Journal Description
Risks
Risks
is an international, scholarly, peer-reviewed, open access journal for research and studies on insurance and financial risk management. Risks is published monthly online by MDPI.
- Open Access— free for readers, with article processing charges (APC) paid by authors or their institutions.
- High visibility: indexed within Scopus, ESCI (Web of Science), EconLit, EconBiz, RePEc, and other databases.
- Journal Rank: JCR - Q2 (Business, Finance) / CiteScore - Q1 (Economics, Econometrics and Finance (miscellaneous))
- Rapid Publication: manuscripts are peer-reviewed and a first decision is provided to authors approximately 20.4 days after submission; acceptance to publication is undertaken in 4.3 days (median values for papers published in this journal in the second half of 2023).
- Recognition of Reviewers: reviewers who provide timely, thorough peer-review reports receive vouchers for a discount on the APC of their next publication in any MDPI journal, in appreciation of the work done
Impact Factor:
2.0 (2023);
5-Year Impact Factor:
1.7 (2023)
Latest Articles
Foreign Exchange Futures Trading and Spot Market Volatility in Thailand
Risks 2024, 12(7), 107; https://doi.org/10.3390/risks12070107 - 26 Jun 2024
Abstract
This paper investigates how the introduction of foreign exchange futures has an impact on spot volatility and considers the contemporaneous and dynamic relationship between spot volatility and foreign exchange futures trading activity, including trading volume and open interest in the Thailand Futures Exchange
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This paper investigates how the introduction of foreign exchange futures has an impact on spot volatility and considers the contemporaneous and dynamic relationship between spot volatility and foreign exchange futures trading activity, including trading volume and open interest in the Thailand Futures Exchange context, with the examples of the EUR/USD futures and USD/JPY futures. The results of the EGARCH (1,1) model show that the introduction of foreign exchange futures decreases spot volatility. It also increases the rate at which new information is impounded into spot prices but decreases the persistency of volatility shocks. A positive effect of unexpected trading volume and a negative effect of unexpected open interest on contemporaneous spot volatility are in line with the VAR(1) model results of the dynamic relationship between spot volatility and foreign exchange futures trading activity. With the impact on spot volatility caused by unexpected open interest rate being stronger than by unexpected trading volume, foreign exchange futures trading stabilizes spot volatility.
Full article
(This article belongs to the Special Issue Volatility Modeling in Financial Market)
Open AccessArticle
Mean-Reverting Statistical Arbitrage Strategies in Crude Oil Markets
by
Viviana Fanelli
Risks 2024, 12(7), 106; https://doi.org/10.3390/risks12070106 - 25 Jun 2024
Abstract
In this paper, we introduce the concept of statistical arbitrage through the definition of a mean-reverting trading strategy that captures persistent anomalies in long-run relationships among assets. We model the statistical arbitrage proceeding in three steps: (1) to identify mispricings in the chosen
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In this paper, we introduce the concept of statistical arbitrage through the definition of a mean-reverting trading strategy that captures persistent anomalies in long-run relationships among assets. We model the statistical arbitrage proceeding in three steps: (1) to identify mispricings in the chosen market, (2) to test mean-reverting statistical arbitrage, and (3) to develop statistical arbitrage trading strategies. We empirically investigate the existence of statistical arbitrage opportunities in crude oil markets. In particular, we focus on long-term pricing relationships between the West Texas Intermediate crude oil futures and a so-called statistical portfolio, composed by other two crude oils, Brent and Dubai. Firstly, the cointegration regression is used to track the persistent pricing equilibrium between the West Texas Intermediate crude oil price and the statistical portfolio value, and to identify mispricings between the two. Secondly, we verify that mispricing dynamics revert back to equilibrium with a predictable behaviour, and we exploit this stylized fact by applying the trading rules commonly used in equity markets to the crude oil market. The trading performance is then measured by three specific profit indicators on out-of-sample data.
