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 18.7 days after submission; acceptance to publication is undertaken in 5.5 days (median values for papers published in this journal in the first half of 2024).
- 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
Forecasting Age- and Sex-Specific Survival Functions: Application to Annuity Pricing
Risks 2024, 12(7), 117; https://doi.org/10.3390/risks12070117 - 22 Jul 2024
Abstract
We introduce the function principal component regression (FPCR) forecasting method to model and forecast age-specific survival functions observed over time. The age distribution of survival functions is an example of constrained data whose values lie within a unit interval. Because of the constraint,
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We introduce the function principal component regression (FPCR) forecasting method to model and forecast age-specific survival functions observed over time. The age distribution of survival functions is an example of constrained data whose values lie within a unit interval. Because of the constraint, such data do not reside in a linear vector space. A natural way to deal with such a constraint is through an invertible logit transformation that maps constrained onto unconstrained data in a linear space. With a time series of unconstrained data, we apply a functional time-series forecasting method to produce point and interval forecasts. The forecasts are then converted back to the original scale via the inverse logit transformation. Using the age- and sex-specific survival functions for Australia, we investigate the point and interval forecast accuracies for various horizons. We conclude that the functional principal component regression (FPCR) provides better forecast accuracy than the Lee–Carter (LC) method. Therefore, we apply FPCR to calculate annuity pricing and compare it with the market annuity price.
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(This article belongs to the Special Issue Risks Journal: A Decade of Advancing Knowledge and Shaping the Future)
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Towards Diagnosing and Mitigating Behavioral Cyber Risks
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Carlo Pugnetti, Albena Björck, Reto Schönauer and Carlos Casián
Risks 2024, 12(7), 116; https://doi.org/10.3390/risks12070116 - 19 Jul 2024
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A company’s cyber defenses are based on a secure infrastructure and risk-aware behavior by employees. With rising cyber threats and normative training efforts showing limited impact, raising cyber risk awareness is emerging as a challenging effort. The review of the extant literature on
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A company’s cyber defenses are based on a secure infrastructure and risk-aware behavior by employees. With rising cyber threats and normative training efforts showing limited impact, raising cyber risk awareness is emerging as a challenging effort. The review of the extant literature on awareness diagnosis shows interdisciplinary but mainly theoretical approaches to understanding attitudes and influencing risk behavior. We propose and test a novel methodology to combine and operationalize two tools, deep metaphor interviews and the IDEA risk communication model, to apply them for the first time in the context of behavioral cyber vulnerabilities. The results show a link between diagnosed attitudes and effective risk behavior in a real-life organizational setting, indicating the potential for an expanded diagnostic effort. We propose to develop a broader diagnostic and intervention set to improve cyber awareness and a toolkit to support the business practice of cyber risk management.
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Open AccessArticle
The Impact of FinTech Adoption on Traditional Financial Inclusion in Sub-Saharan Africa
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Abdul Karim Kamara and Baorong Yu
Risks 2024, 12(7), 115; https://doi.org/10.3390/risks12070115 - 19 Jul 2024
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This study investigates the impact of FinTech adoption on traditional financial inclusion in 22 countries in sub-Saharan Africa (SSA). The study utilizes the World Bank’s World Development Indicators data and the International Monetary Fund’s Financial Access Survey data. This study employed Principal Component
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This study investigates the impact of FinTech adoption on traditional financial inclusion in 22 countries in sub-Saharan Africa (SSA). The study utilizes the World Bank’s World Development Indicators data and the International Monetary Fund’s Financial Access Survey data. This study employed Principal Component Analysis (PCA) to construct the dimensions of traditional financial inclusion and the overall financial inclusion index. Applying the Generalized Method of Moments estimation technique to annual data spanning from 2004 to 2022, the findings show that FinTech has a negative and statistically significant effect on the geographic and usage dimensions. However, it has a positive and statistically significant impact on the demographic dimension and the overall traditional financial inclusion index. These findings indicate that FinTech does not have a detrimental impact on traditional financial inclusion, which is contrary to the findings of other studies. Therefore, in order to enhance the degree of financial inclusion in SSA, it is important for traditional financial inclusion to effectively utilize FinTech.
