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Risks, Volume 11, Issue 11 (November 2023) – 20 articles

Cover Story (view full-size image): In the financial market, how are high-frequency returns of different stocks related? The author discovered tail dependence patterns from active intraday trading activities in the U.S. stock market. An innovative copula model and a unified tail dependence measure were employed to quantify and compare various tail dependence patterns, and the main findings include interactions between the upper and lower tail dependence over time and across different market sentiments. It is found that tail dependence tends to peak towards the end of the regular trading hours and the upper tail dependence tends to be stronger than the lower tail dependence for short-term returns during a market sell-off. Among the Fama—French five factors, the market excess return plays the most important role. View this paper
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32 pages, 1984 KiB  
Article
Toward Sustainable Development: Assessing the Effects of Financial Contagion on Human Well-Being in Romania
Risks 2023, 11(11), 204; https://doi.org/10.3390/risks11110204 - 20 Nov 2023
Cited by 1 | Viewed by 1457
Abstract
In a globally interconnected economy marked by volatility, this study employs the Autoregressive Distributed Lag (ARDL) model to examine financial contagion’s impact on Romania’s financial stability. It investigates both conventional and unconventional channels through which financial contagion is transmitted, emphasizing its sensitivity to [...] Read more.
In a globally interconnected economy marked by volatility, this study employs the Autoregressive Distributed Lag (ARDL) model to examine financial contagion’s impact on Romania’s financial stability. It investigates both conventional and unconventional channels through which financial contagion is transmitted, emphasizing its sensitivity to factors such as geopolitical events and investor sentiment. The study also assesses the influence of unemployment, market capitalization, and financial freedom on Romania’s Human Development Index (HDI) from 2000 to 2022. Using HDI, which encompasses health and education alongside economic aspects, the research provides a holistic view of well-being and quality of life. In addition to the ARDL model’s insights, this study expands its scope by conducting a multilinear regression analysis, with GDP as the dependent variable. We have incorporated independent variables such as HDI, transaction volume, and the BET-FI index to comprehensively assess their relationships and potential impact on Romania’s economic growth. This analytical approach unveils intricate connections between key economic and financial indicators, paving the way for a deeper understanding of how these variables interact. Furthermore, to shed light on the financial dynamics within Romania, a supplementary analysis in the Altreva Adaptive Modeler was undertaken, focusing on the BET-FI index. This software-based exploration provides a nuanced perspective on the index’s behavior and its interactions with other economic and social indicators. This additional dimension contributes to our holistic understanding of the effects of financial contagion and the implications for sustainable human development in Romania. By combining traditional econometric methodologies with cutting-edge modeling techniques, this study strives to offer a robust framework for comprehending the multifaceted nature of financial contagion and its implications for both the national economy and well-being. These findings have the potential to guide policymakers and financial institutions in implementing more effective risk management strategies, driving economic development, and ultimately enhancing the overall quality of life in Romania. Full article
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20 pages, 1528 KiB  
Article
Coupled Price–Volume Equity Models with Auto-Induced Regime Switching
Risks 2023, 11(11), 203; https://doi.org/10.3390/risks11110203 - 17 Nov 2023
Viewed by 1286
Abstract
In this work, we present a rigorous development of a model for the Price–Volume relationship of transactions introduced in 2009. For this development, we rely on the precise formulation of diffusion auto-induced regime-switching models presented in our previous work of 2020. The auto-induced [...] Read more.
