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2021 | Book

Risk Assessment and Financial Regulation in Emerging Markets' Banking

Trends and Prospects

Editors: Prof. Alexander M. Karminsky, Prof. Paolo Emilio Mistrulli, Prof. Mikhail I. Stolbov, Prof. Dr. Yong Shi

Publisher: Springer International Publishing

Book Series : Advanced Studies in Emerging Markets Finance


About this book

This book describes various approaches in modelling financial risks and compiling ratings. Focusing on emerging markets, it illustrates how risk assessment is performed and analyses the use of machine learning methods for financial risk assessment and measurement. It not only offers readers insights into the differences between emerging and developed markets, but also helps them understand the development of risk management approaches for banks. Highlighting current problems connected with the evaluation and modelling of financial risks in the banking sector of emerging markets, the book presents the methodologies applied to credit and market financial risks and integrated and payment risks, and discusses the outcomes. In addition it explores the systemic risks and innovations in banking and risk management by analyzing the features of risk measurement in emerging countries. Lastly, it demonstrates the aggregation of approaches to financial risk for emerging financial markets, comparing the experiences of various countries, including Russia, Belarus, China and Brazil.

Table of Contents


Banks in Emerging Markets

Peculiarities and Trends of Banking Systems Development
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.
Artem Arkhipov, Natalia Arkhipova, Alexander Karminsky
Regulation of Financial Risks in Emerging Markets: Past, Present, and Future
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.
Artem Arkhipov, Natalia Arkhipova, Alexander Karminsky

Ratings and Risk Measuring

Principles of Rating Estimation in Emerging Countries
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.
Sergei Grishunin, Natalia Dyachkova, Alexander Karminsky
Aggregation of Rating Systems for Emerging Financial Markets
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.
Sergei Grishunin, Natalia Dyachkova, Alexander Karminsky