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Influence of Macroeconomic Factors on Financial Liquidity of Companies: Evidence from Poland
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Jarosław Nowicki, Piotr Ratajczak and Dawid Szutowski
Risks 2024, 12(7), 114; https://doi.org/10.3390/risks12070114 - 18 Jul 2024
Abstract
The objective of this study is to examine the relationship between macroeconomic variables and the financial liquidity of companies. In this context, two main research questions were formulated. Firstly, which macroeconomic variables impact the financial liquidity of companies? Secondly, what is the direction
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The objective of this study is to examine the relationship between macroeconomic variables and the financial liquidity of companies. In this context, two main research questions were formulated. Firstly, which macroeconomic variables impact the financial liquidity of companies? Secondly, what is the direction and strength of the influence of these macroeconomic variables on the financial liquidity of companies? This study employed panel data analysis conducted on an unbalanced panel of 5327 Polish enterprises over the period 2003–2021. The primary research method employed was linear regression (pooled OLS) with robust standard errors clustered at the firm level. The main results of this study indicate that (1) the majority of macroeconomic variables, which illustrate the overall efficiency of the economic system (GDP per capita, ratio of foreign trade goods balance to GDP, CPI, and money supply), demonstrate a positive relationship with corporate liquidity; only the consumption-to-GDP ratio exhibits a negative relationship; (2) a positive relationship was observed between the number of building permits for housing and financial liquidity; (3) variables from the informal institutional environment indicate a positive relationship for the employment rate and a negative relationship for the share of the pre-working age population in the overall population; (4) the relationship between the ratio of internal expenditures on research and development to GDP and corporate liquidity is positive. This study addresses limitations of previous research by examining the impact of macroeconomic factors, particularly those from the institutional and technical environment, on corporate financial liquidity.
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Open AccessArticle
A New Approach to Build a Successful Straddle Strategy: The Analytical Option Navigator
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Orkhan Rustamov, Fuzuli Aliyev, Richard Ajayi and Elchin Suleymanov
Risks 2024, 12(7), 113; https://doi.org/10.3390/risks12070113 - 18 Jul 2024
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The study described in this paper develops a new technique which permits the execution of an open straddle strategy based on the superior volatility forecast for analyzing historical data. We extend the current litearure by measuring the volatility of an underlying asset in
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The study described in this paper develops a new technique which permits the execution of an open straddle strategy based on the superior volatility forecast for analyzing historical data. We extend the current litearure by measuring the volatility of an underlying asset in the last predefined period and comparing the actual volatility in currency with historical volatility in currency to make predictions of implied volatility. We calculated stock price volatility through an optimal holding period (OHP) and set up bars of volatility in currency. To obtain this, we solved optimization equations to find maximum and minimum movements in the volatility in currency within the defined range. We placed volatility in currency into percentile rankings and designed a straddle trading strategy based on the last OHP’s volatility in currency. The technique allows for an investor (or trader) to open either short or long positions based on calculations for a selected OHP’s volatility in currency. We applied this strategy to 130 stocks which are traded on CBOE. We developed a trading algorithm which can be used by institutional as well as individual investors. The algorithm is set to determine historical volatility in currency and forecast upcoming volatilities in currency through the understanding of the market sentiment. The empirical findings show that the stocks analyzed with the algorithm generate positive returns along a spectrum of changing volatilities of the underlying assets.
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Open AccessArticle
Government Borrowing and South African Banks’ Capital Structure: A System GMM Approach
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Ndonwabile Zimasa Mabandla and Godfrey Marozva
Risks 2024, 12(7), 112; https://doi.org/10.3390/risks12070112 - 16 Jul 2024
Abstract
This paper aimed to investigate the effects of government borrowing banks’ capital structure using a sample of banks registered in South Africa from 2012 to 2021. Despite the extensive literature on this association, few prominent researchers have studied this phenomenon in the banking
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This paper aimed to investigate the effects of government borrowing banks’ capital structure using a sample of banks registered in South Africa from 2012 to 2021. Despite the extensive literature on this association, few prominent researchers have studied this phenomenon in the banking sector. Applying the generalised method of moments (GMM) model, the study established a positive but significant effect on the South African banks’ capital structure from total government borrowing, local government borrowing and foreign government borrowing, and capital structure. Contrary to the crowding-out effects detected, the results revealed a positive and significant relationship between government borrowing and banks’ capital structure. The crowding-in effect better explains these results, where government borrowing stimulates the local market for goods and services, motivating banks to borrow more in order to meet the demand for loans. Future research should test the cointegrating and causality relationship between government borrowing and bank capital structure. Also, given that the banking sector is constrained by Basel III’s capital adequacy requirement, controlling for this factor is critical in future research.