In this work, we present a rigorous development of a model for the Price–Volume relationship of transactions introduced in 2009. For this development, we rely on the precise formulation of diffusion auto-induced regime-switching models presented in our previous work of 2020. The auto-induced regime-switching models referred to may be based on a finite set of stochastic differential equations (SDE)—all defined on the same bounded time interval—and a sequence of interlacing stopping times defined by the hitting threshold times of the trajectories of the solutions of the SDE. The coupling between price and volume—which we take as a proxy of liquidity—is assumed to be the following: the regime switching in the price variable occurs at the stopping times for which there is a change of region—in the product state space of price and liquidity—for the liquidity variable (and vice versa). The regimes may be defined parametrically—that is, the SDE coefficients keep the same functional form but with varying parameters—or the functional form of the SDE coefficients may change with each regime. By using the same noise source for both the price and the liquidity regime-switching models—volume (liquidity), which, in general, is not a tradable asset—we ensure that despite incorporating information on liquidity, the price part of the coupled model can be assumed to be arbitrage free and complete, allowing the pricing and hedging of derivatives in a simple way. Full article
(This article belongs to the Special Issue Risks Journal: A Decade of Advancing Knowledge and Shaping the Future)
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17 pages, 1315 KiB  
Article
Risk-Based Assessment of the Performance of Territorial Bodies of the Federal Treasury of the Russian Federation
Risks 2023, 11(11), 202; https://doi.org/10.3390/risks11110202 - 17 Nov 2023
Viewed by 1115
Abstract
This paper presents the authors’ methodology of a risk-oriented approach to assessing the performance of territorial bodies of the Federal Treasury of the Russian Federation. The proposed methodology consists in the application of adjustment coefficients, which account for the quality of the execution [...] Read more.
This paper presents the authors’ methodology of a risk-oriented approach to assessing the performance of territorial bodies of the Federal Treasury of the Russian Federation. The proposed methodology consists in the application of adjustment coefficients, which account for the quality of the execution of budgetary powers and the growth rate of the gross regional product of the corresponding territory. The goal of the study is to develop a risk-oriented methodology for assessing the contribution of the territorial bodies of the Federal Treasury to the United Nations sustainable development goals and national goals. The current study employs systemic, process based, risk-oriented approaches, statistical data analysis, and mathematical research methods. The gross regional product for the subjects of the Russian Federation is calculated for 2018–2019. Based on an analysis of Russian and foreign research on modern controlling systems and in accordance with the current concept of controlling, an attempt is made to develop a methodology for assessing the performance of the Federal Treasury and its territorial bodies. The main conclusion of the study is that the most expedient approach to assessing the efficiency of territorial bodies of the Federal Treasury is through the balanced scorecard system built in accordance with the strategic goals of the Federal Treasury, the national goals of the Russian Federation, and the UN SDGs. Full article
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14 pages, 506 KiB  
Article
Market Reaction to Delisting Announcements in Frontier Markets: Evidence from the Vietnam Stock Market
Risks 2023, 11(11), 201; https://doi.org/10.3390/risks11110201 - 16 Nov 2023
Viewed by 1109
Abstract
This paper aims to measure the effects of delisting on stock returns for the Vietnam stock market. This study employs a sample of 118 stocks that were compulsorily delisted from the market between January 2011 and December 2021. Using an event study methodology, [...] Read more.
This paper aims to measure the effects of delisting on stock returns for the Vietnam stock market. This study employs a sample of 118 stocks that were compulsorily delisted from the market between January 2011 and December 2021. Using an event study methodology, the empirical findings confirm that the delisting has negative effects on stock returns in the Vietnam stock market. Specifically, results derived from tests show that the average abnormal return of delisted stocks continuously declines during three trading days following the announcement of delisting. Moreover, it is found that the differences in cumulative abnormal returns between post-delisting and pre-delisting periods are significantly negative for all tracking periods. Apart from the negative effect of delisting on stock abnormal returns, we also find that the impact of delisting on stock returns for smaller companies is greater than for bigger companies. These results imply that investors can earn abnormal returns by using delisting information in the Vietnam stock market. Full article
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26 pages, 5597 KiB  
Article
Macroeconomic Risks and Monetary Policy in Central European Countries: Parallels in the Czech Republic, Hungary, and Poland
Risks 2023, 11(11), 200; https://doi.org/10.3390/risks11110200 - 15 Nov 2023
Viewed by 1282
Abstract
Changes in interest rates, inflation, and exchange rates are the main components of macroeconomic risks (financial risks) in projects evaluation. However, the conduct of monetary policy as well as its impact on the economic environment is seldom considered as an important component of [...] Read more.
