Risks, Volume 10, Issue 1 (January 2022) – 22 articles

This paper uses three methodologies for measuring the existence of systemic risk in the Colombian banking system. The determination of its existence is based on implementing three systemic risk measures widely referenced in academic works after the subprime crisis, known as CoVaR, MES and SRISK. Together, the three methodologies were implemented for the case of Colombian Banks during the 2008–2017 period. The findings allow us to establish that the Colombian banking sector did not present a systemic risk scenario, despite having suffered economic losses due to external shocks, mainly due to the subprime crisis. The results and findings show the efficiency of the systemic risk measures implemented in this study as an alternative to measure systemic risk in banking systems. Full article

The purpose of this work is to investigate the influence of macroeconomics determinants on non-performing loans (NPLs) in the Italian banking system over the period 2008Q3–2020Q4. We mainly contribute to the literature by being the first empirical article to study this relationship in the Italian context in the recent period, thus providing fresh evidence on the macroeconomic impact on NPLs, i.e., on the credit risk of Italian banks. By employing the Autoregressive Distributed Lag (ARDL) cointegration model, we are able to investigate the short and long-run effects of macroeconomic factors on NPLs. The empirical findings show that gross domestic product and public debt have a negative impact on NPLs. On the other hand, we find that the unemployment rate and domestic credit positively influence impaired loans. Finally, we find evidence of the “gamble for resurrection” approach, i.e., Italian banks tend to support “zombie firms”. Full article

The question of whether environmental, social, and governance investments outperform or underperform other conventional financial investments has been debated in the literature. In this study, we compare the volatility of rates of return of selected ESG indices and conventional ones and investigate dependence between them. Analysis of tail dependence is important to evaluate the diversification benefits between conventional investments and ESG investments, which is necessary in constructing optimal portfolios. It allows investors to diversify the risk of the portfolio and positively impact the environment by investing in environmentally friendly companies. Examples of institutions that are paying attention to ESG issues are banks, which are increasingly including products that support sustainability goals in their offers. This analysis could be also important for policymakers. The European Banking Authority (EBA) has admitted that ESG factors can contribute to risk. Therefore, it is important to model and quantify it. The conditional volatility models from the GARCH family and tail-dependence coefficients from the copula-based approach are applied. The analysis period covered 2007 until 2019. The period of the COVID-19 pandemic has not been analyzed due to the relatively short time series regarding data requirements from models’ perspective. Results of the research confirm the higher dependence of extreme values in the crisis period (e.g., tail-dependence values in 2009–2014 range from 0.4820/0.4933 to 0.7039/0.6083, and from 0.5002/0.5369 to 0.7296/0.6623), and low dependence of extreme values in stabilization periods (e.g., tail-dependence values in 2017–2019 range from 0.1650 until 0.6283/0.4832, and from 0.1357 until 0.6586/0.5002). Diversification benefits vary in time, and there is a need to separately analyze crisis and stabilization periods. Full article

Quantile regression provides a way to estimate a driver’s risk of a traffic accident by means of predicting the percentile of observed distance driven above the legal speed limits over a one year time interval, conditional on some given characteristics such as total distance driven, age, gender, percent of urban zone driving and night time driving. This study proposes an approximation of quantile regression coefficients by interpolating only a few quantile levels, which can be chosen carefully from the unconditional empirical distribution function of the response. Choosing the levels before interpolation improves accuracy. This approximation method is convenient for real-time implementation of risky driving identification and provides a fast approximate calculation of a risk score. We illustrate our results with data on 9614 drivers observed over one year. Full article

This study on explaining aggregated recovery rates (ARR) is based on the largest existing loss and recovery database for commercial loans provided by Global Credit Data, which includes defaults from 5 continents and over 120 countries. The dependence of monthly ARR from bank loans on various macroeconomic factors is examined and sources of their variability are stated. For the first time, an influence of stochastically estimated monthly growth of GDP USA and Europe is quantified. To extract monthly signals of GDP USA and Europe, dynamic factor models for panel data of different frequency information are employed. Then, the behavior of the ARR is investigated using several regression models with unshifted and shifted explanatory variables in time to improve their forecasting power by taking into account the economic situation after the default. An application of a Markov switching model shows that the distribution of the ARR differs between crisis and prosperity times. The best fit among the compared models is reached by the Markov switching model. Moreover, a significant influence of the estimated monthly growth of GDP in Europe is observed for both crises and prosperity times. Full article