Full article
(This article belongs to the Special Issue Portfolio Theory, Financial Risk Analysis and Applications)
Open AccessArticle
Intellectual Capital, Political Connection, and Firm Performance: Exploring from Indonesia
by
Suham Cahyono and Ardianto Ardianto
Risks 2024, 12(7), 105; https://doi.org/10.3390/risks12070105 - 24 Jun 2024
Abstract
The relationship between intellectual capital and firm performance represents a critical facet of corporate governance, warranting comprehensive investigation. By analyzing data from 1151 non-financial firms listed on the Indonesia Stock Exchange over the period from 2018 to 2022, the authors utilize fixed effect
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The relationship between intellectual capital and firm performance represents a critical facet of corporate governance, warranting comprehensive investigation. By analyzing data from 1151 non-financial firms listed on the Indonesia Stock Exchange over the period from 2018 to 2022, the authors utilize fixed effect regression analysis to test their hypothesis. This study’s findings reveal a positive and significant relationship between intellectual capital and firm performance. Additionally, the interaction model incorporating political connections yields statistically significant results, indicating that political connections can moderate the relationship between intellectual capital and firm performance. This study makes a substantial contribution to the literature, particularly by advancing the understanding of corporate governance through the lens of intellectual capital’s influence on firm performance. It offers both theoretical and practical insights into the Indonesian context, highlighting the moderating role of political connections. Notably, this study is the first to incorporate interaction models to assess the impact of political connections on this relationship.
Full article
(This article belongs to the Special Issue Corporate Finance and Intellectual Capital Management)
Open AccessArticle
Inference for the Parameters of a Zero-Inflated Poisson Predictive Model
by
Min Deng, Mostafa S. Aminzadeh and Banghee So
Risks 2024, 12(7), 104; https://doi.org/10.3390/risks12070104 - 24 Jun 2024
Abstract
In the insurance sector, Zero-Inflated models are commonly used due to the unique nature of insurance data, which often contain both genuine zeros (meaning no claims made) and potential claims. Although active developments in modeling excess zero data have occurred, the use of
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In the insurance sector, Zero-Inflated models are commonly used due to the unique nature of insurance data, which often contain both genuine zeros (meaning no claims made) and potential claims. Although active developments in modeling excess zero data have occurred, the use of Bayesian techniques for parameter estimation in Zero-Inflated Poisson models has not been widely explored. This research aims to introduce a new Bayesian approach for estimating the parameters of the Zero-Inflated Poisson model. The method involves employing Gamma and Beta prior distributions to derive closed formulas for Bayes estimators and predictive density. Additionally, we propose a data-driven approach for selecting hyper-parameter values that produce highly accurate Bayes estimates. Simulation studies confirm that, for small and moderate sample sizes, the Bayesian method outperforms the maximum likelihood (ML) method in terms of accuracy. To illustrate the ML and Bayesian methods proposed in the article, a real dataset is analyzed.
Full article
(This article belongs to the Special Issue Statistical Applications to Insurance and Risk)
Open AccessArticle
An Exposition of the Gap between Public Sector and Private Sector Participation in Green Finance
by
Chekani Nkwaira and Huibrecht Margaretha Van der Poll
Risks 2024, 12(7), 103; https://doi.org/10.3390/risks12070103 - 21 Jun 2024
Abstract
Greening the environment cannot be achieved satisfactorily, considering that the private sector lags behind the public sector in participation levels. The purpose of this study was to determine the reasons behind the gap in green finance between the two sectors using numerically derived
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Greening the environment cannot be achieved satisfactorily, considering that the private sector lags behind the public sector in participation levels. The purpose of this study was to determine the reasons behind the gap in green finance between the two sectors using numerically derived outcomes. Six-year data in the form of total shareholder returns, comprising capital gains and dividends paid from the largest banks in China, the USA, and Europe involved in financing fossil fuels, were extracted from Yahoo.com finance and Macrotrends public forums. Equity premiums were calculated from the total shareholder returns and risk-free rates. A 95% confidence interval was established to determine the lower and upper limits of the equity premiums. The resulting upper limits were used to project premiums that could attract the private sector by 2030. Equity premiums averaged 2.73%, 9.73%, and 4.31% for China, the USA, and Europe, respectively, indicating the substantial task in the USA of attracting the private sector compared to Europe and China. The projections of total shareholder returns showed the same patterns in equity premiums among China, the United States (USA), and Europe. To bridge the gap, the significant need for economic benefits for the private sector should ideally be addressed through green bonds, tailored to green financing projects that are earmarked for revenue generation.