Changes in interest rates, inflation, and exchange rates are the main components of macroeconomic risks (financial risks) in projects evaluation. However, the conduct of monetary policy as well as its impact on the economic environment is seldom considered as an important component of macroeconomic risks. In this paper, we offer a simple framework to analyze the conduct of monetary policy. We examine the stabilizing properties of monetary policy, its impact, and the parallels in the monetary policy approaches taken in the Czech Republic, Hungary, and Poland until the pandemic. We provide a simple theoretical background to motivate the main elements of the debate and the choice of policy strategy. We then rationalize the adoption of a form of flexible inflation targeting (FIT). It is characterized by an explicit concern over exchange rates. The empirical evidence, consisting of calibrated and extended Taylor rules, together with local projections estimates, suggests that monetary policy has been practiced with considerable flexibility by all three central banks and has contributed to business cycle stabilization in the region. Most notably, the exchange rate plays an important role in the conduct of monetary policy. Full article
(This article belongs to the Special Issue Risks Journal: A Decade of Advancing Knowledge and Shaping the Future)
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18 pages, 1369 KiB  
Article
Domain Knowledge Features versus LASSO Features in Predicting Risk of Corporate Bankruptcy—DEA Approach
Risks 2023, 11(11), 199; https://doi.org/10.3390/risks11110199 - 15 Nov 2023
Viewed by 1338
Abstract
Predicting the risk of corporate bankruptcy is one of the most important challenges for researchers dealing with the issue of financial health evaluation. The risk of corporate bankruptcy is most often assessed with the use of early warning models. The results of these [...] Read more.
Predicting the risk of corporate bankruptcy is one of the most important challenges for researchers dealing with the issue of financial health evaluation. The risk of corporate bankruptcy is most often assessed with the use of early warning models. The results of these models are significantly influenced by the financial features entering them. The aim of this paper was to select the most suitable financial features for bankruptcy prediction. The research sample consisted of enterprises conducting a business within the Slovak construction industry. The features were selected using the domain knowledge (DK) approach and Least Absolute Shrinkage and Selection Operator (LASSO). The performance of VRS DEA (Variable Returns to Scale Data Envelopment Analysis) models was assessed with the use of accuracy, ROC (Receiver Operating Characteristics) curve, AUC (Area Under the Curve) and Somers’ D. The results show that the DK+DEA model achieved slightly better AUC and Somers’ D compared to the LASSO+DEA model. On the other hand, the LASSO+DEA model shows a smaller deviation in the number of identified businesses on the financial distress frontier. The added value of this research is the finding that the application of DK features achieves significant results in predicting businesses’ bankruptcy. The added value for practice is the selection of predictors of bankruptcy for the analyzed sample of enterprises. Full article
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18 pages, 449 KiB  
Article
Country Risk and Financial Stability: A Focus on Commercial Banks in Africa
Risks 2023, 11(11), 198; https://doi.org/10.3390/risks11110198 - 14 Nov 2023
Viewed by 1278
Abstract
This paper employs dynamic panel models to investigate the impact of country risk on the financial stability of banks in Africa. Using country risk and bank specific data for 10 African countries over the period of 2000 and 2021, the results reveal that [...] Read more.
This paper employs dynamic panel models to investigate the impact of country risk on the financial stability of banks in Africa. Using country risk and bank specific data for 10 African countries over the period of 2000 and 2021, the results reveal that African countries have a high country risk exposure. The country risk negatively and significantly affects African bank stability. The study findings suggest that compliance with at least Basel II capital requirements is needed to protect African banks from the negative effects of country risk on their stability in the short run. However, the adverse effects of prolonged country risk are mitigated by the compliance with higher Basel capital requirements in the long run. The results further show that an efficient legal and regulatory framework is essential to complement the capital buffer against country risk. Policies must be introduced to reduce country risk to enable African banks to adequately support the African economy in good and challenging times. Overall, country risk remains a threatening factor for bank stability, and consequently, banks need adequate capital to reduce the impact of country risk on bank stability in Africa. Full article
30 pages, 446 KiB  
Article
Empirical Testing of Models of Autoregressive Conditional Heteroscedasticity Used for Prediction of the Volatility of Bulgarian Investment Funds
Risks 2023, 11(11), 197; https://doi.org/10.3390/risks11110197 - 14 Nov 2023
Viewed by 1295
Abstract
The relevance of the development is determined by the possibility of testing a complex analytical methodology for forecasting the daily volatility of Bulgarian investment funds, which will support the investment community in making adequate investment decisions. The used risk attribution quantification models GARCH [...] Read more.
