeature PaperArticle

A Neural Network Approach for Pricing Correlated Health Risks

by Alessandro G. Laporta, Susanna Levantesi and Lea Petrella

Risks 2025, 13(5), 82; https://doi.org/10.3390/risks13050082 - 24 Apr 2025

Viewed by 353

Abstract

In recent years, the actuarial literature involving machine learning in insurance pricing has flourished. However, most actuarial machine learning research focuses on property and casualty insurance, while using such techniques in health insurance is yet to be explored. In this paper, we discuss [...] Read more.

In recent years, the actuarial literature involving machine learning in insurance pricing has flourished. However, most actuarial machine learning research focuses on property and casualty insurance, while using such techniques in health insurance is yet to be explored. In this paper, we discuss the use of neural networks to set the price of health insurance coverage following the structure of a classical frequency-severity model. In particular, we propose negative multinomial neural networks to jointly model the frequency of possibly correlated medical claims and Gamma neural networks to estimate the expected claim severity. Using a case study based on real-world health insurance data, we highlight the overall better performance of the neural network models with respect to more established regression models, both in terms of accuracy (frequency models achieve an average out-of-sample deviance of 8.54 compared to 8.61 for classical regressions) and risk diversification, as indicated by the ABC lift metric, which is

5.62 \times 10^{- 3}

for neural networks versus

8.27 \times 10^{- 3}

for traditional models. Full article

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

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50 pages, 6857 KiB

Open AccessArticle

The Impact of Economic Policies on Housing Prices: Approximations and Predictions in the UK, the US, France, and Switzerland from the 1980s to Today

by Nicolas Houlié

Risks 2025, 13(5), 81; https://doi.org/10.3390/risks13050081 - 23 Apr 2025

Viewed by 217

Abstract

I show that house prices can be modeled using machine learning (kNN and tree-bagging) and a small dataset composed of macroeconomic factors (MEF), including an inflation metric (CPI), US Treasury rates (10-yr), Gross Domestic Product (GDP), and portfolio size of central banks (ECB, [...] Read more.

I show that house prices can be modeled using machine learning (kNN and tree-bagging) and a small dataset composed of macroeconomic factors (MEF), including an inflation metric (CPI), US Treasury rates (10-yr), Gross Domestic Product (GDP), and portfolio size of central banks (ECB, FED). This set of parameters covers all the parties involved in a transaction (buyer, seller, and financing facility) while ignoring the intrinsic properties of each asset and encompassing local (inflation) and liquidity issues that may impede each transaction composing a market. The model here takes the point of view of a real estate trader who is interested in both the financing and the price of the transaction. Machine learning allows for the discrimination of two periods within the dataset. First, and up to 2015, I show that, although the US Treasury rates level is the most critical parameter to explain the change of house-price indices, other macroeconomic factors (e.g., consumer price indices) are essential to include in the modeling because they highlight the degree of openness of an economy and the contribution of the economic context to price changes. Second, and for the period from 2015 to today, I show that, to explain the most recent price evolution, it is necessary to include the datasets of the European Central Bank programs, which were designed to support the economy since the beginning of the 2010s. Indeed, unconventional policies of central banks may have allowed some institutional investors to arbitrage between real estate returns and other bond markets (sovereign and corporate). Finally, to assess the models’ relative performances, I performed various sensitivity tests, which tend to constrain the possibilities of each approach for each need. I also show that some models can predict the evolution of prices over the next 4 quarters with uncertainties that outperform existing index uncertainties. Full article

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12 pages, 2274 KiB

Open AccessArticle

A New Approach on Country Risk Monitoring

by Christos E. Kountzakis and Christos Floros

Risks 2025, 13(5), 80; https://doi.org/10.3390/risks13050080 - 22 Apr 2025

Viewed by 274

Abstract

Most of indexes regarding Credit Rating of the national debt bonds are associated to Gross National Product, which involves the well-known Keynesian Multiplicator of the IS-LM Equilibrium. Specifically, a common way of Sovereign Debt evaluation is its percentage of the Gross National Product [...] Read more.

Most of indexes regarding Credit Rating of the national debt bonds are associated to Gross National Product, which involves the well-known Keynesian Multiplicator of the IS-LM Equilibrium. Specifically, a common way of Sovereign Debt evaluation is its percentage of the Gross National Product in terms of a spot value. Another index is the spot value of the percentage of the annual interest rate payments of the state to the owners of sovereign debt. These indexes provide an inefficient evaluation of the national debt and moreover they are sensitive in their calculative aspect. Hence, we propose another index of national debt evaluation, which is more realistic, since public debt is a part of the balance sheet of the state itself. Moreover, this index may be translated into growth variables of the national economy. Since Gross National Product relies on consumption of the Economy, more consumption implies an ’illusion’ about sovereign debt. On the other hand, this index has limits to its credibility because it depends on the size of the annual investments. Full article

(This article belongs to the Special Issue Computational Methods and Models in the Financial Risk Management Process)

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Risks, Volume 13, Issue 5 (May 2025) – 20 articles

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