Risks, Volume 9, Issue 12 (December 2021) – 20 articles

The aim of this article is to analyse and assess the impact of the COVID-19 pandemic on the consumer credit market in the countries of the Visegrad Group (V4, i.e., the Czech Republic, Poland, Slovakia, and Hungary). There is no doubt that the pandemic has determined the amount of household debt due to consumer credit in the V4 group, and thus the question arises of how the pandemic affects the propensity of households to take out loans and the propensity to lend to them, and therefore whether it affects both the behaviour of borrowers and lenders. The study used the time series and multiple linear regression methods. The results of the study show that the Covid-19 pandemic has determined the level of household debt in the V4 group and is not indifferent to household decisions regarding taking out consumer loans. Although the research is preliminary, it has contributed to some extent to a better understanding of household indebtedness at a time of turbulence and instability resulting from health factors in V4 countries. In the future, this research will serve as the basis for future research on the phenomenon of household indebtedness in other countries. Full article

This paper presents the construction of a particle filter, which incorporates elements inspired by genetic algorithms, in order to achieve accelerated adaptation of the estimated posterior distribution to changes in model parameters. Specifically, the filter is designed for the situation where the subsequent data in online sequential filtering does not match the model posterior filtered based on data up to a current point in time. The examples considered encompass parameter regime shifts and stochastic volatility. The filter adapts to regime shifts extremely rapidly and delivers a clear heuristic for distinguishing between regime shifts and stochastic volatility, even though the model dynamics assumed by the filter exhibit neither of those features. Full article

An upward trend in the share of cash in GDP has been observed since the beginning of the 21st century and has not yet been fully explained in the literature. In fact, the interest rate is the only variable that has been well researched and well confirmed as a determinant of the cash/GDP ratio. The novelty of this study is primarily considering new determinants of the share of cash in GDP (including in particular monetization and financial development), as well as testing the significance of uncertainty and institutions. The data cover the period 2001–2020 for 82 countries. The most important conclusions include: the share of cash in GDP is primarily dependent on its lagged values (payment habits) and the ultra-loose monetary policy of central banks. However, some other variables also contribute to this process—such as monetization and crises in the real economy. Full article

The aim of this study was to examine the impact of environmental, social, and governance (ESG) measures on credit ratings given to non-financial institutions by the largest credit rating agencies according to economic sector divisions. The hypotheses were as follows: a strong negative impact on non-financial institutions’ credit rating changes will result from ESG risk changes, and the reaction of credit rating changes will vary in different sectors. Panel event models were used to verify these hypotheses. The study used data from the Thomson Reuters Database for the period 2010–2020. The analysis was based on the literature on credit rating determinants and on papers and reports on COVID-19, ESG factors, and their impact on credit rating changes. Linear decomposition was used for the analysis. To verify these hypotheses, long-term issuer credit ratings presented by Moody’s and Fitch for European companies listed on these stock exchanges have been used. In the analyses, financial and non-financial factors were also considered. The results suggested that, within the last year, the methodology presented by credit rating agencies has changed, and ESG factors are one of the basic measures that are used to verify credit rating changes, especially those related to the pandemic. Full article

An audit team includes engagement partners, CPAs, and staff. Among them, partners play a vital role in performing tasks that require expertise and experience, such as analyzing and understanding the industry, and supervising the overall audit process. In detail, the partners establish an audit plan, determine the overall audit time, provide the audit input ratio of the engagement team, and review the audit reports. This study examines for association between the partner’s audit hour ratio and audit quality depending on the client firms’ characteristics. Although the role of partners is important, the information about partner audit hours is limited. However, the Korean government requires audit firms to disclose the partner hour information in the audit report starting in the 2014 fiscal year. By the disclosure, it is possible to examine the association between partner audit hours and audit quality. In this study, the information on partner audit hour is hand-collected from the firms’ business reports. Using 6340 observations from 2014 to 2017, the partner audit hour ratio is associated with audit quality, under the characteristics of client firms. Firms’ risks are adopted for client characteristics, and we focused on the operation of internal control. The internal control operation level is measured by the following: (1) the ratio of internal control personnel and (2) experience of the internal control personnel in the accounting and IT departments. The result suggests that for the firms where internal control is not effectively operated, partners make more effort to enhance audit quality. Full article

