Systemic Risk Measures Research Articles

Is Size Everything?

We examine sources of systemic risk (threshold size, complexity, and interconnectedness) with factors constructed from equity returns of large financial firms, after accounting for standard risk factors. From the factor loadings and factor returns, we estimate the implicit government subsidy for each systemic risk measure, and find that, from 1963 to 2006, only our big-versus-huge threshold size factor, TSIZE, implies a positive implicit subsidy on average. Further, pre-2007 TSIZE-implied subsidies predict the Federal Reserve’s liquidity facility loans and the Treasury’s TARP loans during the crisis, both in the time series and the cross section. TSIZE-implied subsidies increase around the bailout of Continental Illinois in 1984 and the Gramm-Leach-Bliley Act of 1999, as well as around changes in Fitch Support Ratings indicating higher probability of government support. Since 2007, however, the relative share of TSIZE-implied subsidies falls, especially after Lehman’s failure, whereas complexity and interconnectedness-implied subsidies are substantial, resulting in an almost sevenfold increase in total implicit subsidies. The results, which survive a variety of robustness checks, indicate that the market’s perception of the sources of systemic risk changes over time.

Systemic risk and the organization of the financial system: overview

PurposeBefore providing an overview of the conference with the above title and this Special Issue, this paper aims to present a view of the meaning of systemic risk, factors that affect systemic risk and measures of systemic risk. Thereafter, the conference presentations and the papers in this issue are summarized.Design/methodology/approachCharacteristics and measures of systemic risk are reviewed. Conference papers and presentations are summarized.FindingsWhile some aspects of systemic risk of a financial institution can be measured, an important aspect associated with contagion through markets is not easily captured by simple measures.Originality/valueThe conference and the papers in this issue contribute to the policy debate about sources and characteristics of systemic risk.

Correlation between the 2014 EU-wide stress tests and the market-based measures of systemic risk

In this paper, we compare the results of the European Banking Authority (EBA) stress tests conducted in 2014 with the market-based measures of capital losses, which are the marginal expected shortfall (MES), the systemic risk measure (SRISK) and the delta conditional value at risk (ΔCoVaR). These measures allow us to estimate the expected capital shortfall in the case of a crisis. The empirical analysis is performed on a sub-sample of the banks included in the stress test exercise, because only 57 European banks, in 22 countries, are publicly traded. We find that the SRISK is the best predictor of systemic risk among the 3 systemic risk measures, since it is the most correlated with the stress test results. Furthermore, we focus on the realized outcomes (realized loss, realized return and realized volatility) and compare them with the 2014 EU stress test results.

Bank Holdings and Systemic Risk

The recent financial crisis has focused attention on identifying and measuring systemic risk. In this paper, we propose a novel approach to estimate the portfolio composition of banks as function of daily interbank trades and stock returns. While banks’ assets are reported to regulators and/or the public at relatively low frequencies (e.g. quarterly or annually), our approach estimates bank asset holdings at higher frequencies which allows us to derive precise estimates of (i) portfolio concentration within each bank—a measure of diversification—and (ii) common holdings across banks—a measure of market susceptibility to propagating shocks. We find evidence that systemic risk measures derived from our approach lead, in a forecasting sense, several commonly used systemic risk indicators.

Conditional Extremes in Asymmetric Financial Markets

ABSTRACTThe global financial crisis of 2007–2009 revealed the great extent to which systemic risk can jeopardize the stability of the entire financial system. An effective methodology to quantify systemic risk is at the heart of the process of identifying the so-called systemically important financial institutions for regulatory purposes as well as to investigate key drivers of systemic contagion. The article proposes a method for dynamic forecasting of CoVaR, a popular measure of systemic risk. As a first step, we develop a semi-parametric framework using asymptotic results in the spirit of extreme value theory (EVT) to model the conditional probability distribution of a bivariate random vector given that one of the components takes on a large value, taking into account important features of financial data such as asymmetry and heavy tails. In the second step, we embed the proposed EVT method into a dynamic framework via a bivariate GARCH process. An empirical analysis is conducted to demonstrate and compare the performance of the proposed methodology relative to a very flexible fully parametric alternative.