This paper focuses on the problem of the high financial risks of agricultural entrepreneurship, which hinder the sustainable development of agriculture and do not provide food security. This problem is especially topical in the conditions of the COVID-19 crisis when financial risks are urgent. The research basis is the theory of financial risks of entrepreneurship. This paper’s RQ is as follows: how should financial risks for the sustainable development of agriculture be managed for the provision of food security? The purpose of this paper is to find ways of managing the financial risks of agricultural entrepreneurship based on its corporate social responsibility for sustainable development and the provision of food security. The contribution to the literature is that the authors offer a solution to the problem of the financial risks of agricultural entrepreneurship. The originality of this paper is that the solution is corporate social responsibility. The universal character of the paper is due to the description of the international experience of corporate social responsibility and proving the contribution of this responsibility for the sustainable development of agriculture and food security as well as its demonstration—based on the case experience of modern Russia of specific, effective, and perspective practices of corporate social responsibility that make a significant contribution to the sustainable development of agriculture and food security. The results are very important for decision making in managing the financial risks of agricultural entrepreneurship. Full article

After the financial crisis, the European Banking Authority (EBA) has established tighter standards around the definition of default (Capital Requirements Regulation CRR Article 178, EBA/GL/2017/16) to increase the degree of comparability and consistency in credit risk measurement and capital frameworks across banks and financial institutions. Requirements of the new definition of default (DoD) concern how banks recognize credit defaults for prudential purposes and include quantitative impact analysis and new rules of materiality. In this approach, the number and timing of defaults affect the validity of currently used risk models and processes. The recommendation presented in this paper is to address current gaps by considering a Bayesian approach for PD recalibration based on insights derived from both simulated and empirical data (e.g., a priori and a posteriori distributions). A Bayesian approach was used in two steps: to calculate the Long Run Average (LRA) on both simulated and empirical data and for the final model calibration to the posterior LRA. The Bayesian approach result for the PD LRA was slightly lower than the one calculated based on classical logistic regression. It also decreased for the historically observed LRA that included the most recent empirical data. The Bayesian methodology was used to make the LRA more objective, but it also helps to better align the LRA not only with the empirical data but also with the most recent ones. Full article

This paper investigates the optimal asset allocation of a financial institution whose customers are free to withdraw their capital-guaranteed financial contracts at any time. In accounting for the asset-liability mismatch risk of the institution, we present a general utility optimization problem in a discrete-time setting and provide a dynamic programming principle for the optimal investment strategies. Furthermore, we consider an explicit context, including liquidity risk, interest rate, and credit intensity fluctuations, and show by numerical results that the optimal strategy improves both the solvency and asset returns of the institution compared to a standard institutional investor’s asset allocation. Full article

The main purpose of this study was to explore the relationship between market and accounting measures of risk and the profitability of companies listed on the Frankfurt Stock Exchange. An important aspect of the study was to employ accounting beta coefficients as a systematic risk measure. The research considered classical and downside risk measures. The profitability of a company was expressed as ROA and ROE. When determining the downside risk, two approaches were employed: the approach by Bawa and Lindenberg and the approach by Harlow and Rao. In all the analyzed companies, there is a positive and statistically significant correlation between the average value of profitability ratios and the market rate of return on investment in their stocks. Additionally, correlation coefficients are higher for the companies included in the DAX index compared with those from the MDAX or SDAX indices. A positive and in each case a statistically significant correlation was observed for all DAX-indexed companies between all types of market betas and corresponding accounting betas. Likewise, for the MDAX-indexed companies, these correlations were positive but statistical significance emerged only for accounting betas calculated on ROA. As regards the DAX index, not every correlation was positive and significant. Full article

