Systemic risk across sectors; Are banks different?

This research compares systemic risk in the banking sector, the insurance sector, the construction sector, and the food sector. To measure systemic risk, we use extreme negative returns in stock market data for a time-varying panel of the 20 largest U.S. firms in each sector. We find that systemic risk is significantly larger in the banking sector relative to the other three sectors. This result is robust to separating out correlations with an economy-wide stock market index. For the non-banking sectors, the ordering from high to low systemic risk is: insurance sector, construction sector, and food sector. The difference between the insurance sector and the construction sector is no longer significant after correcting for correlations with the economy as a whole. The correction has a large effect for the banking sector and the insurance sector, and a smaller effect for the other two sectors.

The multi-layer network nature of systemic risk and its implications for the costs of financial crises

The inability to see and quantify systemic financial risk comes at an immense social cost. Systemic risk in the financial system arises to a large extent as a consequence of the interconnectedness of its institutions, which are linked through networks of different types of financial contracts, such as credit, derivatives, foreign exchange and securities. The interplay of the various exposure networks can be represented as layers in a financial multi-layer network. In this work we quantify the daily contributions to systemic risk from four layers of the Mexican banking system from 2007-2013. We show that focusing on a single layer underestimates the total systemic risk by up to 90%. By assigning systemic risk levels to individual banks we study the systemic risk profile of the Mexican banking system on all market layers. This profile can be used to quantify systemic risk on a national level in terms of nation-wide expected systemic losses. We show that market-based systemic risk indicators systematically underestimate expected systemic losses. We find that expected systemic losses are up to a factor four higher now than before the financial crisis of 2007-2008. We find that systemic risk contributions of individual transactions can be up to a factor of thousand higher than the corresponding credit risk, which creates huge risks for the public. We find an intriguing non-linear effect whereby the sum of systemic risk of all layers underestimates the total risk. The method presented here is the first objective data driven quantification of systemic risk on national scales that reveal its true levels.Comment: 15 pages, 6 figure

Default risk in an interconnected banking system with endogeneous asset markets : [Version: August 2011]

This paper analyzes the emergence of systemic risk in a network model of interconnected bank balance sheets. Given a shock to asset values of one or several banks, systemic risk in the form of multiple bank defaults depends on the strength of balance sheets and asset market liquidity. The price of bank assets on the secondary market is endogenous in the model, thereby relating funding liquidity to expected solvency - an important stylized fact of banking crises. Based on the concept of a system value at risk, Shapley values are used to define the systemic risk charge levied upon individual banks. Using a parallelized simulated annealing algorithm the properties of an optimal charge are derived. Among other things we find that there is not necessarily a correspondence between a bank's contribution to systemic risk - which determines its risk charge - and the capital that is optimally injected into it to make the financial system more resilient to systemic risk. The analysis has policy implications for the design of optimal bank levies. JEL Classification: G01, G18, G33 Keywords: Systemic Risk, Systemic Risk Charge, Systemic Risk Fund, Macroprudential Supervision, Shapley Value, Financial Networ

Multivariate Shortfall Risk Allocation and Systemic Risk

The ongoing concern about systemic risk since the outburst of the global financial crisis has highlighted the need for risk measures at the level of sets of interconnected financial components, such as portfolios, institutions or members of clearing houses. The two main issues in systemic risk measurement are the computation of an overall reserve level and its allocation to the different components according to their systemic relevance. We develop here a pragmatic approach to systemic risk measurement and allocation based on multivariate shortfall risk measures, where acceptable allocations are first computed and then aggregated so as to minimize costs. We analyze the sensitivity of the risk allocations to various factors and highlight its relevance as an indicator of systemic risk. In particular, we study the interplay between the loss function and the dependence structure of the components. Moreover, we address the computational aspects of risk allocation. Finally, we apply this methodology to the allocation of the default fund of a CCP on real data.Comment: Code, results and figures can also be consulted at https://github.com/yarmenti/MSR

Systemic Risk

Governments and international organizations worry increasingly about systemic risk, under which the world’s financial system can collapse like a row of dominoes. There is widespread confusion, though, about the causes and even the definition of systemic risk, and uncertainty about how to control it. This Article offers a conceptual framework for examining what risks are truly “systemic,” what causes those risks, and how, if at all, those risks should be regulated. Scholars historically have tended to think of systemic risk primarily in terms of financial institutions such as banks. However, with the growth of disintermediation, in which companies can access capital-market funding without going through banks or other intermediary institutions, greater focus should be devoted to financial markets and the relationship between markets and institutions. This perspective reveals that systemic risk results from a type of tragedy of the commons in which market participants lack sufficient incentive, absent regulation, to limit risk-taking in order to reduce the systemic danger to others. Law, therefore, has a role in reducing systemic risk

Regulating Systemic Risk

The failure to spot emerging systemic risk and prevent the current global financial crisis warrants a reexamination of the approach taken so far to crisis prevention. The paper argues that financial crises can be prevented, as they build up over time due to policy mistakes and eventually erupt in slow motion. While one cannot predict the precise timing of crises, one can avert them by identifying and dealing with sources of instability. For this purpose, policymakers need to strengthen top-down macroprudential supervision, complemented by bottom-up microprudential supervision. The paper explores such a strategy and the institutional setting required to implement it at the national level. Given that the recent regulatory reforms that have been undertaken to address systemic risks are inadequate to prevent and combat future crises, the paper argues that national measures to promote financial stability are crucial and that the Westphalian principles governing international financial oversight should be rejected. The paper proposes that while an effective national systemic regulator should be established, strong international cooperation is indispensable for financial stability.systemic risk, global financial crisis, macroprudential supervision, microprudential supervision, regulatory reform

Measuring systemic risk

We present a simple model of systemic risk and show how each financial institution’s contribution to systemic risk can be measured and priced. An institution’s contribution, denoted systemic expected shortfall (SES), is its propensity to be undercapitalized when the system as a whole is undercapitalized, which increases in its leverage, volatility, correlation, and tail-dependence. Institutions internalize their externality if they are “taxed” based on their SES. Through several examples, we demonstrate empirically the ability of components of SES to predict emerging systemic risk during the nancial crisis of 2007-2009.Systemic risk ; Risk