Cross-Section of Asset Returns: Emerging Markets and Market Integration

Asset pricing in its essence is a very controversial topic. Despite numerous research papers criticising traditional approaches, such as linear factor models, practitioners as well as academics repeatedly return to the milestone models such as the Capital Asset Pricing Model (CAPM), mainly due to their attractive simplicity. This article focuses on the risk-return relationship by comparing the power of traditional and alternative asset pricing models in explaining the cross-section of asset returns. The focus is on unconditional models, commonly used among investors and equity analysts. This paper is based on the research performed by Estrada in 2004 and it extends his approach by introducing the use of GMM. The results suggest that for Emerging markets' investors should give preference to total risk measures over systematic risk measures. Within the category of systematic risk measures, downside beta proved its superiority to traditional CAPM beta. The results can be attributed to delayed integration process, partially justified by the lower FDI and portfolio investments into Emerging markets.

Systemic Risk in the Insurance Industry and Its Impact on the Economy

Systemic risk, defined as the risk that a failure of a large financial institution could lead to counterparty failures and could trigger adverse effects for the real economy, has been a priority for both academicians and policy-makers. The literature focuses on the measures and definition of systemic risk and on its impact on the financial sector with little or no attention given to the relationship between systemic risk and the real economy. In this paper, we investigate the link between the systemic risk in the insurance sector and the macro economy during the 2008 financial crisis. In particular, we test the predictive power of insurance companies’ systemic risk of macroeconomic downturns using various measures of systemic risk constructed from daily returns for 169 insurance companies between 1988 and 2011. Our findings show that insurance systemic risk forecasts macroeconomic downturns three months into the future.

Analysis of the SRISK measure and its application to the Canadian banking and insurance industries

In this paper, we analyse, modify, and apply one of the most widely used measures of systemic risk, SRISK, developed by Brownlees and Engle (in Rev Financ Stud 30:48–79, 2016). The measure is defined as the expected capital shortfall of a firm conditional on a prolonged market decline. We argue that segregated funds, also known as separate accounts in the US, should be excluded from actuarial liabilities when SRISK is calculated for insurance companies. We also demonstrate the importance of careful analysis of accounting standards when specifying the prudential capital ratio used in SRISK methodology. Based on the proposed adjustments to SRISK, we assess the systemic risk of the Canadian banking and insurance industries. It is shown that in its current implementation, the SRISK methodology substantially overestimates the systemic risk of Canadian insurance companies.

EVALUATING THE ACCURACY OF TAIL RISK FORECASTS FOR SYSTEMIC RISK MEASUREMENT

In this paper, we address how to evaluate tail risk forecasts for systemic risk (SRISK) measurement. We propose two loss functions, the Tail Tick Loss and the Tail Mean Square Error, to evaluate, respectively, Conditional Value-at-Risk (CoVaR) and MES forecasts. We then analyse CoVaR and MES forecasts for a panel of top US financial institutions between 2000 and 2012 constructed using a set of bivariate DCC-GARCH-type models. The empirical results highlight the importance of using an appropriate loss function for the evaluation of such forecasts. Among other findings, the analysis confirms that the DCC-GJR specification provides accurate predictions for both CoVaR and MES, in particular for the riskiest group of institutions in the panel (Broker-Dealers).

