Risks

33 pages, 2148 KiB

Open AccessArticle

Exploring Corporate Capital Structure and Overleveraging in the Pharmaceutical Industry

by Samar Issa and Hussein Issa

Risks 2025, 13(2), 26; https://doi.org/10.3390/risks13020026 (registering DOI) - 2 Feb 2025

This paper applies an empirical model of corporate capital structure, optimal debt, and overleveraging to estimate overleveraging measured as the difference between actual and optimal debt. Estimated using a sample of the twenty largest pharmaceutical firms, covering the time span from 2000 to [...] Read more.

This paper applies an empirical model of corporate capital structure, optimal debt, and overleveraging to estimate overleveraging measured as the difference between actual and optimal debt. Estimated using a sample of the twenty largest pharmaceutical firms, covering the time span from 2000 to 2018, the model sheds light on an industry-specific default risk. The analysis presented in this paper reveals a concerning trend in the pharmaceutical industry, with corporate excess debt steadily increasing over the past two decades, particularly peaking during the 2008 crisis and after 2013. These findings underscore the critical role of excess debt in exacerbating financial instability and highlight the pharmaceutical sector’s unique challenges, including high R&D intensity and regulatory pressures. By quantifying overleveraging and linking it to financial risk, the paper offers valuable policy implications, emphasizing the need for proactive management of optimal debt levels to mitigate default risks and enhance macroeconomic resilience. Full article

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

25 pages, 929 KiB

Open AccessArticle

An IID Test for Functional Time Series with Applications to High-Frequency VIX Index Data

by Xin Huang, Han Lin Shang and Tak Kuen Siu

Risks 2025, 13(2), 25; https://doi.org/10.3390/risks13020025 - 30 Jan 2025

Abstract

To address a key issue in functional time series analysis on testing the randomness of an observed series, we propose an IID test for functional time series by generalizing the Brock–Dechert–Scheinkman (BDS) test, which is commonly used for testing nonlinear independence. Similarly to [...] Read more.

To address a key issue in functional time series analysis on testing the randomness of an observed series, we propose an IID test for functional time series by generalizing the Brock–Dechert–Scheinkman (BDS) test, which is commonly used for testing nonlinear independence. Similarly to the BDS test, the proposed functional BDS test can be used to evaluate the suitability of prediction models as a model specification test and to detect nonlinear structures as a nonlinearity test. We establish asymptotic results for the test statistic of the proposed test in a generic separate Hilbert space and show that it enjoys the same asymptotic properties as those for the univariate case. To address the practical issue of selecting hyperparameters, we provide the recommended range of the hyperparameters. Using empirical data on the VIX index, empirical studies are conducted that feature the applications of the proposed test to evaluate the adequacy of the fAR

(1)

and fGARCH

(1, 1)

models in fitting the daily curves of cumulative intraday returns (CIDR) of the index. The results reveal that the proposed test remedies some shortcomings of the existing independence test. Specifically, the proposed test can detect nonlinear temporal structures, while the existing test can only detect linear structures. Full article

72 pages, 725 KiB

Open AccessArticle

Sectoral Counter-Cyclical Approach to Financial Risk Management Based on CSR for Sustainable Development of Companies

by Uran Zh. Ergeshbaev, Dilobar M. Mavlyanova, Yulia G. Leskova, Elena G. Popkova and Elena S. Petrenko

Risks 2025, 13(2), 24; https://doi.org/10.3390/risks13020024 - 30 Jan 2025

Abstract

This research determines the contribution of Corporate Social Responsibility (CSR) to reducing financial risks and, consequently, to the sustainable development of companies in different sectors of the economy and at different phases of the economic cycle (using Russia as an example). The informational [...] Read more.