The relevance of the development is determined by the possibility of testing a complex analytical methodology for forecasting the daily volatility of Bulgarian investment funds, which will support the investment community in making adequate investment decisions. The used risk attribution quantification models GARCH (1.1), EGARCH (1.1), GARCH-M (1.1) and TGARCH (1.1) are adapted to predict the volatility of investment funds. The current development focuses on forecasting the risk concentration of investment funds (in Bulgaria) through the testing of complex, analytical and specialized models from the GARCH group. The object of the study includes quantitative analysis, estimation and forecasting of daily volatility through the models GARCH, EGARCH, GARCH-M and TGARCH with specification (1.1). The research covers the net balance sheet value of forty-two investment funds for the period from 13 July 2020 to 13 July 2023, where the results of the research show that according to three of the models GARCH, EGARCH and GARCH-M with the highest risk concentration the investment fund “Golden Lev Index 30” stands out. An exception to the thus formed trend is related to the TGARCH model in which the future conditional volatility is with the “EF Rapid” investment fund. When testing the models, we found that the GARCH model and the EGARCH model successfully optimize the regression parameters of the final equation for all analyzed investment funds, and as a result, valid forecasts are formed. In the case of the remaining two GARCH-M and TGARCH models, the impossibility of applicability of the model for some investment funds was found because of the optimization procedure, in which the parameters of the models have a value of zero. The present study is a unique mechanism for forecasting the daily volatility of Bulgarian investment funds, which further assists investors in risk assessment and is a prerequisite for making adequate and responsible investment decisions. The wide-spectrum toolkit of risk forecasting models allows their testing in investment funds with different risk natures (high-risk, balanced and low-risk). From a research point of view, in future research dedicated to modeling the risk attribution of investment funds, the analytical toolkit can be enriched with the following models: QGARCH, PGARCH, GJR-GARCH, IGARCH, SGARCH, AVGARCH, NGARCH and GAS. From a statistical point of view, we can apply the analyzed models to different probability distributions in order to describe the risky nature of investment funds. Full article
16 pages, 2692 KiB  
Article
Claims Modelling with Three-Component Composite Models
Risks 2023, 11(11), 196; https://doi.org/10.3390/risks11110196 - 13 Nov 2023
Cited by 1 | Viewed by 1166
Abstract
In this paper, we develop a number of new composite models for modelling individual claims in general insurance. All our models contain a Weibull distribution for the smallest claims, a lognormal distribution for the medium-sized claims, and a long-tailed distribution for the largest [...] Read more.
In this paper, we develop a number of new composite models for modelling individual claims in general insurance. All our models contain a Weibull distribution for the smallest claims, a lognormal distribution for the medium-sized claims, and a long-tailed distribution for the largest claims. They provide a more detailed categorisation of claims sizes when compared to the existing composite models which differentiate only between the small and large claims. For each proposed model, we express four of the parameters as functions of the other parameters. We fit these models to two real-world insurance data sets using both maximum likelihood and Bayesian estimation, and test their goodness-of-fit based on several statistical criteria. They generally outperform the existing composite models in the literature, which comprise only two components. We also perform regression using the proposed models. Full article
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17 pages, 2980 KiB  
Article
Discovering Intraday Tail Dependence Patterns via a Full-Range Tail Dependence Copula
by
Risks 2023, 11(11), 195; https://doi.org/10.3390/risks11110195 - 11 Nov 2023
Viewed by 1159
Abstract
In this research, we employ a full-range tail dependence copula to capture the intraday dynamic tail dependence patterns of 30 s log returns among stocks in the US market in the year of 2020, when the market experienced a significant sell-off and a [...] Read more.
