Systemic Risk Glossary

We briefly present below the definitions of some of the most important systemic risk measures (and related concepts) developed by the current literature. For every definition, you will find a link to the related paper. Moreover, for some of the measures described below, you will also find a link to access databases, codes and programs through the RunMyCode website, which is an online repository allowing researchers to share codes and data associated with scientific publications (articles and working papers).

    Systemic Risk Elementary Notions

  • Systemic Risk

    Wikipedia, the free encyclopedia

    In finance, systemic risk is the risk of collapse of an entire financial system or entire market, as opposed to risk associated with ...

  • Systemic Risk


    The possibility that an event at the company level could trigger severe instability or collapse an entire industry or economy ...

  • Systemic Risk

    Financial Times Lexicon

    The risk of an adverse change in the financial system as a whole, which would affect all ...

  • Systemic Risk

    Merriam-Webster, an Encyclopedia Britannica Company

    The risk that the failure of one financial institution (as a bank) could cause other interconnected institutions to fail and harm ...

  • Systemic Risk

    Moneyterms, investment and finance explained

    Systemic risk is risk that is posed to the financial system or the economy, as opposed to risk that is faced by an ...

  • Systemic Risk


    General: Probability of loss or failure common to all members of a class or group or to an entire system. Erroneously ...

    Systemic Risk Measures

  • Component Expected Shortfall (CES)

    The Component Expected Shortfall measures the absolute contribution of a firm to the risk of the financial system. Formally, CES corresponds to the product of Marginal Expected Shortfall (see definition below) and the weight of the institution in the financial system that can be defined for example as the relative market capitalization. CES allows decomposing the risk of the aggregate financial system (measured by ES) according to the institutions therein.

    Banulescu G.-D. and E.-I. Dumitrescu, (2012), “How to Identify the SIFI? A Component Expected Shortfall (CES) Approach to Systemic Risk”, forthcoming in Journal of Banking and Finance, 30 pages.
    Abstract / Where to find it / RunMyCode
  • Conditional Value-at-Risk (CoVaR)

    This measure corresponds to the value at risk (VaR) of the financial system conditional on institutions being under distress. Here, an institution's contribution to systemic risk is defined as the difference between CoVaR conditional on the institution being under distress and the CoVaR in the median state of the institution.

    Adrian T. and M. Brunnermeier, (2011), “CoVaR”, Working Paper #17454, National Bureau of Statistics, 43 pages.
    Abstract / Where to find it
  • Corrected-Conditional Value-at-Risk (Co-CoVaR)

    The Co-CoVaR is a model-risk corrected version of the CoVaR measure (see definition above). The CoVaR heavily relies on quantile estimates, it is very sensitive of errors in the measure of extreme quantiles and might be largely impacted by mild mis-measurements of extreme risk of financial companies. Therefore, model-risk should be taken into account when calculating these types of measures.

    Boucher C., P. Kouontchou, B. Maillet and O. Scaillet, (2013), “The Co-CoVaR and some other Fair Systemic Risk Measures with Model Risk Corrections”, Work in progress, 40 pages.
    Abstract / Where to find it
  • Long Run Marginal Expected Shortfall (LRMES)

    This is an extension of the Marginal Expected Shortfall. Here, the most pessimistic scenarios for the market return are treated as Crisis scenarios. Whenever the market index falls by 40% over the next six months, this is viewed as a crisis. For these scenarios, the expected loss of equity value of firm i is called the Long Run Marginal Expected Shortfall or LRMES. This is just the average of the fractional returns of the firm’s equity in the crisis scenarios.

    Acharya V., C. Brownlees, R. Engle, F. Farazmand and M. Richardson, (2013), “Measuring Systemic Risk”, in Managing and Measuring Risk: Emerging Global Standards and Regulation After the Financial Crisis, Roggi-Altman (eds.), World Scientific Series in Finance.
    Abstract / Where to find it
  • Marginal Expected Shortfall (MES)

    The Marginal Expected Shortfall measures a firm’s expected equity loss when market falls below a certain threshold over a given horizon. MES is simple to compute and therefore easy for regulators to consider. It can be calculated as the average return of a firm during the x% worst days for the market. MES and leverage are able to predict a firm's contribution to a crisis.