Full article
(This article belongs to the Special Issue Tail Risk Analysis and Management)
Open AccessFeature PaperArticle
Can Multi-Peril Insurance Policies Mitigate Adverse Selection?
by
Peter Zweifel and Annette Hofmann
Risks 2024, 12(6), 102; https://doi.org/10.3390/risks12060102 - 20 Jun 2024
Abstract
The objective of this paper is to pursue an intuitive idea: for a consumer who represents an “unfavorable” health risk but an “excellent risk” as a driver, a multi-peril policy could be associated with a reduced selection effort on the part of the
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The objective of this paper is to pursue an intuitive idea: for a consumer who represents an “unfavorable” health risk but an “excellent risk” as a driver, a multi-peril policy could be associated with a reduced selection effort on the part of the insurer. If this intuition should be confirmed, it will serve to address the decade-long concern with risk selection both in the economic literature and on the part of policy makers. As an illustrative example, a two-peril model is developed in which consumers deploy effort in search of a policy offering them maximum coverage at the current market price while insurers deploy effort designed to stave off unfavorable risks. Two types of Nash equilibria are compared: one in which the insurer is confronted with high-risk and low-risk types, and another one where both types are a “better risk” with regard to a second peril. The difference in the insurer’s selection effort directed at high-risk and low-risk types is indeed shown to be lower in the latter case, resulting in a mitigation of adverse selection.
Full article
(This article belongs to the Special Issue Advancements in Actuarial Mathematics and Insurance Risk Management)
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Open AccessArticle
Support of the SDGs as a New Approach to Financial Risk Management in Responsible Universities in Russia
by
Zhanna V. Gornostaeva, Larisa V. Shabaltina, Igor V. Denisov, Aleksandra A. Musatkina and Nikolai G. Sinyavskiy
Risks 2024, 12(6), 101; https://doi.org/10.3390/risks12060101 - 20 Jun 2024
Abstract
The purpose of this paper was to reveal the influence of the support of the sustainable development goals (SDGs) on the financial risks of responsible universities in Russia. This paper fills the gap in the literature that exists regarding the unknown consequences of
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The purpose of this paper was to reveal the influence of the support of the sustainable development goals (SDGs) on the financial risks of responsible universities in Russia. This paper fills the gap in the literature that exists regarding the unknown consequences of SDGs’ support by responsible Russian universities concerning their financial risks. Based on the experience of the top 30 most responsible Russian universities in 2023, we used regression analysis to compile a model for their financial risk management. This model mathematically describes the cause-and-effect relationships of financial risk management in responsible Russian universities. This paper offers a new approach to financial risk management in responsible Russian universities. In it, financial risks to Russian universities are reduced due to universities accepting responsibility for state and private investors. A feature of the new approach is that the effective use of university funds is ensured not by cost savings but by the support of the SDGs. The potential for a reduction in financial risk in responsible universities in Russia through alternative approaches to financial risk management was disclosed. The proposed new approach can potentially raise (to a large extent) the aggregate incomes of responsible universities in Russia compared to the existing approach. The main conclusion is that the existing approach to financial risk management in Russian universities is based on low-efficiency managerial measures which risk burdening universities. This burden could be prevented with the newly developed approach to financial risk management in responsible universities in Russia through support of the SDGs. The theoretical significance lies in clarifying the specific list of the SDGs whose support makes the largest contribution to reducing financial risks for the universities—namely, SDG 4, SDG 8, and SDG 9. The practical significance is that the new approach will allow for full disclosure of the potential reduction in financial risks in responsible universities in Russia in the Decade of Action (2020–2030). The managerial significance is as follows: the proposed recommendations will allow improved financial risk management in Russian universities through optimization of the support of the SDGs.
