Nowadays, Russian banks are developing and updating models for assessing the probability of default (PD) for various risk segments of corporate borrowers. This is to enable the use of rating models when assessing regulatory capital, in compliance with the requirements of the Regulations of the Bank of Russia. The application of rating models allows credit institutions to more accurately distribute regulatory and economic capital among borrowers, calculate reserves according to RAS and IFRS, set interest rates for transactions with borrowers as part of the pricing procedure, and conduct stress testing of both borrower portfolios and individual borrowers. The results of stress testing can then be incorporated into strategic planning.One of the Regulator’s requirements is to consider assessments of sustainable development and responsible financing ESG (environmental, social, and corporate governance) indicators as part of the credit risk management procedure, particularly in the development of models for assessing the probability of default. This research focuses on the development of an integral module (ESG rating) that includes ESG indicators. The aim is to improve the accuracy of existing models for assessing the probability of default (PD models) for corporate borrowers through the use of ESG factors. The findings of this research can be utilized by Russian banks to enhance the discriminatory and predictive capabilities of their own PD models, and by the regulator to understand the set of ESG indicators that impact the creditworthiness of corporate borrowers.

This study researches the impact of the Paris Agreement and the United Nations Global Compact (UNGC) on financial sustainability across various industries and states worldwide, a topic of significant global discourse. As sustainable development goals increasingly shape the modern economy, actors at different levels of the global community are ensuring Environmental, Social, and Governance (ESG) standards. However, the economic crises of the past decade have intensified competition among states. By using an event study model, specifically the Cumulative Average Abnormal Returns (CAAR) approach, this research analyzes the diverse impacts of the Paris Agreement and the UNGC on financial sustainability. It examines how these agreements affect developed versus developing states, and ecologically sensitive versus non-sensitive industries, and assesses whether the Paris Agreement has a stronger impact than the UNGC. Furthermore, this paper engages in a discourse on the influence of ESG on financial sustainability, highlighting associated challenges and potential solutions.

Sustainable topics have become increasingly important in recent years, as the world faces growing environmental and social challenges. Environmental, social, and governance (ESG) ratings are tools used to assess the sustainability practices of companies. This study focuses on the impact of environmental, social and governance components on the financial performance of IT companies. The panel data were collected for 43 IT companies operating in the time period from 2004 to 2020. Data include ESG ratings, their components and financial performance indicators of IT companies. The method of OLS regression, a model with fixed individual effects or a model with random individual effects, was used. It was found that the increase in the environmental and social scores has a significant impact on the financial performance of IT companies. This paper is an extended version of our work published in the ITQM 2022.

This paper investigates and defines the concept of "credit behavior". A number of factors influencing credit behaviour of the population were identified. Among them are economic factors, e.g. income and expenses, debt burden, unemployment, interest rates, etc. Another important group of factors impacting credit behavior is social aspects, i.e. level of education, social status, financial and digital literacy, and others. The next important group is demographic factors which include gender and age characteristics, family composition, etc. The research found rigid and positive dependence between the volume of per capita lending with per capita income, which can be explained by the fact that the amount of income directly determines “solvency” of a client. A positive relationship is also observed when assessing the link between the volume of per capita loans and availability of hospital beds.

The aim of the paper is to investigate the impact of ESG factors on the performance of information technology (IT) companies. The paper analyzes the position of IT companies in the ESG rating relative to other industries, highlights the key strengths and weaknesses in their ESG components. It is shown that IT companies are not currently the leaders in terms of ESG rating, which leads to the conclusion that IT companies have the opportunity to develop their ESG practice, if its development will improve the position of the company and will have a positive effect on its performance. On the basis of the studied literature, the author formulated that market value of the company is the most suitable as an indicator for assessing the influence of ESG factors on it. In addition, the paper formulates hypotheses that can be used to test the influence of ESG on the market value of IT companies, developed a model to assess such an influence and provide recommendations for data sample. The author intends to continue research and test the formulated hypotheses with the developed model.

