Risks
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Risks ◽  
2022 ◽  
Vol 10 (1) ◽  
pp. 19
Author(s):  
Albert Pitarque ◽  
Montserrat Guillen

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.


Risks ◽  
2022 ◽  
Vol 10 (1) ◽  
pp. 20
Author(s):  
Joanna Górka ◽  
Katarzyna Kuziak

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.


Risks ◽  
2022 ◽  
Vol 10 (1) ◽  
pp. 21
Author(s):  
Matteo Foglia

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”.


Risks ◽  
2022 ◽  
Vol 10 (1) ◽  
pp. 18
Author(s):  
Stephan Höcht ◽  
Aleksey Min ◽  
Jakub Wieczorek ◽  
Rudi Zagst

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.


Risks ◽  
2022 ◽  
Vol 10 (1) ◽  
pp. 17
Author(s):  
Andrey A. Polukhin ◽  
Veronika I. Panarina

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.


Risks ◽  
2022 ◽  
Vol 10 (1) ◽  
pp. 16
Author(s):  
Aneta Ptak-Chmielewska ◽  
Paweł Kopciuszewski

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.


Risks ◽  
2022 ◽  
Vol 10 (1) ◽  
pp. 15
Author(s):  
Areski Cousin ◽  
Ying Jiao ◽  
Christian Yann Robert ◽  
Olivier David Zerbib

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.


Risks ◽  
2022 ◽  
Vol 10 (1) ◽  
pp. 14
Author(s):  
Anna Rutkowska-Ziarko

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.


Risks ◽  
2022 ◽  
Vol 10 (1) ◽  
pp. 12
Author(s):  
Alexey S. Kharlanov ◽  
Yuliya V. Bazhdanova ◽  
Teimuraz A. Kemkhashvili ◽  
Natalia G. Sapozhnikova

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.


Risks ◽  
2022 ◽  
Vol 10 (1) ◽  
pp. 13
Author(s):  
Jaewon Park ◽  
Minsoo Shin

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.


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