Measuring systemic risk of the banking industry in China: A DCC-MIDAS-t approach

This paper measures systemic risk in the Chinese banking sector by using the CoVaR approach. First, we introduce the student's t distribution into the standard DCC-MIDAS and propose a DCC-MIDAS-t model, particularly suitable for processing fat-tailed financial returns. We then apply the proposed DCC-MIDAS-t model to measure systemic risk following the idea of CoVaR. The empirical studies on the Chinese banking industry show that the DCC-MIDAS-t model is preferred in terms of the accuracy of volatility prediction and CoVaR measure. Macroeconomic information are proved to be able to improve the accuracy of systemic risk measure and M2 is most useful in comparing with IP and PPI. Several banks, such as BOC and BONB, suffered a lot from the stock market crash in June 2015 and some of them still maintain high risk spillovers during the after-crash period. Our findings suggest that these banks should be the focus of supervision even after the crash.

Ranking consistency of systemic risk measures: a simulation-based analysis in a banking network model

In a banking network model, I analyse the ranking consistency of common systemic risk measures (SRMs). In contrast to previous studies, this model-based analysis offers the advantage that the sensitivity of the ranking consistency with respect to bank and network characteristics can easily be checked. The employed network model accounts, among others, for bank insolvencies as well as illiquidities, stochastic dependencies of non-bank loans as well as of liquidity buffer assets across various banks, bank rating-dependent volumes of deposits and interbank liabilities, and the funding liquidity reducing effect of fire sales of other banks. Within the assumed banking network model, I find that, in general, the ranking consistency (measured by the rank correlation) of various SRMs is rather low. A further finding is that the ranking consistency can significantly vary in statistical terms, for example for an increasing correlation between the returns of the liquidity buffer assets across banks, an increasing volatility of these assets or an increasing default rate in the non-bank loan portfolios. However, forecasting which effect a specific change in parameters, bank behavior or network characteristics has on the ranking consistency of SRMs seems to be difficult because the sign of the effect can be different for different pairs of SRMs. Furthermore, the economic significance of these changes on the overall ranking consistency as measured by Kendall’s coefficient of concordance in general is rather low.

Du MEDAF avec risque systémique à la détermination des institutions financières d’importance systémique

Dans cet article, nous nous proposons de tester une extension du modèle d’évaluation des actifs financiers (MEDAF), dans lequel nous ajoutons un facteur de risque systémique, mesuré par une agrégation des principales mesures de risques systémiques, comme proposée récemment par différents auteurs, dans le cadre d’une analyse en composantes principales parcimonieuse (ACPP). Il ressort de nos analyses empiriques menées sur le marché américain que le risque systémique est effectivement une composante importante de la rémunération sur certains titres. Nous proposons finalement une application originale servant à l’identification et au classement des institutions financières d’importance systémique (IFIS). Classification JEL : C45, C53, C58, G01, G11.

Identifying systemic risk drivers in financial networks

In this work, we propose a methodology to decompose drivers of systemic risk that arise due to insolvency contagion in evolving financial networks. There is an ongoing discussion on how network topology and capital buffer influence systemic risk. On the one hand, the network contagion literature tends to emphasize the influence of the network topology. On the other hand, policy works tend to discuss restrictions over the capital buffers of financial institutions. Systemic risk is usually a complex function of both risk drivers and thus isolating the contributive effects of each risk driver to systemic risk is not a trivial task. Our decomposition methodology permits to identify and isolate these effects and can be used for evaluating the effectiveness of policy issues that affect either the network topology or capital buffer of banks. We apply our methodology to the global banking network and find that the network topology explains most of the systemic risk measure’s volatility. Additionally, we show that the capital buffer explains the persistent reduction in systemic risk buildup with effects concentrated around the global financial crisis. Our results attest the importance played by both risk drivers in measuring systemic risk.

A Volume-Weighted-Average-Price (VWAP) Method for Estimating Beta in the Context of Reference-Day Risk.

The ability to accurately estimate systematic risk (or beta) when reference-day risk is considered is an ineluctable requirement for all applications of the capital asset pricing model (CAPM). This research documents evidence of reference-day risk for shares on the Johannesburg All Share Index. In response to the need for greater accuracy when estimating systematic risk, this paper contributes a volume-weighted-average-price (VWAP) method for estimating beta which may be employed when reference-day risk is considered. Furthermore, this research applies a graphical time-series approach to test the underlying risk-reward tenet postulated by the CAPM. Using beta as a measure of systematic risk, this research finds that the CAPM appears to imperfectly specify the risk-reward trade-off.