    Acharya V., C. Brownlees, R. Engle, F. Farazmand and M. Richardson, (2013), “Measuring Systemic Risk”, in Managing and Measuring Risk: Emerging Global Standards and Regulation After the Financial Crisis, Roggi-Altman (eds.), World Scientific Series in Finance.
    Abstract / Where to find it
  • Systemic RISK (SRISK)

    This is an extension of the MES measure in order to take into account both the liability and the size of financial institutions. It corresponds to the expected capital shortfall of a given financial institution, conditional on a crisis affecting the whole financial system. In other words, it is the capital that a firm is expected to need if we have another financial crisis.

    Acharya V., R. Engle and M. Richardson, (2012), “Capital Shortfall: A New Approach to Ranking and Regulating Systemic Risks”, American Economic Review, 102(3), 59-64.
    Abstract / Where to find it
  • Systemic RISK with Structural GARCH

    Recently, Engle and Siriwardane (2014) proposed a new model of volatility where financial leverage amplifies equity volatility by what they call the “leverage multiplier”. The exact specification of the model is motivated by standard structural models of credit; whilst its parametrization is flexible and accurate enough to capture environments where the firm’s asset volatility is stochastic, asset returns can jump, and asset shocks are non-normal. A direct application is encapsulated in a new systemic risk measure (SRISK), in which the core model is the Structural GARCH (SGARCH) model.

    Engle R., E. Siriwardane, (2014), “Structural GARCH: The Volatility-Leverage Connection”, Working Paper, 65 pages.
    Abstract / Where to find it

    Related Concepts

  • Coherent Risk Measures

    A coherent risk measure is a function ρ that satisfies properties of monotonicity, sub-additivity, homogeneity, and translational invariance. For instance, it is well known that Value-at-Risk is not, in general, a coherent risk measure because it does not respect the sub-additivity property. As a consequence, Value-at-Risk may discourage diversification.

    Artzner P., F. Delbaen, J.-M. Eber and D. Heath, (1999), “Coherent Measures of Risk”, Mathematical Finance 9(3), 203-228.
    Abstract / Where to find it
  • Dynamic Conditional Beta

    It is an extension of the Marginal Expected Shortfall developed by Brownlees and Engle (2010) to the case with several factors explaining the dynamic of financial firms' return and with asynchronicity of the time zones. This model combines a DCC model to estimate the dynamic of the beta parameters, univariate GARCH models to estimate the dynamic of the volatility of the error terms, and a dynamic t copula to estimate the dynamic of the dependence structure between the innovations.

    Engle R., E. Jondeau and M. Rockinger, (2014), “Systemic Risk in Europe”, forthcoming in Review of Finance, 55 pages.
    Abstract / Where to find it
  • Expected Shortfall (ES)

    The Expected Shortfall at the (1-α%) level is the expected return in the worst α% of the cases. The Expected Shortfall takes into account the losses beyond the VaR thus letting know how important the loss would be in case it surpasses the VaR. ES is a coherent risk measure as it satisfies the properties of monotonicity, sub-additivity, homogeneity, and translational invariance.

    Acerbi C. and D. Tasche, (2001), “Expected Shortfall: A Natural Coherent Alternative to Value-at-Risk”, Economic Notes, 31(2), 379-388.
    Cover / Where to find it
  • Principal Component Analysis (PCA)

    Mathematical procedure that uses an orthogonal transformation to convert a set of observations of possibly correlated variables into a set of values of linearly uncorrelated variables called principal components. This measure can identify and quantify financial crisis periods, and seems to contain predictive power in out-of-sample tests.

    Billio M., M. Getmansky, A. Lo and L. Pelizzon, (2012), “Econometric Measures of Connectedness and Systemic Risk in the Finance and Insurance Sector”, Journal of Financial Economics, 104(3), 535-559.
    Abstract / Where to find it
  • Value-at-Risk (VaR)

    Risk measure that gives the worst potential loss of an asset i, at a given date, for some defined frequency and horizon, written for a probability threshold alpha. VaR has four main uses in finance: risk management, financial control, financial reporting and computing regulatory capital. Since its original publication, value-at-risk has become the industry standard in risk management.

    Jorion P., (2006), Value-at-Risk: The New Benchmark for Managing Financial Risk. McGraw-Hill, 600 pages.
    Cover / Where to find it