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Risks
Volume 12
Issue 7
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Risks, Volume 12, Issue 7 (July 2024) – 15 articles

Cover Story (view full-size image): Financial risk aversion and financial risk tolerance are often seen as ‘opposite sides of the same coin’, with risk aversion indicating unwillingness to take risks and risk tolerance representing willingness to engage in risky behavior. This paper presents an alternative view, demonstrating that risk aversion and tolerance are complementary in models describing household investment portfolio risks. Multivariate tests reveal that although correlated as expected, risk aversion and risk tolerance are not substitutes. Using both measures in the same model increases the explained variance in portfolio risk. Although risk tolerance had the largest effect, risk aversion was also statistically significant in the models analyzed. View this paper
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15 pages, 656 KiB  
Article
Forecasting Age- and Sex-Specific Survival Functions: Application to Annuity Pricing
by Shaokang Wang, Han Lin Shang, Leonie Tickle and Han Li
Risks 2024, 12(7), 117; https://doi.org/10.3390/risks12070117 - 22 Jul 2024
Viewed by 984
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, [...] Read more.
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. Full article
(This article belongs to the Special Issue Risks Journal: A Decade of Advancing Knowledge and Shaping the Future)
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19 pages, 1077 KiB  
Article
Towards Diagnosing and Mitigating Behavioral Cyber Risks
by Carlo Pugnetti, Albena Björck, Reto Schönauer and Carlos Casián
Risks 2024, 12(7), 116; https://doi.org/10.3390/risks12070116 - 19 Jul 2024
Viewed by 947
Abstract
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 [...] Read more.
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. Full article
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19 pages, 718 KiB  
Article
The Impact of FinTech Adoption on Traditional Financial Inclusion in Sub-Saharan Africa
by Abdul Karim Kamara and Baorong Yu
Risks 2024, 12(7), 115; https://doi.org/10.3390/risks12070115 - 19 Jul 2024
Viewed by 1703
Abstract
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 [...] Read more.
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. Full article
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22 pages, 451 KiB  
Article
Influence of Macroeconomic Factors on Financial Liquidity of Companies: Evidence from Poland
by Jarosław Nowicki, Piotr Ratajczak and Dawid Szutowski
Risks 2024, 12(7), 114; https://doi.org/10.3390/risks12070114 - 18 Jul 2024
Viewed by 1281
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 [...] Read more.
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. Full article
17 pages, 7649 KiB  
Article
A New Approach to Build a Successful Straddle Strategy: The Analytical Option Navigator
by Orkhan Rustamov, Fuzuli Aliyev, Richard Ajayi and Elchin Suleymanov
Risks 2024, 12(7), 113; https://doi.org/10.3390/risks12070113 - 18 Jul 2024
Viewed by 1461
Abstract
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 [...] Read more.
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. Full article
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16 pages, 711 KiB  
Article
Government Borrowing and South African Banks’ Capital Structure: A System GMM Approach
by Ndonwabile Zimasa Mabandla and Godfrey Marozva
Risks 2024, 12(7), 112; https://doi.org/10.3390/risks12070112 - 16 Jul 2024
Viewed by 899
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 [...] Read more.
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. Full article
(This article belongs to the Special Issue Financial Analysis, Corporate Finance and Risk Management)
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27 pages, 1930 KiB  
Article
Determinants of the Effectiveness of Risk Management in the Project Portfolio in the FinTech Industry
by Oliwia Khalil-Oliwa and Izabela Jonek-Kowalska
Risks 2024, 12(7), 111; https://doi.org/10.3390/risks12070111 - 4 Jul 2024
Cited by 1 | Viewed by 1157
Abstract
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, [...] Read more.
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. Full article
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21 pages, 11889 KiB  
Article
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
by 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
Viewed by 1106
Abstract
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 [...] Read more.
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. Full article
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16 pages, 649 KiB  
Article
The Complementary Nature of Financial Risk Aversion and Financial Risk Tolerance
by John Grable, Abed Rabbani and Wookjae Heo
Risks 2024, 12(7), 109; https://doi.org/10.3390/risks12070109 - 2 Jul 2024
Cited by 1 | Viewed by 1397
Abstract
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 [...] Read more.
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. Full article
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22 pages, 6143 KiB  
Article
Unified Spatial Clustering of Territory Risk to Uncover Impact of COVID-19 Pandemic on Major Coverages of Auto Insurance
by Shengkun Xie and Nathaniel Ho
Risks 2024, 12(7), 108; https://doi.org/10.3390/risks12070108 - 1 Jul 2024
Viewed by 832
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 [...] Read more.
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. Full article
(This article belongs to the Special Issue Statistics, Stochastic Modelling and Quantitative Risk Management for Insurance)
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21 pages, 2172 KiB  
Article
Foreign Exchange Futures Trading and Spot Market Volatility in Thailand
by Woradee Jongadsayakul
Risks 2024, 12(7), 107; https://doi.org/10.3390/risks12070107 - 26 Jun 2024
Viewed by 1419
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 [...] Read more.
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)
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19 pages, 1046 KiB  
Article
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
Viewed by 3430
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 [...] Read more.
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)
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16 pages, 390 KiB  
Article
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
Viewed by 1269
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 [...] Read more.
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)
18 pages, 523 KiB  
Article
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
Cited by 1 | Viewed by 822
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 [...] Read more.
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)
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13 pages, 372 KiB  
Article
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
Viewed by 1085
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 [...] Read more.
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)
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