This research determines the contribution of Corporate Social Responsibility (CSR) to reducing financial risks and, consequently, to the sustainable development of companies in different sectors of the economy and at different phases of the economic cycle (using Russia as an example). The informational and empirical base comprises data on the dynamics of stock prices of sectoral indices of the Moscow Exchange’s total return “gross” (in Russian rubles): oil and gas, electricity, telecommunications, metals and mining, finance, consumer sector (retail trade), chemicals and petrochemicals, and transportation, as well as the “Responsibility and Openness” index in 2019 (before the crises), in 2020 (COVID-19 crisis), 2022 (sanction crisis), and 2024 (Russia’s economic growth). Economic–mathematical models, compiled through regression analysis, showed that the contribution of CSR to reducing the financial risks of companies is highly differentiated among economic sectors and phases of the economic cycle. The research presents a new sectoral perspective on counter-cyclical management of the financial risks of companies through CSR, enabling a deeper study of the cause-and-effect relationships of such management for the sustainable development of companies from different economic sectors. This is the theoretical significance of this research, its novelty, and its contribution to the literature. The research has practical significance, revealing previously unknown best practices for the sustainable development of companies from different economic sectors of Russia across different phases of the economic cycle. The systematized experience will be useful for forecasting the financial risks of companies during future economic crises in Russia and improving the practice of planning and organizing the financial risk management of Russian companies through CSR. The authors’ conclusions have managerial significance because they will help enhance the flexibility and efficiency of corporate financial risk management by considering the sectoral specifics and cyclical nature of the economy when implementing CSR. Full article

19 pages, 3093 KiB

Open AccessArticle

Redesigning Home Reversion Products to Empower Retirement for Singapore’s Public Flat Owners

by Koon Shing Kwong, Jing Rong Goh, Jordan Jie Xin Lee and Ting Lin Collin Chua

Risks 2025, 13(2), 23; https://doi.org/10.3390/risks13020023 - 30 Jan 2025

Abstract

This paper introduces an innovative sell-type home reversion product aimed at monetizing Singapore’s public flats, serving as a new alternative to the existing Singapore Lease Buyback Scheme (LBS). This new product not only retains the LBS’s guaranteed period of residence in the property [...] Read more.

This paper introduces an innovative sell-type home reversion product aimed at monetizing Singapore’s public flats, serving as a new alternative to the existing Singapore Lease Buyback Scheme (LBS). This new product not only retains the LBS’s guaranteed period of residence in the property along with life annuity incomes but also enhances the product features to meet specific homeowner needs, including the ability to age in place, flexibility in retaining part of the property, options for bequests, and guaranteed principal return. By incorporating these additional features, the new product seeks to stimulate greater demand for monetizing public flats among asset-rich but cash-poor homeowners. An actuarial pricing model is developed to establish a transparent and fair framework for justifying the cost of each product feature. Additionally, we present a cost–benefit analysis from both the provider and consumer perspectives to highlight the major contributions of the new product when compared to the LBS. Full article

28 pages, 938 KiB

Open AccessArticle

A Different Risk–Return Relationship

by Aydin Selim Oksoy, Matthew R. Farrell and Shaomin Li

Risks 2025, 13(2), 22; https://doi.org/10.3390/risks13020022 - 27 Jan 2025

Abstract

We challenge the widely accepted premise that the valuation of an early-stage firm is simply the capital invested (USD) divided by the equity received (%). Instead, we argue that this calculation determines the break-even point for the investor; for example, investing USD 1.0 [...] Read more.

We challenge the widely accepted premise that the valuation of an early-stage firm is simply the capital invested (USD) divided by the equity received (%). Instead, we argue that this calculation determines the break-even point for the investor; for example, investing USD 1.0 in exchange for a 10% equity sets a firm-level free cash flow target of USD 10.0, resulting in a 0% return for the investor. The design of our study is that of a descriptive analysis of the phenomenon, based on three assumptions: that angel investing is a two-issue negotiation, that negotiation positions are communicated sequentially from capital to equity, and that the capital is fixed to a strategic trajectory. We note that when pausing the negotiation once a strategic trajectory (and thus capital) has been defined, utilizing the break-even point as the main reference point provides a structure that can serve as a guiding barometer for negotiators, as they evaluate their options across the full range of equity greater than 0% and less than 100%. We draw attention to the diminishing benefit of the marginal equity percentage point [diminishing at a rate of (−1/x²)] for the investor to break even on their investment. This relationship tracks to the equation [value = 1/equity], which presents the full option set for any offer, once the capital is determined. Our study provides the practitioner with the subtle benefit of situational awareness and the scholar with a logical foundation for future research. Full article

