Against the background of the great financial crisis, this paper assesses the merits of bank-based versus market-based financing by exploring the relationship between financial structure and systemic risk. The findings indicate that bank-based financial structures are associated with higher systemic risk than market-based financial structures. In relatively bank-based financial structures, bank financing is found to increase systemic risk while market financing decreases systemic risk. By contrast, in relatively market-based financial structures, bank and market financing do not impact systemic risk. Together, the results signal that market-based financial structures are more resilient to systemic risk

In this paper we develop an in-depth analysis of the systemic risk and contagion determinants through the differential effects of excluding one bank on the banking system. The measure allows for splitting the contribution of individual banks into systemic risk as the sum of two components—the stand-alone bank risk and the contagion risk—and measuring the role of assets, riskiness, capitalization, and interconnectedness as determinants of each of the two components. Results show that the variables determining the stand-alone risk component are different from those determining the contagion risk component, so that a bank which is relatively safe with respect to stand-alone risk, can be an important contagion vehicle, or vice versa. Results also show that crisis severity significantly affects results, so that the severity of different crises results in different weights for the input variables and different contributions for the banks considered. These results add highly significant information for macroprudential regulation, not only from the cross-sectional point of view, but also with reference to the time dimension

We evaluate the performance of two popular systemic risk measures, CoVaR and SRISK, during eight financial panics in the era before FDIC insurance. Bank stock price and balance sheet data were not readily available for this period. We rectify this shortcoming by constructing a novel dataset for the New York banking system before 1933. Our evaluation exercise focuses on two challenges: ranking systemically important financial institutions (SIFIs) and financial crisis prediction. We find that CoVaR and SRISK meet the SIFI ranking challenge. That is, they help identify systemic institutions in periods of distress beyond what is explained by standard risk measures up to six months before panics. In contrast, aggregate CoVaR and SRISK are only somewhat effective at predicting financial crises

We investigate the effects of the announcement and the disclosure of the clarification, methodology, and outcomes of the U.S. banking stress tests on banks’ equity prices, credit risk, systematic risk, and systemic risk. We find evidence that stress tests have moved stock and credit markets following the disclosure of stress test results. We also find that banks’ systematic risk, as measured by betas, declined in nearly all years after the publication of stress test results. Our evidence suggests that stress tests affect systemic risk

We investigate whether and how the uniqueness of banking activities affects the performance and systemic risk of U.S. banks. We find that banks performing more unique activities exhibit higher profitability and lower risk, controlling for size, diversification, and other key characteristics. We further find that banks’ sensitivity to systemic risk displays an inversely U-shaped relation with activity uniqueness. We interpret the impact of uniqueness in analogy to recent theories showing that systemic diversity promotes financial stability. Our study highlights the role of uniqueness in banking and has important implications for policy makers and banking regulators

To capture systemic risk related to network structures, this paper introduces a measure that complements direct exposures with common exposures, as well as compares these to each other. Trying to address the interconnected nature of financial systems, researchers have recently proposed a range of approaches for assessing network structures. Much of the focus is on direct exposures or market-based estimated networks, yet little attention has been given to the multivariate nature of systemic risk, indirect exposures and overlapping portfolios. In this regard, we rely on correlation network models that tap into the multivariate network structure, as a viable means to assess common exposures and complement direct linkages. Using BIS data, we compare correlation networks with direct exposure networks based upon conventional network measures, as well as we provide an approach to aggregate these two components for a more encompassing measure of interconnectedness

[First paragraph] The worldwide financial crisis (WFC) of 2007-09 has shown the importance of cross-sectional dependencies of assets, credit exposures and volatility, which can threaten domestic and global financial stability through cascades in financial networks. A correct assessment of company-specific risk has to account for the potential risk spillover effects from other firms (Hautsch et al. 2014). This is because of the intertwined nature of financial markets, which allow the spread of risk throughout the system (Acemoglu et al., 2015). The potential impact of interconnected financial institutions on the entire financial system has been a financial stability concern for central banks and regulators. The need for economic foundations for a systemic risk measure is more than an academic concern since it involves regulators, supervisory authorities and policy-makers (Acharya et al. 2017). This special issue provides a substantial contribution to the systemic risk literature

We consider a financial network where banks are heterogeneous in scale and each bank has only local knowledge regarding the network. Each bank must make counterparty and portfolio decisions while anticipating uncertainty regarding the network structure. Such network uncertainty is an important consideration in banks’ risk management practice, which aims to minimize the effect of exogenous liquidity shocks and hedge against possible fire-sale in asset markets. We show that network uncertainty gives rise to an endogenous core-periphery structure which is optimal in mitigating financial contagion yet concentrates systemic risk at the core of big banks