Full article
(This article belongs to the Special Issue Managing Financial Risks Based on Corporate Social Responsibility for Sustainable Development II)
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Open AccessArticle
Knowledge Capital and Stock Returns during Crises in the Manufacturing Sector: Moderating Role of Market Share, Tobin’s Q, and Cash Holdings
by
Chaeho Chase Lee, Erdal Atukeren and Hohyun Kim
Risks 2024, 12(6), 100; https://doi.org/10.3390/risks12060100 - 19 Jun 2024
Abstract
This study analyzes the impact of knowledge capital (KC), a key element of firms’ innovation and competitiveness, on stock returns during economic crises when sustainable competitiveness becomes particularly important. We analyze the impact of the Global Financial Crisis and COVID-19 as economic crises,
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This study analyzes the impact of knowledge capital (KC), a key element of firms’ innovation and competitiveness, on stock returns during economic crises when sustainable competitiveness becomes particularly important. We analyze the impact of the Global Financial Crisis and COVID-19 as economic crises, focusing on manufacturing industries with a high proportion of investment shifts from physical capital to KC. Our findings indicate that KC is positively associated with stock returns during the Global Financial Crisis and COVID-19. This positive relationship is strengthened by the firm’s ability to leverage KC, as measured by greater product market share, higher Tobin’s Q, and larger cash holdings. This study emphasizes the protective role of KC during the economic crisis when the market pays more attention to corporate sustainability and provides implications to corporate managers and investors.
Full article
Open AccessArticle
The Economic and Financial Health of Lithuanian Logistics Companies
by
Rita Bužinskienė and Vera Gelashvili
Risks 2024, 12(6), 99; https://doi.org/10.3390/risks12060099 - 19 Jun 2024
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In recent decades, the importance of transport and logistics companies has increased considerably, especially for Lithuania, where this sector is on the rise and creating benefits for various users. Therefore, this study aims to analyse the economic–financial situation of transport and logistics companies
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In recent decades, the importance of transport and logistics companies has increased considerably, especially for Lithuania, where this sector is on the rise and creating benefits for various users. Therefore, this study aims to analyse the economic–financial situation of transport and logistics companies operating in Lithuania, focusing mainly on their financial risk, probability of bankruptcy, and level of solvency. To achieve these results, 416 companies were analysed based on their data from 2022. The employed methodology included descriptive analysis, quartile ratio analysis, the use of Altman’s Z-score model to predict bankruptcy, and, finally, logistic regression analysis to answer the hypotheses. The results show that the companies analysed in this study were highly profitable, with a high level of solvency and liquidity that did not compromise their continuity in the market. These results were confirmed by the Z-score analysis. In addition, it was observed that the age and size of the companies did not affect their survival on the market. This study presents results that are of great interest for the academic literature, as well as for the management of logistics companies. The originality of the study lies in its relevance and timeliness, presenting robust results for different stakeholders, such as policymakers or new entrepreneurs, among others.
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Open AccessArticle
Cross-Sectional Determinants of Analyst Coverage for R&D Firms
by
Ashraf Khallaf, Feras M. Salama, Musa Darayseh and Eid Alotaibi
Risks 2024, 12(6), 98; https://doi.org/10.3390/risks12060098 - 18 Jun 2024
Abstract
Prior research document a positive association between analyst coverage and R&D. However, they do not investigate what particular attribute of R&D leads to this positive association. In this study we aim to fill the gap in the extant literature and explore the cross-sectional
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Prior research document a positive association between analyst coverage and R&D. However, they do not investigate what particular attribute of R&D leads to this positive association. In this study we aim to fill the gap in the extant literature and explore the cross-sectional determinants of the association between R&D and analyst coverage. We investigate four cross-sectional determinants: reporting biases arising from expensing of R&D compared to capitalization of R&D, uncertainty associated with R&D, investors’ attention, and scale effects of R&D. We find that while reporting biases and uncertainty decrease analyst coverage for R&D firms, investors’ attention and scale effects of R&D increase analyst coverage. Furthermore, we find that the positive association between R&D and analyst coverage documented by Barth et al. is fully explained by scale effects of R&D.