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

23 pages, 1263 KiB

Open AccessArticle

Turning Points in the Core–Periphery Displacement of Systemic Risk in the Eurozone: Constrained Weighted Compositional Clustering

by Anna Maria Fiori and Germà Coenders

Risks 2025, 13(2), 21; https://doi.org/10.3390/risks13020021 - 24 Jan 2025

Abstract

Investigating how systemic risk originates and spreads across the financial system poses an inherently compositional question, i.e., a question concerning the joint distribution of relative risk share across several interdependent contributors. To address this question, we propose a weighted compositional clustering approach aimed [...] Read more.

Investigating how systemic risk originates and spreads across the financial system poses an inherently compositional question, i.e., a question concerning the joint distribution of relative risk share across several interdependent contributors. To address this question, we propose a weighted compositional clustering approach aimed at tackling the trajectories and turning points of systemic risk in the Eurozone, from both a chronological and a geographical perspective. The cluster profiles emerging from our analysis indicate a progressive shift from Northern Europe towards the Euro-Mediterranean region in the coordinate center of systemic risk compositions. This shift matures as the outcome of complex interactions between core and peripheral EU countries that compositional methods have the merit of capturing and unifying in a self-contained multivariate framework. Full article

(This article belongs to the Special Issue Systemic Risk in the Financial System: New Developments and Challenges)

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16 pages, 1521 KiB

Open AccessArticle

Data Mining for the Adjustment of Credit Scoring Models in Solidarity Economy Entities: A Methodology for Addressing Class Imbalances

by Ivan Mauricio Bermudez Vera, Jaime Mosquera Restrepo and Diego Fernando Manotas-Duque

Risks 2025, 13(2), 20; https://doi.org/10.3390/risks13020020 - 22 Jan 2025

Abstract

This study addresses the quantification of credit risk in solidarity economy entities, proposing a new methodology to redefine the concept of a “default” in the frequent situations of extreme class imbalances. The objective is to develop and evaluate credit scoring models that enhance [...] Read more.

This study addresses the quantification of credit risk in solidarity economy entities, proposing a new methodology to redefine the concept of a “default” in the frequent situations of extreme class imbalances. The objective is to develop and evaluate credit scoring models that enhance risk management by incorporating internal and external data to assess default risk. Data mining techniques are applied to address class imbalances, redefining the term “default” to include external credit information and increasing the representation of the minority class. The effectiveness of machine learning and statistical models is evaluated using class-balancing methods such as under-sampling, over-sampling, and the Synthetic Minority Over-sampling Technique (SMOTE). The evaluation is based on the Balanced Accuracy metric and the holding power of the performance, ensuring a consistent predictive power of the model while avoiding overfitting. While machine learning methods can improve credit scoring, logistic regression-based models remain effective, especially when combined with class-balancing techniques. It is concluded that a balanced sample in a class size is essential to improve predictive performance. Full article

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

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24 pages, 689 KiB

Open AccessFeature PaperArticle

Modeling the Inter-Arrival Time Between Severe Storms in the United States Using Finite Mixtures

by Ilana Vinnik and Tatjana Miljkovic

Risks 2025, 13(2), 19; https://doi.org/10.3390/risks13020019 - 21 Jan 2025

Abstract

When inter-arrival times between events follow an exponential distribution, this implies a Poisson frequency of events, as both models assume events occur independently and at a constant average rate. However, these assumptions are often violated in real-insurance applications. When the rate at which [...] Read more.