Over the last two decades global economy and financial markets have seen many crises with an increasing frequency. Many of them were either unexpected or their effects were unpredictable. Therefore, a lot of conventional and popular investment opportunities have shortened or became less attractive for either private or institutional investors leaving a room for new instruments and products to gain popularity. Unlike new volatile and uncertain markets like cryptocurrency, there are potentially more stable processes such as M&A activity, which can be predicted to earn abnormal returns. In this paper, we study various financial and non-financial indicators of acquired and non-acquired companies to provide a set of variables that can describe a company from different perspectives. Next, M&A prediction model is designed. Then, techniques are discovered to increase its explanatory and predictive power, and flexibility making it applicable for different economic environments without being harder to implement it by a potential user. In the end, its efficiency is measured on a real data to compare it with a result of methods found in earlier papers.

In the article the notion of an “ecosystem” and its taxonomy are examined as well as an eventual development of the so-called platform ecosystems. Based on heuristics methodology, a formalized model of an ecosystem is built and put into simulation, its results then compared to the status of Russia’s platform ecosystems.

Models for assessing the probability of default play an important role in the risk management systems of commercial banks, as they allow assessing the creditworthiness of various counterparties and transactions. Many Russian banks are trying to switch to an advanced approach based on internal ratings (IRB-approach) for evaluating regulatory capital. The main goals that banks pursue when switching to an advanced approach are: stability of credit risk assessment for the ability to carry out strategic planning; the validity of the credit risk assessment to simplify interaction with the regulator and external and internal audit; potential reduction of regulatory capital due to the high quality of the forecast capabilities of the developed models, which leads to a reduction in the regulatory capital of banks. To use internal rating models in the calculation of regulatory capital banks serve the petitions on them to the regulator, on basis of which external validation of the models is carried out and a decision about the possibility of using models for regulatory purposes is made. The main event of credit risk, the default event is determined by banks in the framework of credit policy, is consistent with the Central Bank and is predicted using models for assessing the probability of default. The PD models are the most popular in banking practice due to the fact that according to regulatory requirements, they are developed on the horizon of 1 year, and the minimum amount of statistical data for such models must be at least 5 years. The risk segments are identified using both economic and statistical evaluation criteria based on the banks available empirical data for each group of borrowers to build separate models (Allen, Financial risk management: a practioner’s guide to managing market and credit risk. Wiley, Hoboken, NJ, 288 p, 2003; Lobanov and Chugunov, Encyclopedia of financial risk management, 4th edn, Alpina Business books, 932 p, 2009; Rogov, Risk management, Finance and statistics, Moscow, 120 p, 2001). This paper will describe the specifics of developing models for low-default risk segments (bank assets), both low-default and high-default risk segments (corporate borrowers), and high-default risk segments, including taking into account the availability of a small amount of static data (residential real estate lending and project finance segments).

The COVID-19 pandemic had an enormous social and economic impact on societies in 2020. The epidemiological situation is evolving on a daily basis, and the methodology of how to evaluate the impact of the pandemic and the severity of its consequences is lacking. The only available high-frequency data now are the number of people who have contracted the illness, and the restrictive measures that authorities have implemented to contain the outbreak. The most important question now is whether authorities can prevent subsequent waves. The contribution of the paper is a dynamic model of COVID-19 outbreaks, on the basis of which we investigated the possible impact of the socio-economic behavior and restrictions on its waves. To build the model, a large database for different countries with a wide range of economic and social institutions was collected. We give a detailed description of the model and a comparison of the results with trajectories of the outbreaks in the countries under consideration. The proposed model describes the empirical results and can be used for timely and contemporary predictions of the stages of pandemics. Despite this, the model needs future development and verification because the pandemic is not over, and the accumulation of empirical information continues. Yet the model might also be useful as a basis for researching the impacts of other socio-economic and medical actions for containing pandemics.

