Dynamical Macroprudential Stress Testing Using Network Theory by OFR
Sary Levy-Carciente (a,b), Dror Y. Kenetta (c), Adam Avakian (a), H. Eugene Stanley (a), Shlomo Havlin (d)
(a) Center for Polymer Studies and Department of Physics, Boston University, Boston, USA
(b) Facultad de Ciencias Economicas y Sociales, Universidad Central de Venezuela, Caracas, Venezuela
(c) U.S. Department of the Treasury, Office of Financial Research
(d) Department of Physics, Bar-Ilan University, Ramat-Gan, Israel
Abstract
The increasing frequency and scope of financial crises have made global financial stability one of the major concerns of economic policy and decision makers. This has led to the understanding that financial and banking supervision has to be thought of as a systemic task, focusing on the interdependent relations among the institutions. Using network theory, we develop a dynamic model that uses a bipartite network of banks and their assets to analyze the system’s sensitivity to external shocks in individual asset classes and to evaluate the presence of features underlying the system that could lead to contagion. As a case study, we apply the model to stress test the Venezuelan banking system from 1998 to 2013. The introduced model was able to capture monthly changes in the structure of the system and the sensitivity of bank portfolios to different external shock scenarios and to identify systemic vulnerabilities and their time evolution. The model provides new tools for policy makers and supervision agencies to use for macroprudential dynamical stress testing.
Dynamical Macroprudential Stress Testing Using Network Theory – Introduction
As the banking system of the world has become ever more complex and technological, there has been the need for more advanced supervision of the banking system as well. The financial crisis of 2007-09 made it more clear than ever before that the financial system is a complicated network and needs to be modeled as such by regulators. Most regulation standards still focus on microprudential factors, and although many advances have been made in modeling and stress testing bank networks, we are still far from a unified framework to confidently monitor systemic risk.
So far, most network-based models have focused on bank-to-bank networks, generally linking either via correlated exposures or direct interbank obligations. Such models can be useful when stress testing using individual bank failures as a starting point. However, financial crises often begin with toxic assets, as we saw with real estate-based assets in the 2007-09 financial crisis. A valuable tool to model such crises is a bipartite bank-asset network with banks and assets as elements of the system. We present such a tool and show how it may be used to monitor the whole system’s sensitivity to shocks in various asset prices, as well as which banks are most likely to fail.
1.1. Basel regulation
The Bank of International Settlements (BIS) is a multilateral agency that has paid attention to financial crises since the 1980s. Guidelines on regulation and financial supervision have emerged out of BIS research. Although BIS guidelines are not mandatory, the technical prestige and respectability of the institution attracts voluntary compliance.
In 1988 the Basel Committee on Banking Supervision, BCBS, posted the Basel Capital Accord (International Convergence of Capital Measurement and Capital Standards), better known as Basel I, which proposed banks should keep a minimum amount of equity, equivalent to 8 percent of their risk-weighted assets (Basel Committee on Banking Supervision (BCBS), 1998) in order to maintain global financial stability and a solid and adequately capitalized system.
In 2004, the BCBS published the New Capital Adequacy Framework, known as Basel II. While Basel I considered market and credit risks, Basel II substantially changed the treatment of credit risk and also required that banks should have enough capital to cover operational risks. Basel II also demanded greater transparency of information about credit risk and increased the documentation required to debtors, as well as diversification of balance through insurance activities (Basel Committee on Banking Supervision (BCBS), 2006).
In 2008, the BCBS introduced Basel III. Basel III introduces more stringent regulations to address liquidity risk and systemic risk, raises loan underwriting standards, and emphasizes the need for appropriate handling or removal of spaces with conflict of interest (Ito, 2011). Basel III also instituted some macroprudential measures to ensure banking operation even in times of systemic problems. During the 2010 G-20 Summit in Seoul, South Korea, Basel III standards were established to create greater banking stability through better microprudential supervision. Those standards will be implemented over the next decade.
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