Управление ликвидностью в российском коммерческом банке
This article presents an engineering approach to estimating market resiliency based on analysis of the dynamics of a liquidity index. The method provides formal criteria for defining a “liquidity shock” on the market and can be used to obtain resiliency-related statistics for further research and estimation of this liquidity aspect. The developed algorithm uses the results of a spline approximation for observational data and allows a theoretical interpretation of the results. The method was applied to real data resulting in estimation of market resiliency for the given period.
The methodology of econometric approach to off-site monitoring of the Russian banking system is suggested. It includes econometric models of the probability of bank default, based on historical data on Russian bank defaults; models of ratings assigned to banks by rating agencies or experts, models of banks’ interest rates, and models of banks’ cost efficiency. Models are tested on real data in order to estimate possibility their potential use as part of Early Warning System in banks supervision.
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.