The work is devoted to credit risk modeling of international banks by constructing ordered logistic models of credit ratings assigned by the agencies: Moody’s, Standard & Poor’s, and Fitch ratings. It was demonstrated that mapping the credit ratings into a base scale helps to decrease the possible subjectivity of CRAs and increases models’ forecasting power. Using a random sample of 478 banks from more than 40 countries for the period of 2007–2019, a credit rating model was obtained that could be effectively used to assess credit risk using public information, which was demonstrated with a help of out-of-sample forecasts of the obtained regression. The quality of the model prediction was significantly improved by including interaction terms and applying the Principal Component Analysis. We observed that the transparent politics of the government can lead to increased credit ratings of international banks. This helps them in credit risk management and in the client’s base extension. Additionally, using marginal effects calculation, the empirical evidence of the importance of maintaining a high level of liquidity by banks in times of crisis was found. At the same time, the highest marginal effects of the whole sample time horizon were reached in factors of asset quality and bank’s size among all financial indicators included in the model.

We construct a sentiment-based index of global financial stress (s-GFS index) for the period January 2004-December 2020. It builds on a novel methodological approach, which synthesizes the intensity of Google search for specific terms and word collocations related to financial instability and their prior selection based on the titles and abstracts of more than 2,000 working papers posted on the Basel Bank for International Settlements Central Bank Research Hub. The s-GFS index obtained by means of sparse principal component analysis (PCA) accurately captures major episodes of global financial instability during the observation period, playing a pivotal role for the US financial stress as well as industrial production in the USA, the Eurozone and China. It also Granger causes several well-known measures of global financial instability based on sentiment and "hard" data, e.g. the VIX index, as well as the overall dynamics of the global financial cycle, thereby emphasizing the usefulness of sentiment-based measures in monitoring worldwide financial stress.

Regulation of risks in banking is driven by evolution of financial intermediation and markets, and vice versa. The study analyzes a changing nature of financial institutions’ regulatory and supervisory trends in emerging markets over last 20 years, providing outlook for the future. Although the principles of the Basel Accord have long been the cornerstone of banking regulation in the world, precise requirements and scope were reformed and implemented in response to crises and global trends. At the turn of the century, the regulatory themes in EMs were focused on ensuring financial stability which was closely associated with regulatory and supervisory independence. However, the global financial crisis of 2008–2009 has changed the paradigm from partial improvements under financial liberalization regime to a world-wide regulation tightening on the basis of close coordination between regulators and supervisors in the world. The role of the G-20’s Financial Stability Board was to ensure that initiatives are implemented globally, which further enhanced convergence of financial risks regulation in EMs and DMs. In recent years, that uniformity started to decline as the number of local peculiarities and initiatives impacting banking business increases: some countries eased or lifted certain globally accepted restrictions, yet imposing local regulations (including financial sanctions). Functioning of financial institutions in emerging markets becomes more and more complicated. Modern technological innovations enter spheres of compliance and supervision via RegTechs and SupTechs as a solution to this growing number of such inconsistences.

The study explores the influence of internal factors on the level of exports of products of the agro-industrial complexof the Russian Federation (AIC RF). The subject of the research is the competitiveness of export-oriented companies in the agro-industrial complex of the Russian Federation. The relevance of the study is due to the growth of exports of agricultural products, which is gradually becoming one of the most important sources of foreign exchange earnings in the country. The aim of the paper is to form a rating model for Russian companies focused on the export of agricultural products, on the basis of which to propose the most effective measures to support agricultural enterprises. The authors apply the following methods: systematization and classification of information, statistical, coefficient, and regression analysis. Such tools as linear regression models, logistic regression (logit, probit), ordered probit model are considered The authors use the Ginny coefficient (area under the curve Roc) for binomial models and an adjusted R2 for thelinear model as a quality criterion for the model. As a result, the study identified the key internal and external factors affecting the competitiveness of agricultural exporting companies. Internal factors include stocks, net assets, short-term borrowings, equity capital, fixed assets turnover, long-term liabilities, accounts payable. Among the external factors for both ordinal and binomial models, the most significant were the increase in imports, the logarithm of GDP, and the logarithm of GDP per capita. A model of rating assessment of companies has been developed. Proposals are formulated for using the developed system as a simulation model when making decisions on the development and support of food exports in Russia. The authors propose a combined mechanism for supporting enterprises, depending on the rating determined by the model. It is concluded that the implementation of this approach will significantly increase the level of economic efficiency of budget support funds aimed at stimulating exports. The prospect for further research on this topic is to study the influence of qualitative factors that were not included in the model: the drought index, sanctions, and other macroeconomic events and parameters.

