Bad management, skimping, or both? The relationship between cost efficiency and loan quality in Russian banks
The paper presents a review of stochastic framework for term structure modeling and shows comparative advantages of commonly used techniques. The main application of the research is coherent modeling of credit and interest rate risk for Euro zone issuers.
Bank stabilization measures adopted by the Russian authorities since 2008 have benefited core state-owned financial institutions to a greater extent than other market participants. Public sector keeps swelling at the expense of domestic private sector. According to the author’s methodology, by January 2010 state-controlled banks possessed over 50 percent of all bank assets, thus putting Russia in the same league with China and India. Development banking and policy lending expand. A feature distinguishing Russia is gradual substitution of direct state control by indirect state ownership in the shape of corporate pyramids headed by state-owned enterprises and state-owned banks. We construct a dataset of bank-level statistical data for the period between 2001 and 2010 and find that quasi-private banks (indirectly state-owned banks) were the fastest growing subgroup. Nationalization and rehabilitation of failed banks was carried out by state-controlled banks and entities rather than by federal executive authorities directly. We suggest that the response of the Russian authorities to bank instability was consistent with long-term trends in the banking system evolution. Anti-crisis measures of 2008-9 re-aligned the sector with the traditional model of banking that rests upon dominant state-owned banks, directed lending, protectionism, administrative interference and elements of price controls. Increased government ownership of banks and control over lending activity are unlikely to be fully dismantled after the crisis is over. This scenario can nevertheless accommodate a tactical retreat of the state from non-core assets in the financial sector, leaving control over 3 largest institutions intact.
The purpose of this paper is to assess the size of public sector within the Russian banking industry. We identify and classify at least 78 state-influenced banks. We distinguish between banks that are majority-owned by federal executive authorities or Central Bank of Russia, by sub-federal (regional and municipal) authorities, by state-owned enterprises and banks, and by "state corporations". We estimate their combined market share to have reached 56% of total assets by July 1, 2009. Banks indirectly owned by public capital are the fastest-growing group. Concentration is increasing within the public sector of the industry, with the top five state-controlled banking groups in possession of over 49% of assets. We observe a crowding out and erosion of domestic private capital, whose market share is shrinking from year to year. Several of the largest state-owned banks now constitute a de facto intermediate tier at the core of the banking system. We argue that the direction of ownership change in Russian banking is different from that in CEE countries.
In textbook the main issues connected with organization of credit analysis in a commercial bank were considered. The role of credit analysis in risk management system is shown. The methodology and specific methods for assessing the creditworthiness of borrowers used by banks are set out by complex approach. The textbook includes international recommendations for introduction of internal credit risk assessment systems in banks. With the aim at presenting the material examples from the practice of commercial banks, analytical tables, diagrams and figures were used.
The process of the IPO of banks in Russia is its infancy but the rapid growth is forecasted. This context raises the issue of the factors determining the floated banks stock value. The results of the research on 2007-2009 Russian data showed that the bank stock price is dependent on the macroeconomic indicators (such as the oil prices and the Dow Jones index volatility) and the some banking system indicators(the interbank interest rate, the bank’s ROA, and ROE). However, the results adjusted to the global financial crisis effect proved to exclude the ROE factor and showed the dependence of the stocks prices of the floated banks from the historic trend of the American economy. The models developed are of the practical application and can be used by the institutional as well as the private investors.
The purpose of this paper is to carefully assess the size of public sector within the Russian banking industry. We identify and classify at least 78 state-influenced banks. For the state-owned banks, we distinguish between those that are majority-owned by federal executive authorities or Central Bank of Russia, by sub-federal (regional and municipal) authorities, by state-owned enterprises and banks, and by "state corporations". We estimate their combined market share to have reached 56% of total assets by July 1, 2009. Banks indirectly owned by public capital are the fastest-growing group. Concentration is increasing within the public sector of the industry, with the top five state-controlled banking groups in possession of over 49% of assets. We observe a crowding out and erosion of domestic private capital, whose market share is shrinking from year to year. Several of the largest state-owned banks now constitute a de facto intermediate tier at the core of the banking system. We argue that the direction of ownership change in Russian banking is different from that in CEE countries.
The paper considers the financial choice of entrepreneurs at their initial stage of development as a key criterion of a new firm potential riskiness. The main objective of the research is the methodology elaboration aimed at the numerical estimation of the role of informal financial resources involved in the small business creation. Two fundamental considerations have been tested. The former implies that informal investment is a substitution for unavailable formal sources, including venture capital (because of the lack of essential networks and connections with business associations). The latter performs the opposite concept of negative effects: economic reasoning discouragement and inefficient resources allocation. A special technique is introduced in order to measure the credit quality of early entrepreneurial activity and to estimate its contingency with the financial strategy. The methodology validation is realised under Global Entrepreneurship Monitor conceptual framework. The results are received for 42 countries in 2006-2007, depicting the influence of informal support on potential losses under the second consideration. As a result, informal investments are inefficient when the concentration of credit risk in the economy is rather high. Investorsђ expectations about the entrepreneurial growth of the firm are pessimistic, anticipated returns on investments are too low to be economically reasonable. The outcome leads to the irrecoverable losses, both financial (short-received profitability) and nonfinancial (decreased output, the lack of innovativeness, flexibility, and inventiveness).
This paper uses the banking industry case to show that the boundaries of public property in Russia are blurred. A messy state withdrawal in 1990s left publicly funded assets beyond direct reach of official state bodies. While we identify no less than 50 state-owned banks in a broad sense, the federal government and regional authorities directly control just 4 and 12 institutions, respectively. 31 banks are indirectly state-owned, and their combined share of state-owned banks’ total assets grew from 11% to over a quarter between 2001 and 2010. The state continues to bear financial responsibility for indirectly owned banks, while it does not benefit properly from their activity through dividends nor capitalization nor policy lending. Such banks tend to act as quasi private institutions with weak corporate governance. Influential insiders (top-managers, current and former civil servants) and cronies extract their rent from control over financial flows and occasional appropriation of parts of bank equity.
The paper examines the structure, governance, and balance sheets of state-controlled banks in Russia, which accounted for over 55 percent of the total assets in the country's banking system in early 2012. The author offers a credible estimate of the size of the country's state banking sector by including banks that are indirectly owned by public organizations. Contrary to some predictions based on the theoretical literature on economic transition, he explains the relatively high profitability and efficiency of Russian state-controlled banks by pointing to their competitive position in such functions as acquisition and disposal of assets on behalf of the government. Also suggested in the paper is a different way of looking at market concentration in Russia (by consolidating the market shares of core state-controlled banks), which produces a picture of a more concentrated market than officially reported. Lastly, one of the author's interesting conclusions is that China provides a better benchmark than the formerly centrally planned economies of Central and Eastern Europe by which to assess the viability of state ownership of banks in Russia and to evaluate the country's banking sector.
The paper examines the principles for the supervision of financial conglomerates proposed by BCBS in the consultative document published in December 2011. Moreover, the article proposes a number of suggestions worked out by the authors within the HSE research team.
The paper studies a problem of optimal insurer’s choice of a risk-sharing policy in a dynamic risk model, so-called Cramer-Lundberg process, over infinite time interval. Additional constraints are imposed on residual risks of insureds: on mean value or with probability one. An optimal control problem of minimizing a functional of the form of variation coefficient is solved. We show that: in the first case the optimum is achieved at stop loss insurance policies, in the second case the optimal insurance is a combination of stop loss and deductible policies. It is proved that the obtained results can be easily applied to problems with other optimization criteria: maximization of long-run utility and minimization of probability of a deviation from mean trajectory.