In this research, we employ a full-range tail dependence copula to capture the intraday dynamic tail dependence patterns of 30 s log returns among stocks in the US market in the year of 2020, when the market experienced a significant sell-off and a rally thereafter. We also introduce a model-based unified tail dependence measure to directly model and compare various tail dependence patterns. Using regression analysis of the upper and lower tail dependence simultaneously, we have identified some interesting intraday tail dependence patterns, such as interactions between the upper and lower tail dependence over time among growth and value stocks and in different market regimes. Our results indicate that tail dependence tends to peak towards the end of the regular trading hours, and, counter-intuitively, upper tail dependence tends to be stronger than lower tail dependence for short-term returns during a market sell-off. Furthermore, we investigate how the Fama–French five factors affect the intraday tail dependence patterns and provide plausible explanations for the occurrence of these phenomena. Among these five factors, the market excess return plays the most important role, and our study suggests that when there is a moderate positive excess return, both the upper and lower tails tend to reach their lowest dependence levels. Full article
(This article belongs to the Special Issue Risks Journal: A Decade of Advancing Knowledge and Shaping the Future)
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21 pages, 1107 KiB  
Article
New Classes of Distortion Risk Measures and Their Estimation
Risks 2023, 11(11), 194; https://doi.org/10.3390/risks11110194 - 10 Nov 2023
Viewed by 1115
Abstract
In this paper, we present a new method to construct new classes of distortion functions. A distortion function maps the unit interval to the unit interval and has the characteristics of a cumulative distribution function. The method is based on the transformation of [...] Read more.
In this paper, we present a new method to construct new classes of distortion functions. A distortion function maps the unit interval to the unit interval and has the characteristics of a cumulative distribution function. The method is based on the transformation of an existing non-negative random variable whose distribution function, named the generating distribution, may contain more than one parameter. The coherency of the resulting risk measures is ensured by restricting the parameter space on which the distortion function is concave. We studied cases when the generating distributions are exponentiated exponential and Gompertz distributions. Closed-form expressions for risk measures were derived for uniform, exponential, and Lomax losses. Numerical and graphical results are presented to examine the effects of the parameter values on the risk measures. We then propose a simple plug-in estimate of risk measures and conduct simulation studies to compare and demonstrate the performance of the proposed estimates. The plug-in estimates appear to perform slightly better than the well-known L-estimates, but also suffer from biases when applied to heavy-tailed losses. Full article
(This article belongs to the Special Issue Advancements in Actuarial Mathematics and Insurance Risk Management)
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17 pages, 743 KiB  
Article
Copula Models of COVID-19 Mortality in Minnesota and Wisconsin
Risks 2023, 11(11), 193; https://doi.org/10.3390/risks11110193 - 03 Nov 2023
Viewed by 954
Abstract
In this study, we assess COVID-19-related mortality in Minnesota and Wisconsin with the aim of demonstrating both the temporal dynamics and the magnitude of the pandemic’s influence from an actuarial risk standpoint. In the initial segment of this paper, we discuss the methodology [...] Read more.
In this study, we assess COVID-19-related mortality in Minnesota and Wisconsin with the aim of demonstrating both the temporal dynamics and the magnitude of the pandemic’s influence from an actuarial risk standpoint. In the initial segment of this paper, we discuss the methodology successfully applied to describe associations in financial and engineering time series. By applying time series analysis, specifically the autoregressive integrated with moving average methods (ARIMA), to weekly mortality figures at the national or state level, we subsequently delve into a marginal distribution examination of ARIMA residuals, addressing any deviation from the standard normality assumption. Thereafter, copulas are utilized to architect joint distribution models across varied geographical domains. The objective of this research is to offer a robust statistical model that utilizes observed mortality datasets from neighboring states and nations to facilitate precise short-term mortality projections. In the subsequent section, our focus shifts to a detailed scrutiny of the statistical interdependencies manifesting between Minnesota and Wisconsin’s weekly COVID-19 mortality figures, adjusted for the time series structure. Leveraging open-source data made available by the CDC and pertinent U.S. state government entities, we apply the ARIMA methodology with subsequent residual distribution modeling. To establish dependence patterns between the states, pair copulas are employed to articulate the relationships between the ARIMA residuals, drawing from fully parametric models. We explore several classes of copulas, comprising both elliptic and Archimedean families. Emphasis is placed on copula model selection. Student t-copula with the marginals modeled by non-standard t-distribution is suggested for ARIMA residuals of Minnesota and Wisconsin COVID mortality as the model of choice based on information criteria and tail cumulation. The copula approach is suggested for the construction of short-term prediction intervals for COVID-19 mortality based on publicly available data. Full article
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19 pages, 774 KiB  
Article
Unveiling the Role of Investment Tangibility on Financial Leverage: Insights from African-Listed Firms
Risks 2023, 11(11), 192; https://doi.org/10.3390/risks11110192 - 01 Nov 2023
Viewed by 1378
Abstract
The asset structure of a firm plays a pivotal role in determining its leverage. A higher proportion of physical assets is often associated with high debt ratios. This study explores the impact of investment tangibility on financial leverage, examining both tangible and intangible [...] Read more.