Cyber insurance ratemaking (CIRM) is a procedure used to set rates (or prices) for cyber insurance products provided by insurance companies. Rate estimation is a critical issue for cyber insurance products. This problem arises because of the unavailability of actuarial data and the uncertainty of normative standards of cyber risk. Most cyber risk analyses do not consider the connection between Information Communication and Technology (ICT) sources. Recently, a cyber risk model was developed that considered the network structure. However, the analysis of this model remains limited to an unweighted network. To address this issue, we propose using a graph mining approach (GMA) to CIRM, which can be applied to obtain fair and competitive prices based on weighted network characteristics. This study differs from previous studies in that it adds the GMA to CIRM and uses communication models to explain the frequency of communications as weights in the network. We used the heterogeneous generalized susceptible-infectious-susceptible model to accommodate different infection rates. Our approach adds up to the existing method because it considers the communication frequency and GMA in CIRM. This approach results in heterogeneous premiums. Additionally, GMA can choose more active communications to reflect high communications contribution in the premiums or rates. This contribution is not found when the infection rates are the same. Based on our experimental results, it is apparent that this method can produce more reasonable and competitive prices than other methods. The prices obtained with GMA and communication factors are lower than those obtained without GMA and communication factors. Full article

In the financial world, the importance of “downside risk” and “higher moments” has been emphasized, predominantly in developing countries such as Pakistan, for a substantial period. Consequently, this study tests four models for a suitable capital asset pricing model. These models are CAPM’s beta, beta replaced by skewness (gamma), CAPM’s beta with gamma, downside beta CAPM (DCAPM), downside beta replaced by downside gamma, and CAPM with downside gamma. The problems of the high correlation between the beta and downside beta models from a regressand point of view is resolved by constructing a double-sorted portfolio of each factor loading. The problem of the high correlation between the beta and gamma, and, similarly, between the downside beta and downside gamma, is resolved by orthogonalizing each risk measure in a two-factor setting. Standard two-pass regression is applied, and the results are reported and analyzed in terms of R², the significance of the factor loadings, and the risk–return relationship in each model. The risk proxies of the downside beta/gamma are based on Hogan and Warren, Harlow and Rao, and Estrada. The results indicate that the single factor models based on the beta/downside beta or even gamma/downside gamma are not a better choice among all the risk proxies. However, the beta and gamma factors are rejected at a 5% and 1% significance level for different risk proxies. The obvious choice based on the results is an asset pricing model with two risk measures. Full article

Since late 2019, during one of the largest pandemics in history, COVID-19, global economic recession has continued. Therefore, investors seek an alternative investment that generates profits during this financially risky situation. Cryptocurrency, such as Bitcoin, has become a new currency tool for speculators and investors, and it is expected to be used in future exchanges. Therefore, this paper uses a Value at Risk (VaR) model to measure the risk of investment in Bitcoin. In this paper, we showed the results of the predicted daily loss of investment by using the historical simulation VaR model, the delta-normal VaR model, and the Monte Carlo simulation VaR model with the confidence levels of 99%, 95%, and 90%. This paper displayed backtesting methods to investigate the accuracy of VaR models, which consisted of the Kupiec’s POF and the Kupiec’s TUFF statistical testing results. Finally, Christoffersen’s independence test and Christoffersen’s interval forecasts evaluation showed effectiveness in the predictions for the robustness of VaR models for each confidence level. Full article

Recent pension reforms in Europe have implemented a link between retirement age and life expectancy. The accurate forecast of life tables and life expectancy is hence paramount for governmental policy and financial institutions. We developed a multi-population mortality model which includes a cause-specific environment using Archimedean copulae to model dependence between various groups of causes of death. For this, Dutch data on cause-of-death mortality and cause-specific mortality data from 14 comparable European countries were used. We find that the inclusion of a common factor to a cause-specific mortality context increases the robustness of the forecast and we underline that cause-specific mortality forecasts foresee a more pessimistic mortality future than general mortality models. Overall, we find that this non-trivial extension is robust to the copula specification for commonly chosen dependence parameters. Full article

The rural e-payment market in China is becoming one of the important topics in the research field because of its contribution to the efficiency of fund flows in the economy. Further development of the rural e-payment market mainly depends on its partners’ acceptance. In March 2020, 776.08 million people were using mobile payments in China. After the COVID-19 pandemic in China, the Payment and Clearing Association of China launched an action to encourage citizens to use mobile payments. In this article evolutionary game theory is presented. The benefits of e-payments between financial institutions and users are studied. Based on the analysis of the partners’ selection of costs and profits as well as other factors, important conclusions were drawn. The growth of the rural economy is beneficial to the change of the partners’ behavior in the rural e-payment market. Dynamic evolution of the partners’ behavior makes the supply and demand for rural e-payment services consistent. In order to create more benefits, financial institutions will lead the move to merge the rural e-payment market with the China National Advanced Payment System. These research results are beneficial for its growth by developing strategies to encourage more partners to take part in the rural e-payment market in China. Full article