When inter-arrival times between events follow an exponential distribution, this implies a Poisson frequency of events, as both models assume events occur independently and at a constant average rate. However, these assumptions are often violated in real-insurance applications. When the rate at which events occur changes over time, the exponential distribution becomes unsuitable. In this paper, we study the distribution of inter-arrival times of severe storms, which exhibit substantial variability, violating the assumption of a constant average rate. A new approach is proposed for modeling severe storm recurrence patterns using a finite mixture of log-normal distributions. This approach effectively captures both frequent, closely spaced storm events and extended quiet periods, addressing the inherent variability in inter-event durations. Parameter estimation is performed using the Expectation–Maximization algorithm, with model selection validated via the Bayesian information criterion (BIC). To complement the parametric approach, Kaplan–Meier survival analysis was employed to provide non-parametric insights into storm-free intervals. Additionally, a simulation-based framework estimates storm recurrence probabilities and assesses financial risks through probable maximum loss (PML) calculations. The proposed methodology is applied to the Billion-Dollar Weather and Climate Disasters dataset, compiled by the U.S. National Oceanic and Atmospheric Administration (NOAA). The results demonstrate the model’s effectiveness in predicting severe storm recurrence intervals, offering valuable tools for managing risk in the property and casualty insurance industry. Full article

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

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23 pages, 2121 KiB

Open AccessFeature PaperArticle

Evaluating Transition Rules for Enhancing Fairness in Bonus–Malus Systems: An Application to the Saudi Arabian Auto Insurance Market

by Asrar Alyafie, Corina Constantinescu and Jorge Yslas

Risks 2025, 13(1), 18; https://doi.org/10.3390/risks13010018 - 20 Jan 2025

Abstract

A Bonus–Malus System (BMS) is a ratemaking mechanism used in insurance to adjust premiums based on a policyholder’s claim history, with the goal of segmenting risk profiles more accurately. A BMS typically comprises three key components: the number of BMS levels, the transition [...] Read more.

A Bonus–Malus System (BMS) is a ratemaking mechanism used in insurance to adjust premiums based on a policyholder’s claim history, with the goal of segmenting risk profiles more accurately. A BMS typically comprises three key components: the number of BMS levels, the transition rules dictating the movements of policyholders within the system, and the relativities used to determine premium adjustments. This paper explores the impact of modifications to these three elements on risk classification, assessed through the mean squared error. The model parameters are calibrated with real-world data from the Saudi auto insurance market. We begin the analysis by focusing on transition rules based solely on claim frequency, a framework in which most implemented BMSs work, including the current Saudi BMS. We then consider transition rules that depend on frequency and severity, in which higher penalties are given for large claim sizes. The results show that increasing the number of levels typically improves risk segmentation but requires balancing practical implementation constraints and that the adequate selection of the penalties is critical to enhancing fairness. Moreover, the study reveals that incorporating a severity-based penalty enhances risk differentiation, especially when there is a dependence between the claim frequency and severity. Full article

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26 pages, 5146 KiB

Open AccessArticle

Automated Bitcoin Trading dApp Using Price Prediction from a Deep Learning Model

by Zhi Zhan Lua, Chee Kiat Seow, Raymond Ching Bon Chan, Yiyu Cai and Qi Cao

Risks 2025, 13(1), 17; https://doi.org/10.3390/risks13010017 - 17 Jan 2025

Abstract

Distributed ledger technology (DLT) and cryptocurrency have revolutionized the financial landscape and relevant applications, particularly in investment opportunities. Despite its growth, the market’s volatility and technical complexities hinder widespread adoption. This study proposes a cryptocurrency trading system powered by advanced machine learning (ML) [...] Read more.

Distributed ledger technology (DLT) and cryptocurrency have revolutionized the financial landscape and relevant applications, particularly in investment opportunities. Despite its growth, the market’s volatility and technical complexities hinder widespread adoption. This study proposes a cryptocurrency trading system powered by advanced machine learning (ML) models to address these challenges. By leveraging random forest (RF), long short-term memory (LSTM), and bi-directional LSTM (Bi-LSTM) models, the cryptocurrency trading system is equipped with strong predictive capacity and is able to optimize trading strategies for Bitcoin. The up-to-date price prediction information obtained by the machine learning model is incorporated by custom oracle contracts and is transmitted to portfolio smart contracts. The integration of smart contracts and on-chain oracles ensures transparency and security, allowing real-time verification of portfolio management. The deployed cryptocurrency trading system performs these actions automatically without human intervention, which greatly reduces barriers to entry for ordinary users and investors. The results demonstrate the feasibility of creating a cryptocurrency trading system, with the LSTM model achieving a return on investment (ROI) of 488.74% for portfolio management during the duration of 9 December 2022 to 23 May 2024. The ROI obtained by the LSTM model is higher than the performance of Bitcoin at 234.68% and that of other benchmarking models with RF and Bi-LSTM over the same timeframe. This approach offers significant cost savings, transparent portfolio management, and a trust-free platform for investors, paving the way for broader cryptocurrency adoption. Future work will focus on enhancing prediction accuracy and achieving greater decentralization. Full article