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(This article belongs to the Special Issue Financial Analysis, Corporate Finance and Risk Management)
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Determinants of the Effectiveness of Risk Management in the Project Portfolio in the FinTech Industry
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Oliwia Khalil-Oliwa and Izabela Jonek-Kowalska
Risks 2024, 12(7), 111; https://doi.org/10.3390/risks12070111 - 4 Jul 2024
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Risk management in the project portfolio can contribute to more effective implementation of the goals of the projects, the portfolio, and the entire organization. However, in the literature on the subject, relatively little attention is paid to the determinants of this process. Moreover,
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Risk management in the project portfolio can contribute to more effective implementation of the goals of the projects, the portfolio, and the entire organization. However, in the literature on the subject, relatively little attention is paid to the determinants of this process. Moreover, the process course is rarely analyzed in a strategic context relating to the entire organization. For these reasons, this article’s primary goal is to identify the determinants of the effectiveness of risk management in the project portfolio. Research in this area was carried out in the FinTech industry, and the results were analyzed using structural equation modeling. The results indicated that the most important dimensions of the examined effectiveness are the strategic orientation of the organization and the risk management process in the project portfolio. At the level of strategic orientation, this highlights the need for coherence between the organization’s strategy and the project portfolio. At the level of risk management in the project portfolio, the primacy of ownership and control of individual risks is clearly visible.
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Open AccessArticle
Development of the Black–Scholes Model for Determining Insurance Premiums to Mitigate the Risk of Disaster Losses Using the Principles of Mutual Cooperation and Regional Economic Growth
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Titi Purwandari, Yuyun Hidayat, Sukono, Kalfin, Riza Andrian Ibrahim and Subiyanto
Risks 2024, 12(7), 110; https://doi.org/10.3390/risks12070110 - 3 Jul 2024
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The frequency and economic damage of natural disasters have increased globally over the last two decades due to climate change. This increase has an impact on the disaster insurance field, particularly in the calculation of premiums. Many regions have a shortcoming in employing
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The frequency and economic damage of natural disasters have increased globally over the last two decades due to climate change. This increase has an impact on the disaster insurance field, particularly in the calculation of premiums. Many regions have a shortcoming in employing insurance because the premium is too high compared with their budget allocation. As one of the solutions, the premium calculation can be developed by applying the cross-subsidies mechanism based on economic growth. Therefore, this research aims to develop premium models of natural disaster insurance that uniquely involve two new variables of an insured region: cross-subsidies and the economic growth rate. Another novelty is the development of the Black–Scholes model, considering the two new variables, and it is used to formulate the premium model. Following the modeling process, this study uses the model to estimate the premiums for natural disaster insurance in each province of Indonesia. The estimation results show that all new variables involved in the model novelties significantly affect the premiums. This research can be used by insurance companies to determine the premium of natural disaster insurance, which involves cross-subsidies and economic growth.