The conclusion summarizes the findings obtained by the authors of the monograph with respect to different dimensions of risk management in emerging markets.

Ratings in emerging markets can serve as part of the early warning systems to reflect the weak signals of potential risks to the entity from the environment. Emerging markets have specific features that rating agencies usually consider in judgments of their credit ratings. They are underpinned by the higher volatility, exposure to sovereign issues, weaknesses in institutional governance, and lower rating transparency. Emerging markets are served by both international and national rating agencies. The latter assign national scale ratings which are the opinions of the relative creditworthiness of issuer or the entity relative to the national benchmark. National scale ratings primarily focus on niche markets where they draw on familiarity with specific domestic economic and political circumstances and thus cannot be directly compared to international scale ratings. In the field of the regulation of rating activities, emerging countries follow the regulatory trends that have been established in Europe and the USA. However, the quality and depth of regulation depends significantly on the maturity of the rating industry of the particular countries.

Comparison of trends and peculiarities of financial systems in different countries, especially, in emerging markets, should start with setting the global context. The study identifies several periods of development of world financial institutions in the twenty-first century—deregulation (global optimism regarding financial development), re-regulation (change in the paradigm following the Global Financial Crisis), and de-globalization (growing divergence between conditions for doing banking in different countries). Progress of banking systems in EMs was additionally shaped by local peculiarities at the turn of the century, most of which were related to their location (e.g. European banks penetrated in CEE countries, Russian financial system was dominating in CIS). The common features of emerging markets were low banking services’ penetration and high promised returns. Through time, higher market saturation, technological advances, and trends in regulation and supervision increased degree of convergence in financial systems in developed and developing countries in what concerns main KPIs. That caused revision of focus towards greater attention to risk management and local needs. Global macroeconomic risks, related to countries with high debt, technological risks, and changing clients demands will become the drivers of banking systems development in emerging markets.

The series offers insights into the broad range of finance concepts applied to the specific environment in emerging capital markets.The series presents a broad range of theoretical concepts, empirical analyses and policy conclusions regarding financial markets, financial institutions, and corporate finance.Giving a voice to scholars from emerging and developed countries, comparative studies analyze different financial markets as well as firms' performance in emerging and developed economies.There is a particular focus on the largest emerging economies, namely the BRICS countries.

This paper examines the issues of the aggregation and comparison of the credit ratings of various economic agents for risk management purposes in a commercial bank. The empirical results of the study make it possible to increase the assessment of credit risks based on the constructed system of aggregating credit ratings for industrial companies and commercial banks. The work also confirms the relationship between the level of assigned credit ratings and the various phases of the credit cycle. The dynamics at the macroeconomic level shows that the credit ratings of various economic agents change in different directions and are out of sync with time correlation of credit cycles in various phases. The main scientific result of the study is an aggregate-based approach for credit risk evaluation of various economic agents and to develop the quantitative methods for assessing the relationship between the level of credit ratings and the credit cycle.

The purpose of this study is to identify relationships between changes in ratings and the impact of credit cycles on them. The following methodology was used: we built up an applied statistical probit-model of multiple-choice to determine ratings changes. Our model includes a credit gap indicator for assessing the impact of the credit cycle. Our empirical research also includes a review of the time changes in the ratings during a ten-year period for developed and developing countries. The results of our study show that credit ratings are not only affected by cyclical changes within the credit cycle, but also are delayed in its relation to the cycle. From a practical point of view, these results indicate the practical need to take into account various macroeconomic factors because of the impact of credit cycles for forecasting and risk management in financial markets. During the changes of credit cycles, the rating agencies consider the shifts in macrostructure and in valuation of parameters accordingly to the distribution and ratings proportion for investment and speculative ratings classes. The level of credit ratings and credit gap indicator are strongly influenced by two macroeconomic factors: GDP growth rates and credit spread, the last impact factor relates to the mechanism of monetary policy (as a narrow lending channel). In the end of the credit cycle and the stage of recession (downturn), which is marked by empirical evidence, large number of speculative credit ratings occur and the credit spread begins increase which leads to the rise of negative effects in financial markets.