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21 pages, 477 KiB

Open AccessFeature PaperArticle

Optimal Design of Multi-Asset Options

by Alejandro Balbás, Beatriz Balbás and Raquel Balbás

Risks 2025, 13(1), 16; https://doi.org/10.3390/risks13010016 - 16 Jan 2025

Abstract

The combination of stochastic derivative pricing models and downside risk measures often leads to the paradox (risk, return) = (−infinity, +infinity) in a portfolio choice problem. The construction of a portfolio of derivatives with high expected returns and very negative downside risk (henceforth [...] Read more.

The combination of stochastic derivative pricing models and downside risk measures often leads to the paradox (risk, return) = (−infinity, +infinity) in a portfolio choice problem. The construction of a portfolio of derivatives with high expected returns and very negative downside risk (henceforth “golden strategy”) has only been studied if all the involved derivatives have the same underlying asset. This paper also considers multi-asset derivatives, gives practical methods to build multi-asset golden strategies for both the expected shortfall and the expectile risk measure, and shows that the use of multi-asset options makes the performance of the obtained golden strategy more efficient. Practical rules are given under the Black–Scholes–Merton multi-dimensional pricing model. Full article

23 pages, 589 KiB

Open AccessArticle

Unravelling the Link Between Financialisation and Economic Growth: Evidence from Croatia

by Agim Mamuti, Fatbardha Kadiu, Idaver Sherifi, Inna Romānova and Simon Grima

Risks 2025, 13(1), 15; https://doi.org/10.3390/risks13010015 - 16 Jan 2025

Abstract

This study investigates the relationship between financialisation and economic growth in Croatia, focusing on the period from 1995 to 2021. Using time series econometric models, including the Augmented Dickey–Fuller test for stationarity, Johansen’s cointegration test for long-term relationships, and the Granger causality test [...] Read more.

This study investigates the relationship between financialisation and economic growth in Croatia, focusing on the period from 1995 to 2021. Using time series econometric models, including the Augmented Dickey–Fuller test for stationarity, Johansen’s cointegration test for long-term relationships, and the Granger causality test within the Vector Error Correction Model (VECM) framework, the research reveals a sustained long-term equilibrium relationship between financialisation and economic growth in Croatia. However, the Granger causality test does not indicate a definitive causal direction between these variables. While the study is limited to the Croatian context and the specified period, its findings have significant implications for policymakers in Croatia and similar emerging markets. These results suggest that while financialisation can enhance economic growth through better resource allocation and increased investment, it may also pose risks such as financial instability. Such measures aim to mitigate the risks associated with financialisation while promoting sustainable economic growth. To address these challenges, we recommend the implementation of robust regulatory frameworks, financial literacy initiatives, and economic diversification strategies. Such measures aim to mitigate the risks associated with financialisation while promoting sustainable economic growth. The study fills an important research gap on financialisation in emerging markets, particularly in Croatia, providing empirical evidence on the long-term relationship between financialisation and economic growth and highlighting the need for context-specific policy interventions. Full article

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34 pages, 1327 KiB

Open AccessArticle

Determinants of South African Asset Market Co-Movement: Evidence from Investor Sentiment and Changing Market Conditions

by Fabian Moodley, Sune Ferreira-Schenk and Kago Matlhaku

Risks 2025, 13(1), 14; https://doi.org/10.3390/risks13010014 - 16 Jan 2025

Abstract

The co-movement of multi-asset markets in emerging markets has become an important determinant for investors seeking diversified portfolios and enhanced portfolio returns. Despite this, studies have failed to examine the determinants of the co-movement of multi-asset markets such as investor sentiment and changing [...] Read more.