The main aim of the research was to determine the key factors determining the level of credit risk of individual clients (clients in the form of natural persons, excluding companies) on the example of Polish cooperative banks according to the following features: transaction characteristics, socio-demographic characteristics of the customer, the customer’s financial situation, the customer’s history of cooperation with the cooperative bank where they applied for a loan, and the customer’s history of cooperation with other financial institutions. For the research gathered data from 1000 credit applications submitted by individual customers when applying for a credit in five different cooperative banks were used for the analyses. To assess the credit risk of retail clients we use logit regression models, and additionally, score cards were calculated. The results of the research indicate that among the factors with high predictive power there were the features characterizing the client’s history of cooperation with the cooperative bank, where they applied for a loan. It may mean that when assessing credit risk related to financing individual customers, cooperative banks due to their local character, have an advantage over other financial institutions. Full article

The purpose is to study the new survival trends for states in a multipolar world, determine the successfulness of adaptation to the digitalization of different growth poles, and develop the applied recommendations to improve the practice of adaptation to the risks of digitalization of these growth poles. Design/methodology/approach. The authors use the methods of economic statistics: variation analysis, trend analysis, correlation analysis, and regression analysis. Findings. The commonness of strategies of adaptation to the risks of digitalization for different poles of the world economy is substantiated, and two universal mechanisms—talent management and development of science—are found. The originality of this research is due to the consideration of digitalization from a new view—from the positions of setting states at the brink of survival due to the aggressive digital competition and high complexity of ensuring global competition in a quickly changing digital landscape. The uniqueness of this research is due to taking into account the specific features in a multipolar world. The practical implementation of the offered recommendations opens future perspectives for more successful survival trends in a multipolar world and the improvement of their adaptation to risks digitalization by 69.91% in G7 countries (on average) and by 88.40% in BRICS countries (on average). Full article

With the record high leverage across all segments of the (global) economy, default prediction has never been more important. The excess cash illusion created in the context of COVID-19 may disappear just as quickly as the pandemic entered our world in 2020. In this paper, instead of using any scoring device to discriminate between healthy companies and potential defaulters, we model default probability using a doubly stochastic Poisson process. Our paper is unique in that it uses a large dataset of non-public companies with low-quality reporting standards and very patchy data. We believe this is the first attempt to apply the Duffie–Duan formulation to emerging markets at such a scale. Our results are comparable, if not more robust, than those obtained for public companies in developed countries. The out-of-sample accuracy ratios range from 85% to 76%, one and three years prior to default, respectively. What we lose in (data) quality, we regain in (data) quantity; the power of our tests benefits from the size of the sample: 15,122 non-financial companies from 2007 to 2017, unique in this research area. Our results are also robust to model specification (with different macro and company-specific covariates used) and statistically significant at the 1% level. Full article

We propose a hybrid classical-quantum approach for modeling transition probabilities in health and disability insurance. The modeling of logistic disability inception probabilities is formulated as a support vector regression problem. Using a quantum feature map, the data are mapped to quantum states belonging to a quantum feature space, where the associated kernel is determined by the inner product between the quantum states. This quantum kernel can be efficiently estimated on a quantum computer. We conduct experiments on the IBM Yorktown quantum computer, fitting the model to disability inception data from a Swedish insurance company. Full article

Nat Cat risks are not insurable by traditional insurance mainly because of producing highly correlated losses. The source of such correlation among buildings of a region subject to a natural hazard is discussed. A decomposition method is proposed to split Nat Cat risk into idiosyncratic (and hence insurable) risk and systematic risk (carrying the correlated part). It is explained that the systematic risk can be transferred to capital markets using a set of parametric CAT bonds. Premium calculation is presented for insuring the decomposed risk. Portfolio risk-return trade-off measures for investing on the parametric CAT bond are derived. Multi-regional and multi-hazard parametric CAT bonds are introduced to reduce the risk of the investment. The methodology is applied on a region with about 3000 residential buildings subject to flood hazards. Full article