The asset structure of a firm plays a pivotal role in determining its leverage. A higher proportion of physical assets is often associated with high debt ratios. This study explores the impact of investment tangibility on financial leverage, examining both tangible and intangible investments. Using a dynamic panel data model estimated through the two-step system generalized method of moments (GMM), we analyse a dataset encompassing 815 non-financial listed firms from 22 African stock markets. The results show that African firms have higher inclinations to invest in physical assets. We found a statistically significant negative relationship between leverage and tangible and intangible investments. The findings indicate that African firms tend to maintain lower leverages regardless of whether they invest in tangible or intangible assets. The observed relationship aligns with the hypothesis that high-growth firms, in their expansion efforts, strategically tend to opt for low debt to mitigate the agency costs associated with debt and to help prevent underinvestment. This outcome underscores the interconnected nature of financing and investment decisions. This research contributes to the literature on financial leverage and investment by dissecting investments into tangible and non-tangible components and highlighting their distinct impacts on leverage. Moreover, it provides empirical evidence for previously unexplored African firms, shedding light on the reasons behind the relatively low leverage levels observed in African firms. Full article
(This article belongs to the Special Issue Corporate Finance and Intellectual Capital Management)
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22 pages, 515 KiB  
Article
Inflation, Equity Market Volatility, and Bond Prices: Evidence from G7 Countries
Risks 2023, 11(11), 191; https://doi.org/10.3390/risks11110191 - 31 Oct 2023
Viewed by 1345
Abstract
This study examines the impacts of the US inflation rate on the bond prices of G7 countries across different maturities using inflation-induced equity market volatility (EMV) to better account for bond price determinants. The regression model, a GED-GARCH (1,1) procedure, is adopted to [...] Read more.
This study examines the impacts of the US inflation rate on the bond prices of G7 countries across different maturities using inflation-induced equity market volatility (EMV) to better account for bond price determinants. The regression model, a GED-GARCH (1,1) procedure, is adopted to deal with the volatility clustering and fat tail features in bond return estimation. The testing results indicate that the inflation rate has a negative effect on bond returns across different maturities, although an exception occurs for longer maturities in Japan. Evidence shows that US inflation has a significant impact on bond returns for the non-US G7 countries. The negative effects from US inflation are more profound than those from the domestic market (expect in Japan). This study introduces the equity market volatility arising from inflation or the Fed’s interest rate change; this variable produces market volatility that has a positive effect on bond returns, offsetting part of the original negative effect from a rise in inflation. Full article
43 pages, 1313 KiB  
Article
Business Risks in COVID-19 Crisis Dataset Modeling: Regulatory vs. Marketing Tools of Risk Management
Risks 2023, 11(11), 190; https://doi.org/10.3390/risks11110190 - 31 Oct 2023
Viewed by 1313
Abstract
The research aims to identify the most promising regulatory and marketing tools for business risk management in the COVID-19 crisis and develop recommendations for improving the practice of these tools from a post-pandemic perspective. This paper is devoted to the scientific search for [...] Read more.
