Measures of Systemic Risk

Systemic risk refers to the risk that the financial system is susceptible to failures due to the characteristics of the system itself. The tremendous cost of systemic risk requires the design and implementation of tools for the efficient macroprudential regulation of financial institutions. The current paper proposes a novel approach to measuring systemic risk. Key to our construction is a rigorous derivation of systemic risk measures from the structure of the underlying system and the objectives of a financial regulator. The suggested systemic risk measures express systemic risk in terms of capital endowments of the financial firms. Their definition requires two ingredients: a cash flow or value model that assigns to the capital allocations of the entities in the system a relevant stochastic outcome; and an acceptability criterion, i.e. a set of random outcomes that are acceptable to a regulatory authority. Systemic risk is measured by the set of allocations of additional capital that lead to acceptable outcomes. We explain the conceptual framework and the definition of systemic risk measures, provide an algorithm for their computation, and illustrate their application in numerical case studies. Many systemic risk measures in the literature can be viewed as the minimal amount of capital that is needed to make the system acceptable after aggregating individual risks, hence quantify the costs of a bail-out. In contrast, our approach emphasizes operational systemic risk measures that include both ex post bailout costs as well as ex ante capital requirements and may be used to prevent systemic crises.Comment: 35 pages, 11 figure

Consistent measures of systemic risk

This paper presents a methodology to infer multivariate densities that characterize the asset values for a system of financial institutions, and applies it to quantify systemic risk. These densities, which are inferred from partial information but are consistent with the observed probabilities of distress of financial institutions, outperform parametric distributions typically employed in risk measurement. The multivariate density approach allows us to propose complementary and statistically consistent metrics of systemic risk, which we estimate using market-based data to analyze the evolution of systemic risk in Europe and the U.S., throughout the financial crisis

Econometric Measures of Systemic Risk in the Finance and Insurance Sectors

We propose several econometric measures of systemic risk to capture the interconnectedness among the monthly returns of hedge funds, banks, brokers, and insurance companies based on principal components analysis and Granger-causality tests. We find that all four sectors have become highly interrelated over the past decade, increasing the level of systemic risk in the finance and insurance industries. These measures can also identify and quantify financial crisis periods, and seem to contain predictive power for the current financial crisis. Our results suggest that hedge funds can provide early indications of market dislocation, and systemic risk arises from a complex and dynamic network of relationships among hedge funds, banks, insurance companies, and brokers.

THE DETERMINANTS OF SYSTEMIC RISK: EVIDENCE FROM INDONESIAN COMMERCIAL BANKS

This paper examines the determinants of systemic risk across Indonesian commercial banks using quarterly data from 2001Q4 to 2017Q4. Employing four measures of systemic risk, namely value-at-risk (VaR), historical marginal expected shortfall (MESH), marginal expected shortfall from GARCH-DCC (MESdcc), and long-run marginal expected shortfall (LRMES), we find that bank size is positively related to systemic risk, whereas banks and economic loan activity are negatively related to systemic risk. These findings suggest that the government needs to regulate loan activities and to monitor big banks as they have significant impacts on bank systemic risk.This paper examines the determinants of systemic risk across Indonesian commercial banks using quarterly data from 2001Q4 to 2017Q4. Employing four measures of systemic risk, namely value-at-risk (VaR), historical marginal expected shortfall (MESH), marginal expected shortfall from GARCH-DCC (MESdcc), and long-run marginal expected shortfall (LRMES), we find that bank size is positively related to systemic risk, whereas banks and economic loan activity are negatively related to systemic risk. These findings suggest that the government needs to regulate loan activities and to monitor big banks as they have significant impacts on bank systemic risk

Monitoring Leverage

We discuss how leverage can be monitored for institutions, individuals, and assets. While traditionally the interest rate has been regarded as the important feature of a loan, we argue that leverage is sometimes even more important. Monitoring leverage provides information about how risk builds up during booms as leverage rises and how crises start when leverage on new loans sharply declines. Leverage data is also a crucial input for crisis management and lending facilities. Leverage at the asset level can be monitored by down payments or margin requirement or and haircuts, giving a model-free measure that can be observed directly, in contrast to other measures of systemic risk that require complex estimation. Asset leverage is a fundamental measure of systemic risk and so is important in itself, but it is also the building block out of which measures of institutional leverage and household leverage can be most accurately and informatively constructed.Leverage, Loan to value, Margins, Haircuts, Monitor, Regulate, Leverage on new loans, Asset leverage, Investor leverage

Financial Institutions and Systemic Risk: The Case of Bank of America 2006-2017

This paper explores systemic risk and financial institutions before, during, and after the financial crisis. It focuses on Bank of America the 2nd largest bank in the United States by assets. The paper includes an introduction to systemic risk and a review of literature on systemic risk. A few traditional measures of systemic risk will be defined, such as nonperforming loans, return on assets, return on equity, earnings per share, net interest margin, and capital adequacy ratio. Finally, the paper will take a look at how these traditional measures specifically relate to Bank of America from 2006 to 2017. This time period was chosen to show how the risk measures fluctuate before, during, and after the 2008 financial crisis. This crisis is considered by many to be a time when systemic risk was relatively high in the banking sector. This study finds that systemic risk can be evaluated in many different ways. Outside forces also have an impact on systemic risk in the banking environment. Systemic risk is a financial topic that will only increase in importance as financial innovation and globalization continue to evolve

Systemic Risk and Network Formation in the Interbank Market

We propose a novel mechanism to facilitate understanding of systemic risk in financial markets. The literature on systemic risk has focused on two mechanisms, common shocks and domino-like sequential default. Our approach is a formal model that provides an intellectual combination of the two by looking at how shocks propagate through a network of interconnected banks. Transmission in our model is not based on default. Instead, we provide a simple microfoundation of banks’ profitability based on classic competition incentives. As competitors lending quantities change, both for closely connected ones and the whole market, banks adjust their own lending decisions as a result, generating a ‘transmission’ of shocks through the system. We provide a unique equilibrium characterization of a static model, and embed this model into a full dynamic model of network formation with n agents. Because we have an explicit characterization of equilibrium behavior, we have a tractable way to bring the model to the data. Indeed, our measures of systemic risk capture the propagation of shocks in a wide variety of contexts; that is, it can explain the pattern of behavior both in good times as well as in crisis.Financial networks; interbank lending; interconnections; network centrality; spatial autoregressive models