Estimating and Modeling Credit and Market Risks in Banking

Bank Credit Risk Modeling in Emerging Capital Markets
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).
Alexander Karminsky, Alexei Morgunov
Loss Given Default Estimations in Emerging Capital Markets
This paper proposes an approach to decompose the RR/LGD model development process with two stages, specifically, for the RR/LGD rating model, and to calibrate the model using a linear form that minimizes residual risk. The residual risk in the recovery of defaulted debts is determined by the high uncertainty of the recovery level according to its average expected level. Such residual risk should be considered in the capital requirements for unexpected losses in the loan portfolio. This paper considers a simple residual risk model defined by one parameter. By developing an optimal RR/LGD model, it is proposed to use a residual risk metric. This metric gives the final formula for calibrating the LGD model, which is proposed for the linear model. Residual risk parameters are calculated for RR/LGD models for several open data sources for developed and developing markets. An implied method for updating the RR/LGD model is constructed with a correction for incomplete recovery through the recovery curve, which is built on the training sets. Based on the recovery curve, a recovery indicator is proposed which is useful for monitoring and collecting payments. The given recommendations are important for validating the parameters of RR/LGD model.
Mikhail Pomazanov
Comparing Bankruptcy Prediction Models in Emerging Markets
This paper presents an overview of the main models for predicting bankruptcies of companies and considers the classification of existing approaches. Examples of using algorithms such as logistic models, classification trees, random forests, and artificial neural networks are highlighted. Particular attention is paid to comparing traditional and advanced (based on ML) algorithms. The main development trends of this class of models are considered in Russia, China, and in developed markets of the USA and Europe. This paper forms the basis for the practical use of such models in solving risk management problems.
Roman Burekhin
Measures and Assessment of ALM Risks in Banks: Case of Russia
This сhapter focuses on the assessment and management of ALM risks: liquidity risk and interest-rate risk. The first part is devoted to liquidity risk: various types of liquidity risk, its sources, measures, and the principles of liquidity risk management, as well as scenarios for stress testing of liquidity risk. The second part focuses on the concept and types of interest-rate risk, the methods of evaluation (metrics) and approaches to its management. In the conclusion, current challenges in assessing and managing ALM risks are presented.
Ekaterina Seryakova
Forecasting and Backtesting of Market Risks in Emerging Markets
Emerging markets often go through periods of financial turbulence and the estimation of market risk measures may be problematic. Online search queries and implied volatility may (or may not) improve the model estimates. In these situations a step-by-step analysis with R and Russian market data is provided. Four classes of models are considered (GARCH, HAR, ARFIMA, and realized-GARCH), and a detailed forecasting and backtesting investigation is performed.
Dean Fantazzini
Integrated Risk Measurement System in Commercial Bank
Integrated risk management means the comprehensive and effective management all significant risks (affecting the bank’s activities) and their interrelation, including building a corporate culture of risk management and integrating risk management into strategic planning. The significant risks have big impact on the financial result of the bank, its capital, and liquidity, business reputation, their consideration is required for the assessment of banking creditworthiness and stability for regulators. In the context of economic crises and sanctions, the role of effective risk management in banks is significantly increasing, as it allows the bank to adequately distribute its capital and reserves and contributes to its stable existence in the face of uncertainty. The most significant risks in banking are credit and liquidity risks. In the banking sector, a significant methodological base has now been accumulated for assessing and managing these types of risks. The purpose of this study is to systematize the approaches to the formation of a risk management system in Russian and world practice, to assess their advantages and disadvantages, and also to formulate a list of recommendations for improving the existing system. Decision-making at management levels takes place in conditions of uncertainty in the external and internal environment, which causes partial or complete uncertainty in the final results of activities. In economics, uncertainty is understood as incompleteness or inaccuracy of information on the conditions of economic activity, including the costs and the results. The causes of uncertainty are three main factors: ignorance, randomness, and competition. In particular, the uncertainty is explained by the fact that the problems are reduced to the tasks of choosing from a certain number of alternatives, while the banks do not have full knowledge of the situation to work out the optimal solution, and do not have the resources to adequately account for all the information available to them. A measure of uncertainty is risk, i.e. the probability of occurrence of events, as a result of which unexpected losses of income, property, cash, and other assets are possible. In modern banking risk management systems, procedures for influencing individual risk events or types of risk are increasingly being replaced by the organization of continuous monitoring of the bank’s aggregate risk and the management of the value of various businesses of a credit institution adjusted for their inherent risk. This conceptual approach is called Integrated Risk Management (IRM). In the international banking regulation standards, the IRM logic is disclosed by the requirements of Component 2 of the Basel II and Basel III agreements (BKBN 2004, 2010), in Russian practice—Bank of Russia Ordinance No. 3624-U “On requirements for the risk and capital management system credit organization and banking group”(Bank of Russia, On Requirements for the Risk and Capital Management System of a Credit Institution and a Banking Group, 2015).
Alexander Zhevaga, Alexei Morgunov
Economic Capital Structure and Banking Financial Risks Aggregation
Banks must maintain a balance between their own capital and the level of accepted aggregate risk to ensure financial stability. This paradigm is expressed in terms of capital adequacy requirements to both the minimum capital required to cover regulatory risks and the risk capital required to fully cover bank’s total risk (economic capital). Therefore, the Basel Committee on Banking Supervision requires banks to implement ICAAP procedures to ensure regular risk assessment and maintain a sufficient level of capital. The Basel Committee on Banking Supervision regularly analyzes the implementation of ICAAP by global systemically important banks (G-SIB). Following the results of the analysis, the Committee has identified a number of relevant development areas: selection of approach to aggregate different material risks, detection and allocation of risk capital taking into consideration the effect of diversification, and setting limits as a function of capital allocation by activities and types of risks. This section offers a solution to the problem. It presents a conceptual approach to determining economic capital structure, which is based on material risk identification and on the determination among them of financial risks, assessed using quantitative methods. We propose a simulation model of the bank’s economic capital  where the total risk is presented as a composition of the products of the material risk’s factors on the P&L elements exposed to these risks. Thus, the elements of the P&L define the weights for the material risk’s distributions in the economic capital model. The economic capital model makes it possible to assess the distribution of the bank’s total risk at different management levels (products—departments—total bank), disaggregate the available capital by products, business lines, and types of risks and, on this basis, establish limits based on the distribution of capital in accordance with the Pillar-2 requirements of Basel II.
Marina Pomorina

Systemic Risks Modeling and Stress Testing

Exploring the Interplay Between Early Warning Systems’ Usefulness and Basel III Regulation
We analyse the ability of credit gap measures to predict banking crises by estimating the usefulness measure conditionally on policymaker’s preferences. The results show that the signals based on the credit gap indicators are most useful when the policymaker’s preferences regarding Type I and Type II errors are approximately equal. However, according to the current consensus, the preferences to avoid missing a crisis are higher than issuing a false signal. This means that the usefulness of the credit-gap-based early warning systems is likely to increase once the static Basel III regulative measures are implemented (assuming that their implementation results in lower financial crises’ costs).
Elena Deryugina, Maria Guseva, Alexey Ponomarenko
Does Only Volume Matter? A Stress Test for the Adequacy of International Currency Reserves for Russia
This paper seeks to determine the optimal volume of international currency reserves of the Bank of Russia to prevent adverse fluctuations of the Russian ruble exchange rate, causing a threat to financial stability. We create a system of models, taking into account the linkages between the dynamics of exchange rate and the behavior of economic agents—households, non-financial industries, and banks. Our empirical exercise allows to conclude that, with the occurrence of the most severe stress and the immediate provisions of currency liquidity by the Bank of Russia, the current volume of international reserves will be sufficient to eliminate its consequences. However, in case of retarded provisions of currency liquidity, the volume of highly liquid reserves will not be sufficient, forcing the Bank of Russia to sell a significant volume of foreign government securities. In this light, the Bank of Russia should change the structure of the international reserves in favor of highly liquid assets by reducing the share of securities and increasing the share of short-term deposits in foreign banks with high credit ratings. As for the volume of international reserves for Russia, including less liquid components, it is sufficient to surpass the maximum possible stress in the foreign exchange market and to subsequently keep the Russian economy sustainable.
Renat Akhmetov, Vera Pankova, Oleg Solntsev, Elizaveta Orlova
Regulatory Measures Against Systemic Risk in Banking Sector: The Evidence for the Republic of Belarus
This paper discusses the framework of systemic risk assessment and monitoring in the Belarusian banking sector. It involves comparisons with similar approaches in Russia and Kazakhstan, showing that these countries are generally keen to adopt the tools proposed by the Basel Committee on Banking Supervision. As for the Republic of Belarus, standard risk management instruments so far have sufficed to prevent risk propagation, while the need for a proper legal definition and the demarcation of systemic risk is emphasized.
Svetlana Malykhina
Real Effects of Financial Shocks in Russia
Over the last 10 years, Russia has faced many external and internal challenges. Using the Financial Stress Index for Russia (the ACRA FSI), which indicates the proximity of the Russian financial system to crisis and its reaction to different events, I show that shocks in the Russian financial system have adverse effects on real economic activity. The VAR model and Toda–Yamamoto augmented Granger causality tests are my research tools. I also estimate a threshold structural VAR model, revealing that the impact of a financial shock is bigger and longer lasting for distressed periods compared to normal periods in the Russian financial system. All my findings are in line with other research studies for both emerging and advanced economies.
Vasilisa Baranova