The co-movement of multi-asset markets in emerging markets has become an important determinant for investors seeking diversified portfolios and enhanced portfolio returns. Despite this, studies have failed to examine the determinants of the co-movement of multi-asset markets such as investor sentiment and changing market conditions. Accordingly, this study investigates the effect of investor sentiment on the co-movement of South African multi-asset markets by introducing alternating market conditions. The Markov regime-switching autoregressive (MS-AR) model and Markov regime-switching vector autoregressive (MS-VAR) model impulse response function are used from 2007 March to January 2024. The findings indicate that investor sentiment has a time-varying and regime-specific effect on the co-movement of South African multi-asset markets. In a bull market condition, investor sentiment positively affects the equity–bond and equity–gold co-movement. In the bear market condition, investor sentiment has a negative and significant effect on the equity–bond, equity–property, bond–gold, and bond–property co-movement. Similarly, in a bull regime, the co-movement of South African multi-asset markets positively responds to sentiment shocks, although this is only observed in the short term. However, in the bear market regime, the co-movement of South African multi-asset markets responds positively and negatively to sentiment shocks, despite this being observed in the long run. These observations provide interesting insights to policymakers, investors, and fund managers for portfolio diversification and risk management strategies. That being, the current policies are not robust enough to reduce asset market integration and reduce sentiment-induced markets. Consequently, policymakers must re-examine and amend current policies according to the findings of the study. In addition, portfolio rebalancing in line with the findings of this study is essential for portfolio diversification. Full article

(This article belongs to the Special Issue Portfolio Selection and Asset Pricing)

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18 pages, 4380 KiB

Open AccessFeature PaperArticle

Gaussian Process Regression with a Hybrid Risk Measure for Dynamic Risk Management in the Electricity Market

by Abhinav Das and Stephan Schlüter

Risks 2025, 13(1), 13; https://doi.org/10.3390/risks13010013 - 16 Jan 2025

Abstract

In this work, we introduce an innovative approach to managing electricity costs within Germany’s evolving energy market, where dynamic tariffs are becoming increasingly normal. In line with recent German governmental policies, particularly the Energiewende (Energy Transition) and European Union directives on clean energy, [...] Read more.

In this work, we introduce an innovative approach to managing electricity costs within Germany’s evolving energy market, where dynamic tariffs are becoming increasingly normal. In line with recent German governmental policies, particularly the Energiewende (Energy Transition) and European Union directives on clean energy, this work introduces a risk management strategy based on a combination of the well-known risk measures of the Value at Risk (VaR) and Conditional Value at Risk (CVaR). The goal is to optimize electricity procurement by forecasting hourly prices over a certain horizon and allocating a fixed budget using the aforementioned measures to minimize the financial risk. To generate price predictions, a Gaussian process regression model is used. The aim of this hybrid approach is to design a model that is easily understandable but allows for a comprehensive evaluation of potential financial exposure. It enables consumers to adjust their consumption patterns or market traders to invest and allows more cost-effective and risk-aware decision-making. The potential of our approach is shown in a case study based on the German market. Moreover, by discussing the political and economical implications, we show how the implementation of our method can contribute to the realization of a sustainable, flexible, and efficient energy market, as outlined in Germany’s Renewable Energy Act. Full article

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

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15 pages, 370 KiB

Open AccessFeature PaperArticle

Are Women More Risk Averse? A Sequel

by Christos I. Giannikos and Efstathia D. Korkou

Risks 2025, 13(1), 12; https://doi.org/10.3390/risks13010012 - 15 Jan 2025

Abstract

This paper reexamines the question of gender differences in financial relative risk aversion using updated methods and data. Specifically, the paper revisits the 1998 work “Are women more risk averse?” by Jianakoplos and Bernasek, suggests refinements in their model in relation to the [...] Read more.