The risk-based capital (RBC) ratio, an insurance company’s financial soundness system, evaluates the capital adequacy needed to withstand unexpected losses. Therefore, continuous institutional improvement has been made to monitor the financial solvency of companies and protect consumers’ rights, and improvement of solvency systems has been researched. The primary purpose of this study is to find a set of important predictors to estimate the RBC ratio of life insurance companies in a large number of variables (1891), which includes crucial finance and management indices collected from all Korean insurers quarterly under regulation for transparent management information. This study employs a combination of Machine learning techniques: Random Forest algorithms and the Bayesian Regulatory Neural Network (BRNN). The combination of Random Forest algorithms and BRNN predicts the next period’s RBC ratio better than the conventional statistical method, which uses ordinary least-squares regression (OLS). As a result of the findings from Machine learning techniques, a set of important predictors is found within three categories: liabilities and expenses, other financial predictors, and predictors from business performance. The dataset of 23 companies with 1891 variables was used in this study from March 2008 to December 2018 with quarterly updates for each year. Full article

The motivation of this research consists in the following: the traditional commercial approach to financial risk management amid economic crises implies the reduction of corporate social responsibility, based on the assumption that this responsibility raises the financial risk of business. Due to this, the contribution of business to the achievement of the SDGs is not stable and is often negative, since practices of business management contradict the SDGs in crisis periods and hinder their achievement in society and the economy. However, the refusal from corporate social responsibility during a crisis does not guarantee the following increase in the level of business development in the period of stability. A study of the case experience of integrating the SDGs into corporate strategies of the largest Russian companies during the COVID-19 crisis improved the understanding of the contribution of corporate social responsibility to financial risk management of the business. Dynamic modelling showed that, in a crisis period, corporate social responsibility leads to a reduction of the financial risks of business—it is commercially profitable, similarly to the phase of stability, and critically important. Based on this, an alternative (new) approach to financial risk management is developed, which allows raising the effectiveness of this management amid economic crises (including the COVID-19 crisis) through the integration of the SDGs into corporate strategies and the manifestation of high social responsibility during crises. Full article

This paper aims to research the topics related to risk included in non-financial disclosure (NFD) of companies listed on the Warsaw Stock Exchange (WSE) and explore factors that influence the risk topics ratio in NFD. We applied a content analysis using topic modeling to discover latent risk topics in NFD. Next, with Ward’s clustering, we identified four groups of companies with a homogenous risk topic mixture. For causal analysis, to explain the differences in risk topics ratio, we used qualitative comparative analysis (QCA), which allowed us to obtain three paths (variable configurations) leading to the high ratio of risk topics in NFD. Our results suggest that companies disclosing risk information extensively in their NFDs concentrate almost solely on social risk matters. In contrast, companies talking briefly about environmental and social (E&S) risk prepare their NFDs with a more balanced distribution of E&S topics and their financial implication. In general, the companies’ exposure to E&S risk and the use of NFD standards and guidelines as well as the type of NFD impact the space dedicated to risk information. This paper contributes to academics and regulators, filling the gap about risk disclosure in the NFD, identifying the nature of corporate risk disclosures, and upgrading research about determinants of risk information in non-financial disclosure. Full article

The application of industry 4.0 in banking presents many challenges, with several operational risks related to downtime and timeout services due to system failures. One of the operational risk management steps is to estimate the value of the maximum potential losses. The purpose of this study is to estimate the maximum potential losses for digital banking transaction risks. The method used for estimating risks is the EVaR method. There are several steps in this study. The first step is to resample the data using MEBoot. This process is a simulation of the operational risk loss data of digital banking. Next, the threshold value is determined to obtain the extreme data value. Then, a Kolmogorov–Smirnov test is conducted to fit the data with the GPD. Afterward, the GPD parameter is estimated. Then, EVaR is calculated using a portfolio approach to obtain a combination of risk values as maximum potential losses. The analysis results show that the maximum potential loss is IDR144,357,528,750.94. The research results imply that the banks need to pay attention to the maximum potential losses of digital financial transactions as a reference for risk management. Therefore, banks can anticipate the adequacy of reserve funds for these potential risks. Full article