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The Complementary Nature of Financial Risk Aversion and Financial Risk Tolerance
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John Grable, Abed Rabbani and Wookjae Heo
Risks 2024, 12(7), 109; https://doi.org/10.3390/risks12070109 - 2 Jul 2024
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Financial risk aversion and financial risk tolerance are sometimes considered to be ‘opposite sides of the same coin’, with the implication being that risk aversion (a term describing the unwillingness of an investor to take risks based on a probability assessment) and risk
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Financial risk aversion and financial risk tolerance are sometimes considered to be ‘opposite sides of the same coin’, with the implication being that risk aversion (a term describing the unwillingness of an investor to take risks based on a probability assessment) and risk tolerance (an investor’s willingness to engage in a behavior based on their subjective evaluation of the uncertainty of the outcomes) are inversely-related substitutes. The purpose of this paper is to present an alternative way of viewing these constructs. We show that risk aversion and risk tolerance act as complementary factors in models designed to describe the degree of risk observed in household investment portfolios. A series of multivariate tests were used to determine that financial risk aversion is inversely related to portfolio risk, whereas financial risk tolerance is positively associated with portfolio risk. When used in the same model, the amount of explained variance in portfolio risk was increased compared to models where one, but not the other, measure was used. Overall, financial risk tolerance exhibited the largest model effect, although financial risk aversion was also important across the models analyzed in this study.
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Unified Spatial Clustering of Territory Risk to Uncover Impact of COVID-19 Pandemic on Major Coverages of Auto Insurance
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Shengkun Xie and Nathaniel Ho
Risks 2024, 12(7), 108; https://doi.org/10.3390/risks12070108 - 1 Jul 2024
Abstract
This research delves into the fusion of spatial clustering and predictive modeling within auto insurance data analytics. The primary focus of this research is on addressing challenges stemming from the dynamic nature of spatial patterns in multiple accident year claim data, by using
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This research delves into the fusion of spatial clustering and predictive modeling within auto insurance data analytics. The primary focus of this research is on addressing challenges stemming from the dynamic nature of spatial patterns in multiple accident year claim data, by using spatially constrained clustering. The spatially constrained clustering is implemented under hierarchical clustering with a soft contiguity constraint. It is highly desirable for insurance companies and insurance regulators to be able to make meaningful comparisons of loss patterns obtained from multiple reporting years that summarize multiple accident year loss metrics. By integrating spatial clustering techniques, the study not only improves the credibility of predictive models but also introduces a strategic dimension reduction method that concurrently enhances the interpretability of predictive models used. The evolving nature of spatial patterns over time poses a significant barrier to a better understanding of complex insurance systems as these patterns transform due to various factors. While spatial clustering effectively identifies regions with similar loss data characteristics, maintaining up-to-date clusters is an ongoing challenge. This research underscores the importance of studying spatial patterns of auto insurance claim data across major insurance coverage types, including Accident Benefits (AB), Collision (CL), and Third-Party Liability (TPL). The research offers regulators valuable insights into distinct risk profiles associated with different coverage categories and territories. By leveraging spatial loss data from pre-pandemic and pandemic periods, this study also aims to uncover the impact of the COVID-19 pandemic on auto insurance claims of major coverage types. From this perspective, we observe a statistically significant increase in insurance premiums for CL coverage after the pandemic. The proposed unified spatial clustering method incorporates a relabeling strategy to standardize comparisons across different accident years, contributing to a more robust understanding of the pandemic effects on auto insurance claims. This innovative approach has the potential to significantly influence data visualization and pattern recognition, thereby improving the reliability and interpretability of clustering methods.
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(This article belongs to the Special Issue Statistics, Stochastic Modelling and Quantitative Risk Management for Insurance)
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Open AccessArticle
Foreign Exchange Futures Trading and Spot Market Volatility in Thailand
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Woradee Jongadsayakul
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.
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(This article belongs to the Special Issue Volatility Modeling in Financial Market)
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Mean-Reverting Statistical Arbitrage Strategies in Crude Oil Markets
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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.
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(This article belongs to the Special Issue Portfolio Theory, Financial Risk Analysis and Applications)
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Intellectual Capital, Political Connection, and Firm Performance: Exploring from Indonesia
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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.
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(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
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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.
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(This article belongs to the Special Issue Statistical Applications to Insurance and Risk)
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An Exposition of the Gap between Public Sector and Private Sector Participation in Green Finance
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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.
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(This article belongs to the Special Issue Advancements in Actuarial Mathematics and Insurance Risk Management)
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Support of the SDGs as a New Approach to Financial Risk Management in Responsible Universities in Russia
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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
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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.
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Open AccessArticle
The Economic and Financial Health of Lithuanian Logistics Companies
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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
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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.
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(This article belongs to the Special Issue Corporate Finance and Intellectual Capital Management)
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