This paper reexamines the question of gender differences in financial relative risk aversion using updated methods and data. Specifically, the paper revisits the 1998 work “Are women more risk averse?” by Jianakoplos and Bernasek, suggests refinements in their model in relation to the database used, namely the U.S. Federal Reserve Board’s Survey of Consumer Finances (SCF), and performs new tests on the latest SCF from 2022. The suggested refinements pertain first to an enhanced computation of wealth, which includes additional categories of assets such as 401(k)s or other thrift savings accounts, and second to the more subtle handling and consideration of specific demographic data of the SCF respondents. Unlike the original study, which also included married couples, the new study focuses exclusively on single-headed (never-married) households. This eliminates ambiguity about the actual financial decision maker in households, enabling a clearer assessment of individual gendered behavior. Following the refinements, the new tests reveal a continuing pattern of decreasing relative risk aversion; however, contrary to the 1998 findings, there is no significant gender difference in financial relative risk aversion in 2022. This study also documents that education levels strongly influence risk-taking: single women with higher education levels are more likely to hold risky assets, while for men, higher education correlates with less risk-taking. The paper concludes by informing policymakers and financial educators so as to further tailor their strategies for promoting gender equality in financial decision-making. Full article

22 pages, 482 KiB

Open AccessArticle

Board Gender Diversity and Risk Management in Corporate Financing: A Study on Debt Structure and Financial Decision-Making

by Davood Askarany, Soleil Jafari, Azam Pouryousof, Sona Habibi and Hassan Yazdifar

Risks 2025, 13(1), 11; https://doi.org/10.3390/risks13010011 - 13 Jan 2025

Abstract

Purpose: This study examines the role of board gender diversity in shaping corporate financial decisions, particularly in terms of debt structure and risk management. Focusing on the Tehran Stock Exchange, it explores how female representation on boards influences long-term and short-term leverage decisions, [...] Read more.

Purpose: This study examines the role of board gender diversity in shaping corporate financial decisions, particularly in terms of debt structure and risk management. Focusing on the Tehran Stock Exchange, it explores how female representation on boards influences long-term and short-term leverage decisions, focusing on the moderating effect of board compensation. Design/Methodology: Utilising a quantitative ex post facto design, the study analyses data from 114 companies listed on the Tehran Stock Exchange between 2017 and 2021. Multivariate regression techniques, including year- and industry-fixed effects, are employed to investigate the relationship between board gender diversity, debt structure, and risk-taking behaviour. Findings: The results reveal a significant negative relationship between female board representation and long-term debt, suggesting that companies with more female directors tend to adopt more conservative debt structures, thereby reducing risk. Additionally, the findings demonstrate that board compensation moderates this relationship by curbing managerial risk-taking, further improving financial decision-making. Originality/Value: This research provides novel insights into the intersection of board gender diversity and risk management in financial decision-making, particularly in the context of a developing economy like Iran. It also offers practical implications for firms seeking to optimise their debt structures while maintaining sound risk management practices. Full article

(This article belongs to the Special Issue Financial Analysis, Corporate Finance and Risk Management)

24 pages, 431 KiB

Open AccessArticle

Corporate Social Responsibility, Efficiency, and Risk in US Banking

by Fathi Jouini, Mohamed Amine Chouchen and Ahlem Selma Messai

Risks 2025, 13(1), 10; https://doi.org/10.3390/risks13010010 - 10 Jan 2025

Abstract

Banks have faced increasing attention regarding their ability to balance Corporate Social Responsibility (CSR) initiatives, operational efficiency, and credit risk management, particularly in the wake of global financial challenges. This study examines the interplay between CSR, efficiency, and credit risk in 131 US [...] Read more.