The purpose of this paper is an analysis of the presence of self-selection mechanisms on the market that could bring the market closer to the separating equilibrium state, in line with the Rothschild–Stiglitz equilibrium model and its subsequent modifications. An example is the Polish market of compulsory third-party liability insurance of vehicle owners. This paper describes this market in terms of both its structure and its financial results. The main focus is on describing the assumptions of the Rothschild–Stiglitz model for markets operating under the conditions of information asymmetry and based on the self-selection mechanism, allowing for an unequivocal determination of the insured’s profile without the need to actually observe the insured’s behaviour. Finally, we show that thanks to the self-selection induced by the possibility of driving behaviour monitoring, the industry can minimise the negative effect information asymmetry has on the motor insurance market. This can be achieved, for example, by observing the choices made by the insured after being offered a new voluntary contract with a premium based on telematics data. Our analysis was carried out with the use of three selected characteristics that can determine the insured’s risk profile, i.e., distance covered, self-assessment, and insurance premium paid; the significance of the latter—although it may be intuitive—is questionable at commonly accepted significance levels. Therefore, the main result is that although there is some evidence on the disputed matter, there can be no definitive conclusion—especially in terms of risk as measured by insurance premium. Full article

Thailand recently reached “aged” society status, signifying that over twenty percent of the population is over sixty. Considering that Thailand has a low literacy rate, a fractured pension system, and no regulations that could provide sufficient income to cover basic needs after retirement, there will be economic repercussions if the situation is not handled soon. The government and financial institutions have been encouraging Thai citizens to prepare retirement plans but lack understanding of the root causes of being unprepared for retirement. The objectives of this qualitative research were to explore the behavior, knowledge, and preparedness towards retirement in governmental and private wageworkers. Moreover, the study aims to identify the pain points of being unprepared for retirement and deliver the optimal solutions and sustainable retirement plans suitable for each segment. This article employed a sample of 46 wageworkers in Khon Kaen, Thailand with ages ranging from 20 to 59 years old. Qualitative semi-structured in-depth interviews and qualitative content analysis were conducted with the respondents asking about their income, expenses, pains, and problems towards saving for retirement, their desired outcome after they retire, and how they would achieve it. The framework used for the in-depth qualitative interview was by utilizing the customer, problem, and solution zoom tool. The research contributions were to facilitate Thai citizens being ready for retirement stages and overcome post-retirement risks sustainably. The results revealed that the sample could be divided into four segments by their characteristics. Two low-income segments share the same traits and behaviors that can prove that financial literacy plays an essential role in retirement readiness. Lower-income wage workers do not have their money put in place to prepare for retirement. Additionally, this article discussed the study’s implications for wageworkers, employers, and the Thai government. This article recommended that Thai citizens should accumulate wealth in various ways, including investment in financial assets and earning additional income from a second job. Employers should provide suitable retirement contribution schemes. The government should launch a policy enabling above-60-year-old seniors to continue working. Full article

(1) Background: The coronavirus pandemic has highlighted the problem of combining work and private life. The pandemic conditions have turned out to be particularly difficult for parents who, due to changes in the organization of the education system, have been forced to reconcile their professional duties with the raising of childcare. Thanks to the recommendations for cooperation between schools and parents proposed in this study, it will be possible to reduce the risk and uncertainty of achieving common goals of the education system. (2) Methods: In the preparation of nationwide research, a questionnaire was provided to a sample of 10,331 respondents, including 7800 professional parents, in a trial form before a transition to the study of children. (3) Results: The analysis of the data showed that educational institutions should shape their activities based on cooperation with the family environment of children. (4) Conclusions: Educational institutions can help working parents in times of increased uncertainty. Parents reported that in caring for children, it would be helpful to operate educational institutions in stationary mode. Moreover, they expect increases in extracurricular and extra-curricular activities. Full article

The effective operation of national innovation systems can be a source of many opportunities, but it cannot be forgotten that innovation in itself may mean limiting but also generating various types of risks for the functioning of the local market. The main aim of the article is to present the concept and classification of national innovation systems in the world and to try to answer whether the type of NIS determines the level of innovation of the economies of the European Union countries. The following research thesis was formulated in the study: the type of National Innovation System determines a certain level of innovation in the economy of an European Union country, i.e., in countries belonging to a developed NIS, the level of innovation of the economy is higher than in countries belonging to developing systems. The results of the analysis confirm the research thesis. In the empirical part, the level of innovation in the European Union countries was assessed using the synthetic measure of development (SMD) by Z. Hellwig. Based on the obtained values of the synthetic measure of development (innovation), a ranking of the innovation of the economies of the EU countries was compiled and groups of countries with a similar degree of innovation in the economy were distinguished. The developed ranking of the European Union countries was compared with the NSI classification presented in the theoretical part of the article. The study covered 2010 and 2019. Full article

