A Service of Leibniz-Informationszentrum econstor Wirtschaft Leibniz Information Centre Make Your Publications Visible. zbw for Economics Sum, Katarzyna Article A review of individual and systemic risk measures in terms of applicability for banking regulations Contemporary Economics Provided in Cooperation with: University of Finance and Management, Warsaw Suggested Citation: Sum, Katarzyna (2016) : A review of individual and systemic risk measures in terms of applicability for banking regulations, Contemporary Economics, ISSN 2084-0845, Vizja Press & IT, Warsaw, Vol. 10, Iss. 1, pp. 71-82, http://dx.doi.org/10.5709/ce.1897-9254.199 This Version is available at: http://hdl.handle.net/10419/162076 Standard-Nutzungsbedingungen: Terms of use: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Documents in EconStor may be saved and copied for your Zwecken und zum Privatgebrauch gespeichert und kopiert werden. personal and scholarly purposes. 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A discussion of the features of the respective measures allows us to draw con- clusions for banking regulations based on the analyzed models and to present the main challenges for regulators in terms of bank risk measurement. The study shows that substantial challenges for regulators include compensating for the drawbacks of the Value at Risk (VaR) and expected shortfall risk models, resolving the pro-cyclicality in risk modeling, improving the techniques of stress testing, and addressing the fallacy of composition in banking (i.e., to model risk from a systemic point of view and not only from the perspective of an individual bank). As the discussion concerning proper risk measurement in regulatory frameworks, such as the Basel Accord or the European Banking Author- ity’s (EBA) rules is in progress, the topic seems to be of particular importance; moreover, measures of systemic risk are not yet a subject of regulation. KEY WORDS: bank risk, risk measures, risk models, systemic risk JEL Classification: G2, F3 1 Warsaw School of Economics - Chair of International Finance, Poland Introduction has contributed to incorrect risk measurement. Bank- For decades, bank risk modeling has been a challenge ing regulations derived from these models focus solely for managers of financial institutions and regulating on individual bank risk without regard to the fallacy of authorities. The latest financial crisis has unleashed composition problem; that is, even if individual banks substantial weaknesses of the risk models used by na- function well, the banking system can fail. Hence, the tional supervisors and the Basel Accords. These mod- focus of post-crisis regulations has shifted from a mi- els are too arbitrary to evaluate the risk profiles of large cro-prudential approach of banking supervision to banks. As such, they are prone to pro-cyclicality, which a macro-prudential one. While measuring individual bank risk is a com- Correspondence concerning this article should be addressed to: plex process in itself, the difficulties in estimation and Katarzyna Sum, Warsaw School of Economics - Chair of Interna- modeling increase dramatically when one takes sys- tional Finance, ul. Madalińskiego 6/8, 02-513 Warszawa, Poland. temic bank risk into account. This study is aimed at T: 22 5649365. E-mail: [email protected] presenting the evolution and critique of risk measures www.ce.vizja.pl Vizja Press&IT 72 Vol. 10 Issue 1 2016 71-82 Katarzyna Sum in banking, with a special focus on the dynamically de- aimed at preventing a liquidity crisis triggered by a loss veloping area of systemic risk measurement. The dis- in the case of a low probability event. The term Value cussion of the features of each measure allows to draw at Risk was first used officially by J. P. Morgan in 1995, conclusions for banking regulations that are based on referencing their RiskMetrics database, which enables the mentioned models and to present the main chal- public access to data on the variances of and covari- lenges for regulators in terms of bank risk measure- ances across various asset classes. It has been used by ment. Because the measures capable of counteracting banks and regulators over the last two decades (Damo- systemic risk are currently not a subject of regulation, daran, 2010). this topic seems to be of particular importance. While there is no definition of how to compute VaR, The paper is structured as follows: the first section its estimation requires assumptions about the profit reviews the individual bank risk measures, the second and loss density function. To estimate the potential presents the risk models based on the mentioned mea- loss on a portfolio, the probability distributions of sures, and the third section focuses on systemic risk individual risks, as well as their correlation and their measurement. effect on the value of the potential loss, have to be de- fined, resulting in arbitrary risk modeling. Three solu- Review of risk measures tions are applied to address this problem: estimation Banking risk includes credit risk, market risk (i.e., the based on past values of the parameters, Monte Carlo risk of price changes), and operational risk (i.e., related methods, which require an assumption about the dis- to the banks general activity). The measurement and tribution of the portfolio values, and analytic methods, quantification of the various risk types has been a sub- which are based on assumptions about the return dis- stantial challenge for academics, bank managers, and tribution parameters (Damodaran, 2010). regulators. The VaR measure has many drawbacks. First, it as- In most cases, risk measures are defined as func- sumes a normal distribution of the values without ac- tions of random variables, such as portfolio losses or counting for fat tails. Thus, despite this fact the VaR returns. These types of measures require an arbitrary concept is regarded as the worst case scenario, regu- choice of the time horizon over which one analyses the lators and managers cannot focus on extreme events, losses and returns, as well as the assumed probability which may result in excessive risk taking. Second, distribution (Basel Committee for Banking Supervi- even if the assumption of the distribution is correct, sion [BCBS], 2011). In contrast, risk measures that another problem is the non-stationarity of variances provide general information about the bank’s financial and covariances across assets (BCBS, 2011). Third, the soundness (e.g., the z-score) are based on accounting classical VaR assumes a linear relationship between information and do not require assumptions about risk and portfolio positions, which does not hold the distribution of losses. Determining which risk when derivate instruments are included in the port- measurement to use depends on a number of factors, folio. Finally, another drawback is the narrow focus such as the simplicity of calculation, the mathemati- on market risk. cal properties of the measure, the ability to attribute Prior to the recent financial crisis, the use of VaR risk to individual portfolio components, the ability to models contributed to the build-up of risk in the bank- backtest, and the objectives of regulators/managers ing sector, as the models gave managers and regulators (BCBS, 2011). a false sense of security. A crucial criticism is that while The most common measure of bank risk is the Value VaR is the worst case scenario, it does not account for at Risk (VaR). The theoretical foundations for VaR are situations where the loss exceeds the VaR. The concept based on the portfolio theory of Markowitz. VaR is does not offer any solutions beyond the threshold of defined as the maximum value of the loss on a given VaR, nor are there any implications regarding the dis- portfolio for an assumed probability of the loss over tribution of losses beyond the VaR level. A possible a specific time horizon. In other words, VaR defines solution to this problem is the concept of Conditional how much money an institution should set aside to Value at risk (CVaR), also known as expected shortfall. counteract a predicted loss. The computation of VaR is The expected shortfall concept has substantial theoret- CONTEMPORARY ECONOMICS DOI: 10.5709/ce.1897-9254.199 A review of individual and systemic risk measures in terms of applicability for banking regulations 73 Table 1. VaR Expected Shortfall/ CVaR Abstracts from extreme events Accounts for extreme events Inferior mathematical properties Superior mathematical properties Backtesting possibility No possibility of backtesting Robust to estimation errors Prone to estimation errors- accuracy of tail modeling is crucial ical foundations (Acerbi & Tasche, 2002; Rockafellar & to backtest the model. In contrast to the VaR concept, Uryasev, 2002; Yamai & Yoshiba, 2005). Its popularity expected shortfall forecasts cannot be verified through among bank managers and regulators has increased.