Banks have faced increasing attention regarding their ability to balance Corporate Social Responsibility (CSR) initiatives, operational efficiency, and credit risk management, particularly in the wake of global financial challenges. This study examines the interplay between CSR, efficiency, and credit risk in 131 US banks from 2010 to 2018. Using the Choquet integral, two-step Data Envelopment Analysis, and a dynamic panel with the Generalized Method of Moments, the findings reveal a virtuous circle between CSR and credit risk, where CSR enhances credit risk profiles. Similarly, efficiency and risk exhibit mutual reinforcement. However, a vicious circle is identified between CSR and efficiency, indicating trade-offs between CSR objectives and operational efficiency. These insights guide policymakers and bank managers in optimizing this balance. Full article

21 pages, 4282 KiB

Open AccessFeature PaperArticle

Using Futures Prices and Analysts’ Forecasts to Estimate Agricultural Commodity Risk Premiums

by Gonzalo Cortazar, Hector Ortega and José Antonio Pérez

Risks 2025, 13(1), 9; https://doi.org/10.3390/risks13010009 - 10 Jan 2025

Abstract

This paper presents a novel 5-factor model for agricultural commodity risk premiums, an approach not explored in previous research. The model is applied to the specific cases of corn, soybeans, and wheat. Calibration is achieved using a Kalman filter and maximum likelihood, with [...] Read more.

This paper presents a novel 5-factor model for agricultural commodity risk premiums, an approach not explored in previous research. The model is applied to the specific cases of corn, soybeans, and wheat. Calibration is achieved using a Kalman filter and maximum likelihood, with data from futures markets and analysts’ forecasts. Risk premiums are computed by comparing expected and futures prices. The model considers that risk premiums are not solely determined by contract maturity but also by the marketing crop years. These crop years, in turn, are influenced by the respective harvest periods, a crucial factor in the agricultural commodity market. Results show that risk premiums vary across commodities, with some exhibiting positive and others negative values. While maturity affects risk premiums’ size, sign, and shape, the crop year plays a critical role, especially in the case of wheat. As speculators in the financial markets demand a positive risk premium, its sign provides insights into whether they are buyers or sellers of futures for each crop year, maturity, and commodity. This research offers valuable insights into grain price behavior, highlighting their similarities and differences. These findings have significant practical implications for market participants seeking to refine their trading and risk management strategies and for future research on the industry structure for each crop. Moreover, this enhanced understanding of risk premiums can be directly applied in the finance and agricultural industries, improving decision-making processes. Full article

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

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23 pages, 423 KiB

Open AccessArticle

The Impact of Hyperbolic Discounting on Asset Accumulation for Later Life: A Study of Active Investors Aged 65 Years and over in Japan

by Honoka Nabeshima, Sumeet Lal, Haruka Izumi, Yuzuha Himeno, Mostafa Saidur Rahim Khan and Yoshihiko Kadoya

Risks 2025, 13(1), 8; https://doi.org/10.3390/risks13010008 - 5 Jan 2025

Abstract

Asset accumulation in later life is a pressing issue in Japan due to the growing gap between life expectancy (87.14 years for women, 81.09 years for men in 2023) and the retirement age (65 or less). This gap heightens financial insecurity, emphasizing the [...] Read more.

Asset accumulation in later life is a pressing issue in Japan due to the growing gap between life expectancy (87.14 years for women, 81.09 years for men in 2023) and the retirement age (65 or less). This gap heightens financial insecurity, emphasizing the need to meet asset goals by 65. Hyperbolic discounting, driven by present-biased preferences, often hinders this process, but empirical evidence for those aged 65 and older remains limited. Moreover, prior research has overlooked the varying impacts of hyperbolic discounting across different wealth levels. This study addresses these gaps by analyzing data from 6709 active Japanese investors aged over 65 (2023 wave) using probit regression. Wealth thresholds are categorized into four levels: JPY 20 million, JPY 30 million, JPY 50 million, and JPY 100 million. The results show that hyperbolic discounting significantly impairs asset accumulation at the JPY 100 million level but not at lower thresholds. This effect likely reflects the complex nature of hyperbolic discounting, which primarily affects long-term savings and investments. The findings underscore the importance of addressing hyperbolic discounting in later-life financial planning. Recommendations include implementing automatic savings plans, enhancing financial literacy, and incorporating behavioral insights into planning tools to support better asset accumulation outcomes. Full article

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