Modeling Your Stress Away
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1 Modeling Your Stress Away Friederike Niepmann and Viktors Stebunovs Federal Reserve Board May 30, 2018 Any opinions and conclusions expressed herein are those of the authors and do not necessarily represent the views of the the Federal Reserve Board, or anyone affiliated with the Federal Reserve System. Niepmann and Stebunovs (FRB) Stress Tests 1 / 17
2 This paper EBA stress tests have been subject to criticism Worst banks perform the best, especially in the 2011 test (Acharya et al. (2013)) Build on work by Phillipon, Pessarossi, and Camara (2017) Analyze the 2011 and 2014 tests Focus on credit risk captured by bank-own loss models Conclude that EBA stress tests are informative and not biased Here, apply similar methodology to compare the 2014 and 2016 tests Contribute to the literature on biases in stress tests and interal risk models and information production through stress tests Decompose changes in credit losses from one edition to the other into changes from exposure, scenario, and model changes Explore factors driving exposure, scenario, and model changes Niepmann and Stebunovs (FRB) Stress Tests 2 / 17
3 Highlights of findings Models are systematically adjusted to lower credit losses in stress tests Banks that would have seen credit losses increase the most due to exposure and scenario changes saw the strongest decreases in credit losses from model changes in 2016 Model adjustments do not reflect changes in the riskiness of credit portfolios Model adjustments were most evident for banks that use the Internal Risk Based approach and that have more realistic model performance Stress tests that rely on bank-own models appear to have a significant deficiency, with material implications for investors, supervisors, and financial stability Niepmann and Stebunovs (FRB) Stress Tests 3 / 17
4 Philippon et al. (2017) s model Back out bank-specific models that map macro factors into credit risk losses First step: Estimate the macro factor F p jt = ˆθ p j y jt log l p ijt (1 l p ijt ) = αp i + θ p j y jt + ɛ p ijt, (1) where l p ijt is the impairment rate of bank i in forecast year t on portfolio p in country j; y jt is a triple of inflation, GDP growth, and unemployment; p {retail, corporate} Second step: Estimate the bank-specific loss model {α p i, βp i } log l p ijt (1 l p ijt ) = αp i + β p i F p jt + ɛp ijt. (2) Niepmann and Stebunovs (FRB) Stress Tests 4 / 17
5 Model,sample, and outputs Model Sample Outputs Checks for alternative specifications and for inclusion of more scenario variables Separate estimation for the 2014 and 2016 tests, covering 50 banks in about 25 countries Average β p i close to 1 by construction but significant variation Good fit of both 2014 and 2016 models (R 2 s range from 0.6 to 0.7) Macroeconomic factors are a key driver of loss rates, bank idiosyncrasies are also relevant Good predictive power of both 2014 and 2016 models over the period Niepmann and Stebunovs (FRB) Stress Tests 5 / 17
6 Finding: Changes in exposures led to lower losses Table shows hypothetical loss rates for different combinations of exposures, scenarios and models model/scenario/exposure (1) (2) (3) (4) m16/s16/e16 m16/s16/e14 m16/s14/e16 m16/s14/e14 adverse 178, , , ,484 baseline 102, , , ,433 m14/s14/e14 m14/s14/e16 m14/s16/e14 m14/s16/e16 adverse 253, , , ,138 baseline 124, , , ,679 mb16/sf14/e14 mb14/sf16/e16 adverse 212, ,237 Niepmann and Stebunovs (FRB) Stress Tests 6 / 17
7 Finding: Adverse scenario was less severe in 2016 Adverse scenario was less severe both in absolute an in relative terms. model/scenario/exposure (1) (2) (3) (4) m16/s16/e16 m16/s16/e14 m16/s14/e16 m16/s14/e14 adverse 178, , , ,484 baseline 102, , , ,433 m14/s14/e14 m14/s14/e16 m14/s16/e14 m14/s16/e16 adverse 253, , , ,138 baseline 124, , , ,679 mb16/sf14/e14 mb14/sf16/e16 adverse 212, ,237 Niepmann and Stebunovs (FRB) Stress Tests 7 / 17
8 Finding: Models were tailored to each stress test edition Each model produces the lowest losses given the exposures and scenarios that applied in the corresponding stress test edition. If 2014 model would have been used for 2016 adverse scenario and exposures, aggregate credit losses would have been 20 percent higher. model/scenario/exposure (1) (2) (3) (4) m16/s16/e16 m16/s16/e14 m16/s14/e16 m16/s14/e14 adverse 178, , , ,484 baseline 102, , , ,433 m14/s14/e14 m14/s14/e16 m14/s16/e14 m14/s16/e16 adverse 253, , , ,138 baseline 124, , , ,679 mb16/sf14/e14 mb14/sf16/e16 adverse 212, ,237 Niepmann and Stebunovs (FRB) Stress Tests 8 / 17
