The world financial crisis of 2008-2009 has shown that the existence of systemically important financial institutions (SIFIs) poses serious policy challenges to both developed and developing economies’ authorities. There are currently a range of different approaches to identifying SIFIs which focus on contagion, concentration, correlation and conditions effects. This paper aims at testing a new approach to SIFI identification based on Russian banking panel data. It is hypothesized that SIFIs are characterized by unique behaviour in terms of risks undertaken. Automatic clustering procedure is employed to find homogeneous groups of banks in terms of their risk patterns. Risk patterns include proxies for credit, market, operational risk values for each bank in a sample. In order to reconstruct aggregate risk patterns for the banking clusters, copula models are used. Time variances in risk profile are accounted by identifying copula structural shift moment. The paper also tests a hypothesis about the key role of the institution’s size in determining systemic importance. The effectiveness of SIFI identification based on their risk profile is evaluated. Finally, recommendations on the regulation of SIFIs in Russia are provided.
Modelling Risk Patterns of Russian Systemically Important Financial Institutions
ANDRIEVSKAYA, Irina;
2011-01-01
Abstract
The world financial crisis of 2008-2009 has shown that the existence of systemically important financial institutions (SIFIs) poses serious policy challenges to both developed and developing economies’ authorities. There are currently a range of different approaches to identifying SIFIs which focus on contagion, concentration, correlation and conditions effects. This paper aims at testing a new approach to SIFI identification based on Russian banking panel data. It is hypothesized that SIFIs are characterized by unique behaviour in terms of risks undertaken. Automatic clustering procedure is employed to find homogeneous groups of banks in terms of their risk patterns. Risk patterns include proxies for credit, market, operational risk values for each bank in a sample. In order to reconstruct aggregate risk patterns for the banking clusters, copula models are used. Time variances in risk profile are accounted by identifying copula structural shift moment. The paper also tests a hypothesis about the key role of the institution’s size in determining systemic importance. The effectiveness of SIFI identification based on their risk profile is evaluated. Finally, recommendations on the regulation of SIFIs in Russia are provided.
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