Full article
(This article belongs to the Special Issue Corporate Finance and Intellectual Capital Management)
Open AccessArticle
Dependence Modelling for Heavy-Tailed Multi-Peril Insurance Losses
by
Tianxing Yan, Yi Lu and Himchan Jeong
Risks 2024, 12(6), 97; https://doi.org/10.3390/risks12060097 - 16 Jun 2024
Abstract
The Danish fire loss dataset records commercial fire losses under three insurance coverages: building, contents, and profits. Existing research has primarily focused on the heavy-tail behaviour of the losses but ignored the relationship among different insurance coverages. In this paper, we aim to
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The Danish fire loss dataset records commercial fire losses under three insurance coverages: building, contents, and profits. Existing research has primarily focused on the heavy-tail behaviour of the losses but ignored the relationship among different insurance coverages. In this paper, we aim to model the aggregate loss for all three coverages. To study the pairwise dependence of claims from all types of coverage, an independent model, a hierarchical model, and some copula-based models are proposed for the frequency component. Meanwhile, we applied composite distributions to capture the heavy-tailed severity component. It is shown that consideration of dependence for the multi-peril frequencies (i) significantly enhances model goodness-of-fit and (ii) provides more accurate risk measures of the aggregated losses for all types of coverage in total.
Full article
(This article belongs to the Special Issue Statistical Modelling in Risk Management)
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Open AccessArticle
Sustaining Algeria’s Retirement System in the Population Aging Context: Could a Contribution Cap Strategy Work?
by
Farid Flici and Inmaculada Dominguez-Fabian
Risks 2024, 12(6), 96; https://doi.org/10.3390/risks12060096 - 14 Jun 2024
Abstract
Previous research predicts an increasing financial deficit in Algeria’s PAYG retirement system, mainly due to rapid population aging, and parametric adjustments will be insufficient to alleviate this imbalance. Mitigating the effects of population aging will necessitate further intervention. In this work, we analyze
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Previous research predicts an increasing financial deficit in Algeria’s PAYG retirement system, mainly due to rapid population aging, and parametric adjustments will be insufficient to alleviate this imbalance. Mitigating the effects of population aging will necessitate further intervention. In this work, we analyze how capping contributed salaries can help to mitigate the effects of population aging on the retirement system. Under generous Pay-As-You-Go schemes, promised pension payouts far exceed contributions. Thus, restricting contributions is expected to reduce the burden of future benefits by accepting lower contributions today, while directing public subsidies to low-income individuals. We simulate the future evolution of the financial balance of Algeria’s retirement system under various contributable salary caps versus various scenarios of environmental evolution and potential parametric reform actions. The results demonstrated that a 40% cap, along with major parametric reforms and an ideal environment, would help achieve a cumulatively balanced system in the long run.
Full article
(This article belongs to the Special Issue Life Insurance and Pensions: Latest Advances and Prospects)
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Open AccessArticle
Expected Utility Optimization with Convolutional Stochastically Ordered Returns
by
Romain Gauchon and Karim Barigou
Risks 2024, 12(6), 95; https://doi.org/10.3390/risks12060095 - 14 Jun 2024
Abstract
Expected utility theory is critical for modeling rational decision making under uncertainty, guiding economic agents as they seek to optimize outcomes. Traditional methods often require restrictive assumptions about underlying stochastic processes, limiting their applicability. This paper expands the theoretical framework by considering investment
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Expected utility theory is critical for modeling rational decision making under uncertainty, guiding economic agents as they seek to optimize outcomes. Traditional methods often require restrictive assumptions about underlying stochastic processes, limiting their applicability. This paper expands the theoretical framework by considering investment returns modeled by a stochastically ordered family of random variables under the convolution order, including Poisson, Gamma, and exponential distributions. Utilizing fractional calculus, we derive explicit, closed-form expressions for the derivatives of expected utility for various utility functions, significantly broadening the potential for analytical and computational applications. We apply these theoretical advancements to a case study involving the optimal production strategies of competitive firms, demonstrating the practical implications of our findings in economic decision making.