The research aims to identify the most promising regulatory and marketing tools for business risk management in the COVID-19 crisis and develop recommendations for improving the practice of these tools from a post-pandemic perspective. This paper is devoted to the scientific search for answers to two research questions: RQ1: What tactical tools of business risk management are most effective in the COVID-19 crisis? RQ2: How to carry out strategic risk management of the business from a post-COVID perspective? The authors perform dataset modeling of business risks in the COVID-19 crisis and data analysis of the post-pandemic perspective of managing these risks, relying on data for 2016–2023, reflecting international experience in a representative sample. The key conclusion of this research is that the most complete and effective business risk management in times of COVID-19 crisis requires the integrated application of tools of state and corporate governance, that is, two-tier management: At the state and business levels. On this basis, the authors recommended applying the systemic approach to business risk management in times of the COVID-19 crisis, which includes a set of the most effective regulatory (financial support from the state budget and protectionism) and marketing (use of big data and analytics) tools of business risk management. The practical significance of the research results is that the recommended systemic approach to using regulatory and marketing tools can improve the effectiveness of tactical and strategic risk management in the COVID-19 crisis, thereby increasing business resilience to this crisis. The novelty is due to the fact that we selected the most effective tools of business risk management under the conditions of the COVID-19 crisis and proved the necessity to combine the tools of state and corporate management, which are substantiated, for the first time, not as mutually interchangeable, but complementary practices of risk management in the unique context of the COVID-19 crisis. Full article
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27 pages, 569 KiB  
Article
The Dynamic Endogeneity Issue between Corporate Ownership Structure and Real-Based Earnings Manipulation in an Emerging Market: Advanced Dynamic Panel Model
Risks 2023, 11(11), 189; https://doi.org/10.3390/risks11110189 - 30 Oct 2023
Viewed by 1171
Abstract
This study aims to contribute to the existing literature by examining the relationship between corporate governance (CG) attributes and real-based earnings management (REM) in the context of an emerging market economy. The study employs a sample of 78 Egyptian Exchange (EGX)-listed companies covering [...] Read more.
This study aims to contribute to the existing literature by examining the relationship between corporate governance (CG) attributes and real-based earnings management (REM) in the context of an emerging market economy. The study employs a sample of 78 Egyptian Exchange (EGX)-listed companies covering the period from 2008 to 2017, yielding a total of 780 observations. To address dynamic endogeneity concerns between CG mechanisms and REM, the dynamic panel system-generalized method of moments (SGMM) estimator is used as the main analytical tool. The findings reveal that managerial and family ownership are negatively and significantly correlated with REM proxies, except for the ABCFO measure. By contrast, government and institutional ownership exhibit contrasting results, depending on the REM proxies used. The CG-EM relationship is influenced by several conflicting theoretical perspectives, including agency theory, institutional theory, stewardship theory, and resource dependence theory, resulting in inconsistent empirical findings. To the best of the authors’ knowledge, this study is the first to detect Real-earnings manipulation practices (REM) in the Egyptian context using six models to confirm the validity, reliability, and robustness of the findings. Additionally, the study employs an advanced statistical technique that considers endogeneity, heteroscedasticity, and simultaneity in the relationship between CG mechanisms and earnings quality. The results highlight the importance of considering the institutional and legal context of a country when analyzing the impact of corporate governance mechanisms on earnings quality, as the practice and implementation of governance mechanisms vary across countries. Full article
17 pages, 590 KiB  
Article
Model Uncertainty and Selection of Risk Models for Left-Truncated and Right-Censored Loss Data
Risks 2023, 11(11), 188; https://doi.org/10.3390/risks11110188 - 30 Oct 2023
Viewed by 1113
Abstract
Insurance loss data are usually in the form of left-truncation and right-censoring due to deductibles and policy limits, respectively. This paper investigates the model uncertainty and selection procedure when various parametric models are constructed to accommodate such left-truncated and right-censored data. The joint [...] Read more.
Insurance loss data are usually in the form of left-truncation and right-censoring due to deductibles and policy limits, respectively. This paper investigates the model uncertainty and selection procedure when various parametric models are constructed to accommodate such left-truncated and right-censored data. The joint asymptotic properties of the estimators have been established using the Delta method along with Maximum Likelihood Estimation when the model is specified. We conduct the simulation studies using Fisk, Lognormal, Lomax, Paralogistic, and Weibull distributions with various proportions of loss data below deductibles and above policy limits. A variety of graphic tools, hypothesis tests, and penalized likelihood criteria are employed to validate the models, and their performances on the model selection are evaluated through the probability of each parent distribution being correctly selected. The effectiveness of each tool on model selection is also illustrated using well-studied data that represent Wisconsin property losses in the United States from 2007 to 2010. Full article
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37 pages, 582 KiB  
Article
Rank-Based Multivariate Sarmanov for Modeling Dependence between Loss Reserves
Risks 2023, 11(11), 187; https://doi.org/10.3390/risks11110187 - 26 Oct 2023
Viewed by 1316
Abstract
The interdependence between multiple lines of business has an important impact on determining loss reserves and risk capital, which are crucial for the solvency of a property and casualty (P&C) insurance company. In this work, we introduce the two-stage inference method using the [...] Read more.
