Backtesting European Stress Tests

Philippon, Thomas; Pessarossi, Pierre; Camara, Boubacar

doi:10.3386/w23083

Cited by 20 publications

(21 citation statements)

References 11 publications

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“…Since stress tests are a relatively new addition to the microprudential supervisory toolkit, the literature on stress testing is small but growing. This paper builds on recent work by Philippon et al (2017), who analyze the 2011 and 2014 EBA stress tests. The authors find that the stress tests have informational value and report no evidence for biases in the construction of the scenarios or in the estimated losses across banks of different sizes and ownership structures.…”

Section: Literaturementioning

confidence: 99%

“…To estimate the credit loss models that are run by the banks, the paper follows the methodology in Philippon et al (2017). 3 Because the EBA publishes very detailed information on individual banks' hypothetical loan loss rates, it is possible to estimate the relationship between macroeconomic variables and banks' credit losses using regression techniques.…”

Section: Introductionmentioning

confidence: 99%

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Modeling Your Stress Away

Niepmann

Stebunovs

2018

internationalfinance

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We investigate systematic changes in banks' projected credit losses between the 2014 and 2016 EBA stress tests, employing methodology from Philippon et al. (2017). We find that projected credit losses were smoothed across the tests through systematic model adjustments. Those banks whose losses would have increased the most from 2014 to 2016 due to changes in the supervisory scenarios-keeping the models constant and controlling for changes in the riskiness of underlying portfolios-saw the largest decrease in losses due to model changes. Model changes were more pronounced for banks that rely more on the Internal Ratings-Based approach, and they explain the cross-section of market responses to the release of the 2016 results. Stock prices and CDS spreads increased more for banks with larger reductions in projected credit losses due to model changes, as investors apparently did not interpret lower loan losses as reflecting mainly a decrease in credit risk but, instead, as a sign of lower capital requirements going forward.

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Section: Literaturementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Modeling Your Stress Away

Niepmann

Stebunovs

2018

internationalfinance

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“…Carboni, Fiordelisi, Ricci, and Lopes (2017) and Morgan et al (2014) investigate whether the market has largely deciphered on its own which banks are likely to fail a stress test and document a relationship between the bank's announcement day return and its stress test result performance. Philippon, Pessaross, and Camara (2017) evaluate the quality of banking stress tests in Europe by comparing predicted banks' losses to actual losses. Finally, by applying data on different US stress tests, Glasserman and Tangirala (2016) observe that stress test outcomes have become more predictable over time, while Flannery et al (2017) show that market reactions of stress tests are more pronounced for riskier institutions, for instance, banks that are more leveraged.…”

Section: Introductionmentioning

confidence: 99%

Regulatory stress testing and bank performance

Ahnert

Vogt²,

Vonhoff³

et al. 2020

Euro Fin Management

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This paper investigates the impact of stress testing results on banks’ equity and CDS performance using a large sample of 12 tests from the US CCAR and the European EBA regimes in the time period from 2010 to 2018. Passing banks experience positive abnormal equity returns and tighter CDS spreads, while failing banks show strong drops in equity prices and widening CDS spreads. We also document strong market reactions at the announcement date of the stress tests. We complement existing studies by investigating the predictability of stress test outcomes and evaluating strategic options for affected banks and investors.

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“…Second, our paper adds to the literature on backtesting risk models. While backtesting microprudential risk models is now common practice among market practitioners (e.g., Cavestany and Rodríguez (2015); Daníelsson (2011); Philippon et al (2017)), relatively little attention has been devoted to the case of macroprudential stress tests. Our methodology allows us to compare the performance of different models and thus to identify the most accurate stress test model, given some exogenous parameters.…”

Section: Introductionmentioning

confidence: 99%

Backtesting Macroprudential Stress Tests

2020

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Research Question Different macroprudential stress tests were proposed in the literature, which account for potential contagion effects in financial networks. While these models were useful for building intuition about how shocks may propagate through the system, their capability to accurately predict whether a given bank will default has not been the focus of the literature. It is well-known that different models may yield very different stress testing results. We therefore propose a backtesting framework that assesses the predictive performance of different fire-sale stress test models and allows to choose the most accurate model from a set of alternatives. Contribution We introduce a generalized fire-sale stress test model that captures a wide range of behavioral assumptions with regards to banks' liquidation dynamics under stress. The literature has proposed alternative behavioral assumptions in this regard, all of which are covered by our generalized model. We build a network of common asset holdings using public balance-sheet data for U.S. commercial banks in 2007. We then compare the model predictions with the list of actual defaults that occurred in the U.S. during the years 2008-2010. In order to assess the relative performance of these network models, we also use several alternative benchmarks. Results We identify two asset classes for which the model has predictive power, independently of the assumed liquidation dynamics. We then show how the behavioural assumption yielding the most accurate model depends on the size of the initial shock and on secondary market liquidity. We also identify, for different liquidation dynamics, the optimal number of liquidation rounds. Overall, our analysis shows that properly calibrated macroprudential stress tests can have predictive power superior to alternative benchmarks that do not account for the network of common asset holdings.

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Backtesting European Stress Tests

Cited by 20 publications

References 11 publications

Modeling Your Stress Away

Modeling Your Stress Away

Regulatory stress testing and bank performance

Backtesting Macroprudential Stress Tests

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