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Open AccessReview
Cryptocurrencies’ Impact on Accounting: Bibliometric Review
by
Georgiana-Iulia Lazea, Ovidiu-Constantin Bunget and Cristian Lungu
Risks 2024, 12(6), 94; https://doi.org/10.3390/risks12060094 - 11 Jun 2024
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This bibliometric study explores the cryptocurrency accounting (CA) literature and the connections between authors, institutions, and countries where cryptocurrency activity involves transactions that must be legally recognized in accounting, ensure accuracy and reliability for auditing, and adhere to tax compliance. The design involves
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This bibliometric study explores the cryptocurrency accounting (CA) literature and the connections between authors, institutions, and countries where cryptocurrency activity involves transactions that must be legally recognized in accounting, ensure accuracy and reliability for auditing, and adhere to tax compliance. The design involves the selection of data from Web of Science Core Collection (WoS) and Scopus, published between 2007 and 2023. The technique helps identify influential publications, collaboration networks, thematic clusters, and trends in research on CA using tools VOSviewer, Biblioshiny, and MS Excel. The originality of the study lies in its dual role as a support for accounting professionals and academics to develop innovative solutions for the challenges posed by crypto technology across core accounting areas: financial and managerial accounting, taxation, and auditing. The findings offer insights into the themes mentioned, and even if the collaboration between the authors is not very developed, the innovation and public recognition of the subject could raise researchers’ interest. The limitation of the dataset is that it does not cover all relevant publications in a different period from the one in which the data were retrieved, 9–11 May 2024. This review might need periodic updates because the CA landscape is constantly changing.
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Open AccessArticle
Deep Learning Option Price Movement
by
Weiguan Wang and Jia Xu
Risks 2024, 12(6), 93; https://doi.org/10.3390/risks12060093 - 4 Jun 2024
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Understanding how price-volume information determines future price movement is important for market makers who frequently place orders on both buy and sell sides, and for traders to split meta-orders to reduce price impact. Given the complex non-linear nature of the problem, we consider
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Understanding how price-volume information determines future price movement is important for market makers who frequently place orders on both buy and sell sides, and for traders to split meta-orders to reduce price impact. Given the complex non-linear nature of the problem, we consider the prediction of the movement direction of the mid-price on an option order book, using machine learning tools. The applicability of such tools on the options market is currently missing. On an intraday tick-level dataset of options on an exchange traded fund from the Chinese market, we apply a variety of machine learning methods, including decision tree, random forest, logistic regression, and long short-term memory neural network. As machine learning models become more complex, they can extract deeper hidden relationship from input features, which classic market microstructure models struggle to deal with. We discover that the price movement is predictable, deep neural networks with time-lagged features perform better than all other simpler models, and this ability is universal and shared across assets. Using an interpretable model-agnostic tool, we find that the first two levels of features are the most important for prediction. The findings of this article encourage researchers as well as practitioners to explore more sophisticated models and use more relevant features.