9 Scenario and exposure changes Banks cannot adjust exposures to scenarios because exposures are fixed before scenarios are known No sign that scenarios were biased: They did not lower losses for weaker banks nor for banks with higher 2016 losses because of changes in exposures (1) (2) (3) (4) (5) (6) VARIABLES Si 14 Si 16 Si 14 Si 16 ESi 14 ESi 16 Ei (0.142) Ei ** (0.135) Capital buffer * ( ) ( ) ( ) ( ) Constant *** * (0.0282) (0.0822) (0.0700) (0.142) (0.0978) (0.175) Observations R-squared Niepmann and Stebunovs (FRB) Stress Tests 9 / 17
10 Systematic model adjustments Plot changes in losses because of exposure and scenarios changes against changes in losses because of model changes Banks with a larger increase in losses because of scenario and exposure changes saw losses decrease more due to model changes Niepmann and Stebunovs (FRB) Stress Tests 10 / 17
11 No correlation of model changes with changes in risk Correlation between exposure and scenario changes and model changes is highly significant No correlation with the change in riskiness of portfolios proxied by the change in the share of non-performing exposures from 2013:Q4 to 2015:Q4 If 2014 model had been used in 2016, losses would have been higher by 2.8 percent of a bank s CET1 capital on average in adverse scenario (1) (2) (3) (4) (5) VARIABLES Mi 14 Mi 16 Mi 14 Mi 14 Mi 14 ESi *** ** *** (0.229) (0.274) (0.201) NPE i (0.0351) adj. NPE i (4.430) (3.416) Constant ** 0.163* 0.175* (0.0833) (0.0741) (0.0944) (0.100) (0.0858) Observations R-squared Niepmann and Stebunovs (FRB) Stress Tests 11 / 17
12 Two factors that might have obscured model changes Higher increase in losses from scenario and exposure changes for banks 1) with larger share of exposures subject to IRB approach and 2) with more realistic models Share of IRB exposure Average forecast error Niepmann and Stebunovs (FRB) Stress Tests 12 / 17
13 Model changes went in the right direction Controlling for exposure and scenario changes, banks with larger share of exposures subject to the IRB approach saw larger model adjustments Credit losses increased more for banks for which model under-predicted loss rates more: Model changes went in the right direction (1) (2) (3) (4) (5) (6) (7) ESj 14 ESj 14 ESj 14 Mj 14 Mj 14 Mj 14 Mj 14 IRB share ** ** ( ) ( ) (0.0111) (0.0103) MP 14 i 5.932** 4.377*** ** ES 14 j (2.242) (1.306) (3.439) (3.790) *** ** (0.342) (0.312) Constant ** *** * (0.439) (0.0944) (0.377) (1.000) (0.126) (0.0907) (0.949) Observations R-squared Niepmann and Stebunovs (FRB) Stress Tests 13 / 17
14 Larger model improvements for weaker banks Evidence that weaker banks saw more pressure to improve models Recall: scenario and exposure changes uncorrelated with capital buffers Bottom line: Models improved overall, with bigger improvements for weaker banks despite systematic model adjustments Niepmann and Stebunovs (FRB) Stress Tests 14 / 17
15 Market response to the 2016 test release Abnormal stock price and CDS spread changes on the first two days after the publication of the stress test results (Aug 1-2, 2017) Niepmann and Stebunovs (FRB) Stress Tests 15 / 17
16 Response implies the anticipation of lower capital requirements Test results were to inform 2017 bank-specific capital requirements Response is consistent with lower anticipated requirements due to decrease in losses from model changes No indication that changes in losses from model changes were related to changes in risk: Lower credit losses are associated with an increase in CDS spreads, with a stronger increase for weaker banks stock price CDS CDS (1) (2) (3) Mi * (0.950) (0.0116) (0.0240) Capital buffer ( ) Mi 16 cap buf * ( ) Constant * *** *** (0.744) (0.0124) ( ) Observations R-squared Niepmann and Stebunovs (FRB) Stress Tests 16 / 17
17 Summary Evidence that credit loss models were systematically adjusted to reduce losses/smooth losses from 2014 to 2016 stress tests Magnitude of the adjustments quantitatively significant Difficult to detect because model changes went in the right direction, that is, model performance improved overall in 2016 Stress tests that rely on bank-own models appear to have a significant deficiency, with material implications for investors, supervisors, and financial stability Niepmann and Stebunovs (FRB) Stress Tests 17 / 17
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