Estimating and Managing Financial Risks: Topical Trends in Emerging Capital Markets

Innovation in Developing Countries’ Risk Estimation and Management
Today, an increasingly important role in the economy is being acquired by informatization processes. Digital technologies simplify information transfer and accelerate these processes. The effective use of various complex banking technologies, as well as the use of information and communication technologies in banking operations, can improve the organization of financial products and various tools that are key ways to stimulate the needs and preferences of customers. Various financial innovations, including Internet banking, ATMs, and mobile banks, are increasingly becoming a vital force for diversification, revenue generation, and cost reduction for both banks and customers. The article is devoted to the problem of development of innovations in the field of financial technologies in developing countries. It is shown that, despite a significant increase in innovation activity in the field of the financial sector of the economy, a large number of developments in this area, some gaps remain associated with the practical application, implementation of financial innovations, the use of innovative tools in the field of financial technologies in developing economies, the definition of the role and places of financial innovation in the overall structure of the financial sector. This article aims to fill these gaps. There is a connection between financial innovation and the efficiency of the banking industry for both developed and developing countries around the world. The banking sector in a developing economy is growing thanks to financial innovations in various payment systems, including the use of ATMs, mobile banking, and electronic banking. The aim of the work is to analyze the innovation of risk assessment and risk management in developing countries, for which the following tasks were set: consider the features of information technology, financial instruments, and services; explore diffusion models of banking innovations; identify features of digital solutions in developing countries.
Andrey Egorov, Dmitry Pomazkin
Dynamic Fractal Asset Pricing Model for Financial Risk Evaluation
This article is dedicated to the assessment of the dynamic fractional asset pricing model for financial risk evaluation and the use of the fractal markets theory to mathematically predict the price dynamics of assets as part of a financial risk management strategy. The article identifies recommendations for assessing financial risk based on mathematical methods for forecasting economic processes. Theoretical and empirical research methods were used. The article reveals the features of mathematical modeling of economic processes related to asset pricing in a volatile market. It is shown that financial mathematics in banking contributes to the stable development of the economy. The mathematical modeling of the price dynamics of financial assets is based on a substantive hypothesis and supported by fractal pair pricing models in order to reveal the specific market relations of business entities. According to the authors, the prospects of using forecast models to minimize the financial risks of derivative financial instruments are positive. The authors conclude that the considered methods contribute to managing financial risks and improving forecasts, including operations with derivatives.
Bruno de Conti, Vladimir Gisin, Irina Yarygina
Network Effects in Retail Payments Market: Evidence from Individuals
This paper evaluates empirically the effect of network externalities on individual behavior in the Russian retail payments market. Specifically, the effects of direct and indirect network externalities for cardholding and usage probabilities are examined. Using a representative sample of 1500 individuals across Russian regions, this paper finds significant robust evidence of a positive association between the degree of both types of network externalities and individuals’ activity in the Russian retail payments market. Results are economically significant: a standard deviation increase in network effects leads to a 2.5–4 percentage points increase in the probability of cardholding and usage. The findings suggest a need to account for network effects that play an important role in the payment behavior before implementing any payment stimulating programs in Russia aimed at cardholders or users.
Egor Krivosheya
Conclusion: Instruments of Financial Sustainability in Emerging Markets
The conclusion summarizes the findings obtained by the authors of the monograph with respect to different dimensions of risk management in emerging markets.
Alexander Karminsky, Paulo Emilio Mistrulli, Mikhail Stolbov, Yong Shi
Risk Assessment and Financial Regulation in Emerging Markets' Banking
Prof. Alexander M. Karminsky
Prof. Paolo Emilio Mistrulli
Prof. Mikhail I. Stolbov
Prof. Dr. Yong Shi
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