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Open AccessArticle
Estimating Disease-Free Life Expectancy Based on Clinical Data from the French Hospital Discharge Database
by
Oleksandr Sorochynskyi, Quentin Guibert, Frédéric Planchet and Michaël Schwarzinger
Risks 2024, 12(6), 92; https://doi.org/10.3390/risks12060092 - 3 Jun 2024
Abstract
The development of health indicators to measure healthy life expectancy (HLE) is an active field of research aimed at summarizing the health of a population. Although many health indicators have emerged in the literature as critical metrics in public health assessments, the methods
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The development of health indicators to measure healthy life expectancy (HLE) is an active field of research aimed at summarizing the health of a population. Although many health indicators have emerged in the literature as critical metrics in public health assessments, the methods and data to conduct this evaluation vary considerably in nature and quality. Traditionally, health data collection relies on population surveys. However, these studies, typically of limited size, encompass only a small yet representative segment of the population. This limitation can necessitate the separate estimation of incidence and mortality rates, significantly restricting the available analysis methods. In this article, we leverage an extract from the French National Hospital Discharge database to define health indicators. Our analysis focuses on the resulting Disease-Free Life Expectancy (Dis-FLE) indicator, which provides insights based on the hospital trajectory of each patient admitted to hospital in France during 2008–2013. Through this research, we illustrate the advantages and disadvantages of employing large clinical datasets as the foundation for more robust health indicators. We shed light on the opportunities that such data offer for a more comprehensive understanding of the health status of a population. In particular, we estimate age-dependent hazard rates associated with sex, alcohol abuse, tobacco consumption, and obesity, as well as geographic location. Simultaneously, we delve into the challenges and limitations that arise when adopting such a data-driven approach.
Full article
(This article belongs to the Special Issue Advancement in Mortality Forecasting and Mortality/Longevity Risk Management)
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Open AccessArticle
Determinants of Corporate Indebtedness in Portugal: An Analysis of Financial Behaviour Clusters
by
Fernando Tavares, Eulália Santos, Margarida Freitas Oliveira and Luís Almeida
Risks 2024, 12(6), 91; https://doi.org/10.3390/risks12060091 - 31 May 2024
Abstract
Corporate indebtedness is a powerful tool in determining a company’s financial health with impacts on its image and reputation. The main objective of this research is to study the determining factors in corporate indebtedness in Portugal. It also has the secondary objectives of
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Corporate indebtedness is a powerful tool in determining a company’s financial health with impacts on its image and reputation. The main objective of this research is to study the determining factors in corporate indebtedness in Portugal. It also has the secondary objectives of creating clusters of companies’ behaviour in relation to the use of credit and verifying their differences in relation to the characteristics of the companies. It uses a quantitative methodology based on a questionnaire survey of 1957 Portuguese companies. The results of the factor analysis show the formation of six determining factors in corporate indebtedness, namely the negotiating relationship with banks, financing, cycle and indebtedness, company operating performance, guarantees used to obtain bank financing and financing risk analysis as well as secondary forms of bank financing. The application of cluster analysis to the six factors formed led to the classification of companies into three clusters: the resilient financial cluster, the operational excellence cluster and the strategic financial cluster. There are several statistically significant differences in the corporate financing factors in relation to the clusters to which they belong. The evidence of the factors and clusters explaining company financing provides insights for improving credit access practices and for implementing public policies that facilitate access to credit and promote economic development.
Full article
(This article belongs to the Special Issue Financial Analysis, Corporate Finance and Risk Management)
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Open AccessArticle
Key Determinants of Corporate Governance in Financial Institutions: Evidence from South Africa
by
Floyd Khoza, Daniel Makina and Patricia Lindelwa Makoni
Risks 2024, 12(6), 90; https://doi.org/10.3390/risks12060090 - 30 May 2024
Abstract
The purpose of this study was to examine the key determinants of corporate governance in selected financial institutions. Using South African financial institutions as a unit of analysis, namely insurance companies and banks, the study employed a panel generalised method of moments (GMM)
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The purpose of this study was to examine the key determinants of corporate governance in selected financial institutions. Using South African financial institutions as a unit of analysis, namely insurance companies and banks, the study employed a panel generalised method of moments (GMM) model using a data set for the period from 2007 to 2020, to assess key determinants of corporate governance proxies identified for the study. The study sampled 21 South African financial institutions composed of Johannesburg Securities Exchange (JSE) listed and unlisted banks and insurance companies. To measure corporate governance, the study developed a composite index employing the principal components analysis (PCA) method. The findings revealed a positive and significant association between the corporate governance index and its lagged variables. Furthermore, a significant and positive link was found between the efficiency ratio and corporate governance index and capital adequacy ratio (CAR); corporate governance index and firm size; corporate governance index and leverage ratio (LEV); and corporate governance index and return on assets (ROA). However, a negative and significant correlation was found between financial stability and the corporate governance index. The link between return on equity (ROE) and corporate governance was insignificant. A small cohort of financial institutions was excluded because it was challenging to obtain complete annual reports to extract the required data. The study was limited to only five corporate governance measures, namely board diversity, board size, board composition (independent non-executive directors and non-executive directors), and board remuneration. The findings are anticipated to persuade developing countries to pay special attention to how corporate governance is measured.
