Abstract
Regulators’ stress tests on banks further stimulated an academic debate over systemic risk measures and their predictive content. Focusing on marked based measures, Acharya et al. (Rev Financ Stud 30(1):2–47, 2017) provide a theoretical background to use marginal expected shortfall (MES) for predicting the stress test results, and verify it on the 2009 Supervisory Capital Assessment Program of the US banking system. The aim of this paper is to further test the goodness of MES as a predictive measure, by analysing it in relation to the results of the 2014 European stress tests exercise conducted by the European Banking Authority. Our results underscore the importance of choosing the appropriate index to capture the systemic distress event. In fact MES based on a global market index does not show association with the stress test results, in contrast to Financial MES, which is based on a financial market index, and has a significant information and predictive power.
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Notes
In this work we focus on MES and CoVaR but other market-based measures of systemic risk have been recently developed: for example, Black et al. (2016) calculate the Distress Insurance Premium (DIP) for European banks.
NYU Stern School of Business provides a daily updated estimation of SRISK for US financial institutions (http://vlab.stern.nyu.edu/welcome/risk).
The associated econometric model is developed in Brownless and Engle (2012).
Bongini et al. (2015) developed an event study on the impact of the publication of the list of systemically important banks on market prices.
As suggested in Acharya et al. (2012), market-based measures of systemic risk could also be used to set minimum capital requirements. However, some authors show some scepticism on the use of these measures by regulators. For example, Daniellson et al. (2016), based on a model of regulator’s optimal policy choice, show that a systemic risk measure in order to be useful for regulators should have a degree of reliability far higher than currently available measures such as CoVaR and MES.
See www.eba.europa.eu for details on scenarios.
We excluded banks for which daily returns are zero for more than 25% of the dates considered, which resulted in excluding from the sample the following banks: Alpha Bank, Bank of Cyprus, Bank of Valletta, Dexia NV, Hellenic Bank, Lloyd Banking Group plc, Nova Kreditna Banka Maribor, OsterreichischeVolksbanken AG, Permanent tsb.
For a robustness check we also performed a probit regression obtaining the same results.
Further we believe that using Leverage as an explanatory variable is not appropriate when the dependent is the loss rate given it is defined over total asset.
Acharya and Steffen (2014a) compare SRISK estimates both to capital shortfall and total losses from EBA 2014. While there is no correlation with capital shortfall, there is a positive correlation with total losses suggesting that inconsistencies come from the capital ratio used. Our analysis differs in two ways: first we use a binary variable for undercapitalization instead of the absolute size of capital shortfall; second we compare MES and loss rates which are measures size- independent.
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Acknowledgements
The authors are very grateful to Simone Spadacini for excellent research assistance. The authors would like to thank for helpful comments and suggestions an anonymous referee, the Editor, and participants to a seminar at the Department of Economics and Management of the University of Pavia and the XVIII Workshop on Quantitative Finance. Usual caveat apply.
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Appendices
Appendix 1: List of banks tested by EBA and quoted
The table below summarizes the results of the EBA stress test (as from www.eba.europa.eu) on the banks in our sample. The Capital Shortfall is the difference between two components taken from the published EBA results: the required 5.5% capital required under the adverse scenario and the stressed capital. The variable is set to zero if this difference is negative. CETIER1 is the initial capital (Common Equity Tier 1 as from 31/12/2013) taken from the published EBA results. Total Loss is the sum of three components taken from the EBA published results: losses on the trading book and the banking book in the adverse scenario plus valuation losses due to sovereign shock. Quantities are expressed in Mln EUR.