The activity of each construction company in conditions of high competitiveness is exposed to a number of risks that make it difficult to maintain high financial liquidity. In order to provide the continuity of ongoing economic processes and to be able to develop, entities are forced to build optimal financial management strategies for them. Enterprises can choose between a conservative, moderate and aggressive strategy, which is largely determined by the way they manage their current assets and short-term liabilities. In the case of construction companies, it is also not without significance that they are particularly sensitive to fluctuations in the economic situation and changes in the macroeconomic environment, which imply the availability of funds. The purpose of this paper is to analyze the financial liquidity management strategy of construction sector Polish enterprises from the Podkarpackie Province in 2017–2019 and the impact of this strategy on the profitability of the surveyed entities. In order to achieve the goal, the issues related to the classification of financial liquidity and individual liquidity management strategies are discussed. The issues and the goal set determined the choice of research methods. Literature studies, the Mann–Whitney U test, cluster analysis and Ward’s method were used. The research was carried out on a group of the 10 largest construction companies from the Podkarpackie Province. The selection of entities for the research was deliberately based on enterprises that submit their financial statements to the National Court Register. The conducted research showed that small and large enterprises applied different liquidity management policies even though they operate in the same industry and region. The small entities preferred a conservative strategy, while large entities preferred a moderate strategy. The existence of an inverse relationship between the phenomenon of financial liquidity and profitability of economic entities was also confirmed. Full article

This paper presents a methodology for making decisions in the stock market using the AHP-TOPSIS multi-criteria technique. The problem is related to the stock market’s investment process considering the criteria of liquidity, risk, and profitability. The proposed methodology includes integrating economic and financial theories of investment in equity portfolios with the AHP-TOPSIS multi-criteria technique, which allows for evaluating a finite number of alternatives hierarchically under qualitative and quantitative criteria. The methodology has been tested in a real case of selecting a portfolio of high and medium marketability stocks for the Colombian market from April 2012 to April 2017. The computational results show the importance and efficiency of successfully integrating traditional equity portfolio investment criteria and multi-criteria methodologies to find an appropriate balance between profitability and risk in the investment decision-making process in shares in the Colombian stock market. The proposed methodology could be applied to other emerging markets, similar to Colombia. Full article

Two-part models are important to and used throughout insurance and actuarial science. Since insurance is required for registering a car, obtaining a mortgage, and participating in certain businesses, it is especially important that the models that price insurance policies are fair and non-discriminatory. Black box models can make it very difficult to know which covariates are influencing the results, resulting in model risk and bias. SHAP (SHapley Additive exPlanations) values enable interpretation of various black box models, but little progress has been made in two-part models. In this paper, we propose mSHAP (or multiplicative SHAP), a method for computing SHAP values of two-part models using the SHAP values of the individual models. This method will allow for the predictions of two-part models to be explained at an individual observation level. After developing mSHAP, we perform an in-depth simulation study. Although the kernelSHAP algorithm is also capable of computing approximate SHAP values for a two-part model, a comparison with our method demonstrates that mSHAP is exponentially faster. Ultimately, we apply mSHAP to a two-part ratemaking model for personal auto property damage insurance coverage. Additionally, an R package (mshap) is available to easily implement the method in a wide variety of applications. Full article

This article proposes an interest rate model ruled by mean reverting Lévy processes with a sub-exponential memory of their sample path. This feature is achieved by considering an Ornstein–Uhlenbeck process in which the exponential decaying kernel is replaced by a Mittag–Leffler function. Based on a representation in term of an infinite dimensional Markov processes, we present the main characteristics of bonds and short-term rates in this setting. Their dynamics under risk neutral and forward measures are studied. Finally, bond options are valued with a discretization scheme and a discrete Fourier’s transform. Full article

The breakdown of stock indices is an obvious part of the financial market cycle. A common question about a bear market is the time and the depth of the downtrend, as well as the speed of the following recovery. As the COVID-19 pandemic spread globally, it induced huge price drops in a very short period, and an uptrend with new historical highs afterwards. The results of this research show that the pandemic breakdown was the fastest bear market in history; however, it does not confirm that future downtrends will be at the same or even greater speed. The consequences for individual investors have forced them to prepare for possible similar market behavior in the future, and to adjust their trading techniques and strategies to these conditions. Full article