Full article
(This article belongs to the Special Issue Risk Governance in the Finance and Insurance Industry)
Open AccessArticle
A Case Study of Bank Equity Valuation Methods Employed by South African, Nigerian and Kenyan Equity Researchers
by
Vusani Moyo and Ayodeji Michael Obadire
Risks 2024, 12(6), 89; https://doi.org/10.3390/risks12060089 - 27 May 2024
Abstract
The valuation of banks is inherently complicated because of the uncertainties arising from their information opaqueness and inherent risks. Unlike non-banking firms, banks require specialised equity-side valuation approaches. This study addresses a gap in the literature by examining valuation methods used by bank
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The valuation of banks is inherently complicated because of the uncertainties arising from their information opaqueness and inherent risks. Unlike non-banking firms, banks require specialised equity-side valuation approaches. This study addresses a gap in the literature by examining valuation methods used by bank equity researchers. The study used a total of 201 reports on South African banks (2018–2023), 56 reports on Nigerian banks (2018–2023), and 27 reports on Kenyan banks (2018–2023) to investigate the bank equity valuation methods utilised by analysts in the employ of Investec Ltd. and Standard Bank Group Ltd. The study’s findings show that Investec’s South African analysts predominantly used the warranted equity method, based on book value (BV), and return on equity (ROE), for valuing shares throughout the South African, Nigerian, and Kenyan banks surveyed. Furthermore, Standard Bank Group’s analysts employed this method, incorporating tangible net asset value (tNAV) and return on tangible equity (ROTE), for South African and Nigerian banks, but in Kenya their analysts used the residual income model to value the equities of the five Kenyan banks they covered. These findings suggest that the warranted equity method and the residual income model are the mostly used bank equity valuation methods in South Africa, Nigeria, and Kenya. The study concludes with relevant recommendations, offering significant insights for banks, regulators, and investors to make knowledgeable decisions concerning equity valuation.
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Open AccessArticle
Some Results on Bivariate Squared Maximum Sharpe Ratio
by
Samane Al-sadat Mousavi, Ali Dolati and Ali Dastbaravarde
Risks 2024, 12(6), 88; https://doi.org/10.3390/risks12060088 - 24 May 2024
Abstract
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The Sharpe ratio is a widely used tool for assessing investment strategy performance. An essential part of investing involves creating an appropriate portfolio by determining the optimal weights for desired assets. Before constructing a portfolio, selecting a set of investment opportunities is crucial.
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The Sharpe ratio is a widely used tool for assessing investment strategy performance. An essential part of investing involves creating an appropriate portfolio by determining the optimal weights for desired assets. Before constructing a portfolio, selecting a set of investment opportunities is crucial. In the absence of a risk-free asset, investment opportunities can be identified based on the Sharpe ratios of risky assets and their correlation. The maximum squared Sharpe ratio serves as a useful metric that summarizes the performance of an investment opportunity in a single value, considering the Sharpe ratios of assets and their correlation coefficients. However, the assumption of a normal distribution in asset returns, as implied by the Sharpe ratio and related metrics, may not always hold in practice. Non-normal returns with a non-linear dependence structure can result in an overestimation or underestimation of these metrics. Copula functions are commonly utilized to address non-normal dependence structures. This study examines the impact of asset dependence on the squared maximum Sharpe ratio using copulas and proposes a copula-based approach to tackle the estimation issue. The performance of the proposed estimator is illustrated through simulation and real-data analysis.
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