The paper presents a two-period Walrasian financial market model composed of informed and uninformed rational investors, and noise traders. The rational investors maximize second period consumption utility from the payoffs of trading risk-free holdings to risky assets in the first period. The central bank reacts directly to asset price movements by selling or buying assets to stabilize the market price. It is found that the intervention makes the risky asset’s market price per share less sensitive to information shocks, which presses the market price towards its average price thus reducing price variance. The informed investors’ prediction coefficient remains unaffected, but that of the uninformed investors is magnified, which cancels out the negative effect on shock sensitivity thus keeping the expected value of the risky asset’s dividend constant. Finally, the introduction of the policy rule does not affect rational investors’ risk per share. A general conclusion is that the central bank’s policy can be regarded as an effective automatic stabilizer of financial markets. Full article

Over the last years, farmers have been increasingly exposed to income risk due to the volatility of the commodities prices. Among others, hedging in futures markets (i.e., financial markets) represents an available strategy for producers to cope with income risks at farm level. To better understand the advantages of such promising tools, this paper aims at analyzing the hedging effectiveness for soybean, corn and milling wheat producers in Italy. Following the literature, three different methodologies (i.e., naïve, OLS, GARCH) are applied for the estimation of the hedge portfolio, then compared to an unhedged portfolio for assessing the income risk reduction. Findings confirm the hedging effectiveness of futures contracts for all the considered commodities, showing also that this effect increases with longer hedge horizons, and also showing better performances for the European exchange market (i.e., Euronext), compared to the North American counterpart. Full article

The purpose of the article: to find new (most effective) directions for the corporate COVID-19 risks management and developing management implications for leading this fight amid the pandemic and crisis for sustainable development. The methods of correlation and regression analysis are used. It is proved that the most perspective method of the corporate fight against the COVID-19 risks is a flexible transformation of business according to the new conditions based on the Industry 4.0 technologies. This paper further develops and supplements the concept of corporate social responsibility, including a new direction—corporate fight against the COVID-19 risks in it. The authors develop management implications on improving the corporate fight against the COVID-19 risks as a new direction of corporate social responsibility amid the pandemic. The advantages of using the Industry 4.0 technologies for the corporate fight against the viral threat include reduction of the share of the population with household expenditures for healthcare above 25% of total expenditures or incomes, reduction of the number of new cases per 1 million people, and an increase of the self-isolation index, the share of responsible employers amid COVID-19 risks. Full article

The relevance of this study lies in the fact that financial risk is a serious obstacle to the development of social entrepreneurship, preventing the implementation of potential support for sustainable development goals in business. The purpose of this article is to clarify specific aspects of financing factors and financial risk related to social entrepreneurship in developing countries (in comparison with the standard financial risk related to commercial entrepreneurship) in order to analyze the influence of the financing factors of social entrepreneurship on sustainable development, as well as to determine the potential for the development of social entrepreneurship through financial risk management. To achieve this goal, this article uses the methodology of econometrics—dataset modelling of financial risk management in social entrepreneurship to achieve sustainable development in emerging economies. On the basis of the results of this study, firstly, it is substantiated that the financial risks entailed by social entrepreneurship differ from the standard financial risk present in commercial entrepreneurship. Specific factors of the financing of sustainable development in emerging economies are determined and, on the basis of this, financial risks specific to social entrepreneurship in emerging economies are identified as follows: (1) reduced stimulus to use financial resources in long-term investments, which disrupts the stability and decreases inclusion; (2) joint public–private investments and decreased investment in R&D; and (3) expanded investment in the skills required for jobs and “markets of tomorrow”. Secondly, a contradictory influence of financing factors on sustainable development is demonstrated. Thirdly, a large potential for the development of social entrepreneurship by means of financial risk management (maximum reduction) was identified. With the minimization of financial risk, social entrepreneurship would demonstrate substantial progress, with an increase of 99.61% (more than 50%) from 45.18 points to 90.18 points. A novel contribution of this paper to the extant literature consists of the specification of the essence and specifics of social entrepreneurship in emerging economies through the identification of financial risks and the provision of recommendations for their management. Full article

Accurate revenue prediction is a key factor for the reliable determination of the investment part of entire regional and local budgets, particularly during economic downturns and fiscal uncertainty. An unexpected decline in revenue requires the reduction in capital expenditures and forces the regional government to find additional sources to close the budget gaps. Current studies indicate that budget forecasts often underpredict revenue and use the available information inefficiently. In this article, the authors examine chosen methods of forecasting regional government revenue. In addition to classical forecasting models based on time series and causal models, an original structural forecasting procedure was proposed, which is effective especially in case of data delay. The reliability of applied methods was assessed using data from the Polish area of Zachodniopomorskie over the period 2000–2018. The found evidence supported results that were obtained by many other researchers, which indicated that less comprehensive methods of forecasting can provide reasonably accurate estimates. Full article