Asset commonality of European banks

In this paper, we investigate the notion of asset commonality. We describe the evolution of asset commonality, for 43 European banks over 15 countries, by comparing the 2011 and 2016 EU-wide stress test reporting. We determine the main variables that influence asset commonality and its evolution. We notice that asset commonality can be used as a complementary measure to other market systemic risk measures. Furthermore, we find that asset commonality can influence the returns negatively and the volatility of the bank positively. We also find that some banks, which have no funding problems or fire sales, have experienced a decrease in their performance. Asset commonality can be seen as an interesting tool than can be used by regulators.

A unified approach to systemic risk measures via acceptance sets

AbstractWe specify a general methodological framework for systemic risk measures via multidimensional acceptance sets and aggregation functions. Existing systemic risk measures can usually be interpreted as the minimal amount of cash needed to secure the system after aggregating individual risks. In contrast, our approach also includes systemic risk measures that can be interpreted as the minimal amount of cash that secures the aggregated system by allocating capital to the single institutions before aggregating the individual risks. An important feature of our approach is the possibility of allocating cash according to the future state of the system (scenario‐dependent allocation). We also provide conditions that ensure monotonicity, convexity, or quasi‐convexity of our systemic risk measures.

Systemic risk and copula models

Systemic risk describes the phenomenon that dependency adds a specific component of risk to a system or network of (financial) institutions as a whole, which would not be present if the institutions were independent from each other. This paper introduces the concept of systemic risk measures. We describe and study its behavior as a function of the copula, which represents the loss variables of the institutions in the network. Further, we define stochastic order relations on copulas and relate them with systemic risk measures.

The market rank indicator to detect financial distress

A novel measure is introduced to forecast financial crises, which can also be seen as a supplementary measure in systemic risk analysis. The indicator (the market rank indicator MRI) considers the relation between the largest singular value of a matrix of the return time series and its k smallest singular values. The rationale behind this is that, in times of market excitation and higher correlation, the vectors of the return time series become closer in the linear space containing them. The MRI is related to the notion of condition number, a measure of how close returns are; therefore, the MRI increases in periods of market tensions. The measure is applied to selected stock market indexes and tested empirically for its sensitivity as well as against alternative measures of systemic risk. The MRI could be of interest for both regulators and speculators due to its forecasting power. The empirical analysis underlines that the proposed methodology is particularly appealing to forecast market distress and it shows a clear superiority in terms of predictive capability with respect to other existing measures.

Pitfalls in the Use of Systemic Risk Measures

We examine pitfalls in the use of return-based measures of systemic risk contributions (SRCs). For both linear and nonlinear return frameworks, assuming normal and heavy-tailed distributions, we identify nonexotic cases in which a change in a bank’s systematic risk, idiosyncratic risk, size, or contagiousness increases the risk of the system but lowers the measured SRC of the bank. Assessments based on estimated SRCs could thus produce false interpretations and incentives. We also identify potentially adverse side effects: A change in a bank’s risk structure can make the measured SRC of its competitors increase more strongly than its own.

Aggregate Tail Risk and Expected Returns

Do stocks bear a crash risk premium? We examine the empirical performance of the tail index measure from Kelly and Jiang (2014). We find that the tail index explains the cross-section of the discount rate component of returns, but not the cash-flow component. Moreover, in the time series the tail index is uncorrelated with theoretically motivated measures of aggregate uncertainty and systemic risk. In contrast, the tail index Granger causes and is Granger caused by the level of the term structure, and the slope of the term structure Granger causes tail risk. Received June 22, 2016; editorial decision December 23, 2017 by Editor Raman Uppal.