The interdependence between multiple lines of business has an important impact on determining loss reserves and risk capital, which are crucial for the solvency of a property and casualty (P&C) insurance company. In this work, we introduce the two-stage inference method using the Sarmanov family of multivariate distributions to the actuarial literature. In fact, we study rank-based methods using the Sarmanov distribution to adequately estimate the loss reserves and properly capture the dependence between lines of business. An inadequate choice of the dependence structure may negatively impact the estimation of the marginals and, hence, the reserve. Thus, we propose a two-stage inference strategy in this research to address this, while taking advantage of the flexibility of the Sarmanov distribution. We show that this strategy leads to a more robust estimation, and better captures the dependence between the risks. We also show that it generates smaller risk capital and a better diversification benefit. We extend the model to the multivariate case with more than two lines of business. To illustrate and validate our methods, we use three different sets of real data from both a major US property–casualty insurer and a large Canadian insurance company. Full article
(This article belongs to the Special Issue Applied Financial and Actuarial Risk Analytics)
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9 pages, 810 KiB  
Communication
The Effects of Disaggregate Oil Shocks on the Aggregate Expected Skewness of the United States
Risks 2023, 11(11), 186; https://doi.org/10.3390/risks11110186 - 26 Oct 2023
Viewed by 1124
Abstract
We examine the impact of the global economic activity, oil supply, oil-specific consumption demand, and oil inventory demand shocks on the expected aggregate skewness of the United States (US) economy, obtained based on a data-rich environment involving 211 macroeconomic and financial variables in [...] Read more.
We examine the impact of the global economic activity, oil supply, oil-specific consumption demand, and oil inventory demand shocks on the expected aggregate skewness of the United States (US) economy, obtained based on a data-rich environment involving 211 macroeconomic and financial variables in the quarterly period of 1975:Q1 to 2022:Q2. We find that positive oil supply and global economic activity shocks increase the expected macroeconomic skewness in a statistically significant way, with the effects being relatively more pronounced in the lower regime of the aggregate skewness factor, i.e., when the US is witnessing downside risks. Interestingly, oil-specific consumption demand and oil inventory demand shocks contain no predictive ability for the overall expected skewness. With skewness being a metric for policymakers to communicate their beliefs about the path of future risks, our results have important implications for policy decisions. Full article
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28 pages, 2167 KiB  
Article
Model Error (or Ambiguity) and Its Estimation, with Particular Application to Loss Reserving
Risks 2023, 11(11), 185; https://doi.org/10.3390/risks11110185 - 25 Oct 2023
Viewed by 1167
Abstract
This paper is concerned with the estimation of forecast error, particularly in relation to insurance loss reserving. Forecast error is generally regarded as consisting of three components, namely parameter, process and model errors. The first two of these components, and their estimation, are [...] Read more.
This paper is concerned with the estimation of forecast error, particularly in relation to insurance loss reserving. Forecast error is generally regarded as consisting of three components, namely parameter, process and model errors. The first two of these components, and their estimation, are well understood, but less so model error. Model error itself is considered in two parts: one part that is capable of estimation from past data (internal model error), and another part that is not (external model error). Attention is focused here on internal model error. Estimation of this error component is approached by means of Bayesian model averaging, using the Bayesian interpretation of the LASSO. This is used to generate a set of admissible models, each with its prior probability and likelihood of observed data. A posterior on the model set, conditional on the data, may then be calculated. An estimate of model error (for a loss reserve estimate) is obtained as the variance of the loss reserve according to this posterior. The population of models entering materially into the support of the posterior may turn out to be “thinner” than desired, and bootstrapping of the LASSO is used to increase this population. This also provides the bonus of an estimate of parameter error. It turns out that the estimates of parameter and model errors are entangled, and dissociation of them is at least difficult, and possibly not even meaningful. These matters are discussed. The majority of the discussion applies to forecasting generally, but numerical illustration of the concepts is given in relation to insurance data and the problem of insurance loss reserving. Full article
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