Bank | Country | Capital shortfall | CETIER1 | Total loss |
|---|---|---|---|---|
Aareal Bank AG | Germany | 0 | 2.187 | 398 |
Allied Irish Banks | Ireland | 0 | 8.923 | 4.487 |
Banca Carige SpA | Italy | 1.830 | 898 | 2.085 |
Monte dei Paschi di Siena SpA | Italy | 4.250 | 5.687 | 10.327 |
Banca Popolare dell’Emilia Romagna | Italy | 130 | 3.644 | 2.912 |
Banca Popolare di Milano | Italy | 680 | 2.988 | 1.964 |
Banca Popolare di Sondrio | Italy | 320 | 1.740 | 2.019 |
Banco Bilbao Vizcaya Argentaria SA | Spain | 0 | 36.383 | 18.695 |
Banco BPI | Portugal | 0 | 3.291 | 1.256 |
Banco Commercial Portugues | Portugal | 1.140 | 4.667 | 3.426 |
Banco de Sabadell SA | Spain | 0 | 8.217 | 4.629 |
Banco Popolare | Italy | 690 | 4.234 | 5.972 |
Banco Popular Espanol SA | Spain | 0 | 8.481 | 5.643 |
Banco Santander SA | Spain | 0 | 56.086 | 40.843 |
Bank of Ireland | Ireland | 0 | 6.549 | 4.327 |
Bankinter SA | Spain | 0 | 2.781 | 1.642 |
Barclays Bank plc | UK | 0 | 48.248 | 23.359 |
Bnp Paribas | France | 0 | 65.508 | 32.692 |
Commerzbank AG | Germany | 0 | 23.523 | 10.106 |
Credito Emiliano SpA | Italy | 0 | 1.756 | 670 |
Danske Bank | Denmark | 0 | 16.463 | 7.443 |
Deutsche Bank AG | Germany | 0 | 47.312 | 15.199 |
DNB Bank Group ASA | Norway | 0 | 13.683 | 3.664 |
Erste Group AG | Austria | 0 | 10.173 | 8.572 |
Eurobank Ergasias | Greece | 4.600 | 2.979 | 5.386 |
Group Credite Agricole | France | 0 | 58.831 | 27.574 |
HSBB Holdings plc | UK | 0 | 94.725 | 43.947 |
IKB Deutsche Industriebank AG | Germany | 0 | 1.295 | 440 |
ING Bank NV | Netherlands | 0 | 30.137 | 12.449 |
Intesa Sanpaolo SpA | Italy | 0 | 33.333 | 23.045 |
Jyske Bank | Denmark | 0 | 2.264 | 1.119 |
KBC Group NV | Belgium | 0 | 11.777 | 6.119 |
Mediobanca | Italy | 0 | 4.272 | 3.572 |
National Bank of Greece | Greece | 3.430 | 4.262 | 7.857 |
Nordea Bank AB | Sweden | 0 | 22.244 | 9.273 |
OTP Bank Ltd | Hungary | 0 | 3.894 | 3.639 |
Piraeus Bank | Greece | 660 | 5.959 | 4.422 |
Royal Bank of Scotland Group plc | UK | 0 | 44.104 | 24.460 |
Societe Generale | France | 0 | 366.333 | 19.261 |
Svenska Handelsbanken AB | Sweden | 0 | 10.027 | 2.038 |
Swedbank AB | Sweden | 0 | 8.890 | 2.106 |
Sydbank AB | Denmark | 0 | 1.307 | 639 |
Unicredit SpA | Italy | 0 | 39.164 | 28.125 |
Unione di Banche Italiane | Italy | 0 | 7.526 | 7.633 |
Appendix 2: Definition of the variables used in the empirical analysis
Variable | Definition | Source original data |
|---|---|---|
ES | Expected shortfall over the 5% percentile | Datastream (returns) |
MES | Marginal expected shortfall calculated with respect to the MSCI Europe Index over the 5% percent | Datastream (returns) |
F-MES | Marginal expected shortfall calculated with respect to the MSCI Europe Banks Index over the 5% percent | Datastream (returns) |
LEVERAGE | Total Assets over Book Value of Equity | Datastream (returns) |
DEF | Binary variable with value 1 when the capital under stress is below the required level | EBA |
LOSS_RATE | Total loss under stress over total assets | EBA |
LOSS_CAP | Total loss under stress over initial capital | EBA |
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Pederzoli, C., Torricelli, C. Systemic risk measures and macroprudential stress tests: an assessment over the 2014 EBA exercise. Ann Finance 13, 237–251 (2017). https://doi.org/10.1007/s10436-017-0294-z
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DOI: https://doi.org/10.1007/s10436-017-0294-z