20211109_LargeModelST

Large model stress 

testing 

Katarzyna Budnik 

Katarzyna.Budnik@ecb.europa.eu 

09/11/2021 

Financial Stability and Stress Testing 

Online Course 

November 4-17, 2021 

www.ecb.europa.eu ©

Overview 

• Model 

• Stochastic simulations 

• Scenario design and uncertainty 

• G@R and policy assessment 

2 


What is the macro-micro model BEAST? 

• The core model supporting macro-financial 

risk and policy assessments 

• The emphasis on macro-financial interactions 

within a bank (e.g. the feedback loop between 

solvency and funding costs) and between 

banks and the real economy 

• Ingrains monetary policy transmission, 

especially via the banking channel 

• Developed and applied since 2018, with over a 

dozen unique policy projects completed to date 

• Therein, seven projects to a differing degree 

built-on or explored Growth @ Risk perspective 

• Dedicated working group (MaMi 

workstream/Financial Stability Committee) 

monitoring the development of the model 

• A semi-structural (backward-looking) model 

following the tradition of core forecasting 

models at central banks 

• Including representation of individual euro area 

economies, and individual banks making up 

60-70% of the euro area banking sector 

• Sufficiently detailed representation of banks’ 

balance sheets, profit and loss accounts to 

encapsulate economic, regulatory and 

accounting interactions 

• Empirical identification of main behavioural 

equations and heavy use of bank-level data 

from various sources 

• Long-term stability 

• A well-defined, updateable ‘forecasting’ 

model 

• Additional extensions toward scenario and (to 

an increasing degree) parameter uncertainty 

3 


Model evolution 

Kick-start 

Macroprudential 

stress test 

Impact 

assessment of 

policies 

Pushing-forward 

scenario 

analysis 

Pushing-forward 

stance 

assessment 

(interplay of risks 

and policies) 

March 2018 November 2018 June 2019 September 2020 Now 

The core model version 

with the dynamic balance 

sheet and real economybanking 

sector feedback 

loop 

Extensions specific to 

policy packages, and 

improvements in 

modelling capital risk 

charges, liability side of 

bank balance sheets, 

stochastic simulations 

Exploring further 

stochastic simulations and 

parameter uncertainty 

Macroprudential stress test 

2018 

Macroprudential stress test 

2020 

(On-going) macroprudential 

stress test 2021 

Basel III finalization 

Covid-19 mitigation policies 

NPL policies 

Capital buffer use and 

restoration 

ECB Financial Stability 

Review 

Climate stress test 

4 


BEAST* high-level overview 

19 euro area (25 EU) economies + 

Macro block: 

ECB-CONFIDENTIAL 

• (Structural) VAR representation of each 

economy (12 macro-financial variables) 

• Additional constraints: cross-border trade 

spillovers, common monetary policy 

+ 91 (101) significant euro area banks 

Bank-level block: 

• Detailed representation of a balance sheet: 

assets and liabilities 

• Detailed representation of profit and loss 

accounts e.g. interest income, funding costs 

• Reduced-form ‘sensitivity’ equations for e.g. 

IRFS9 parameters 

• Reduced-form but theory-informed 

behavioural equations e.g. lending volumes 

• Involves non-linearities and occasionally 

binding constraints 

* Budnik, K et al. (2020) “Banking euro area stress test model”, ECB Working Paper Series, 

No 2469, September 

5 


Semi-structural design 

Equations Example Identification 

Macroeconomic behavioural 

equations 

E.g. country-level GDP depends on country-level 

loan volumes to the NFPS 

Country-level structural panel VAR* with 

long-run priors** 

Macroeconomic identities 

E.g. country imports depend on exports of its 

counterparties 

Calibration on the basis of trade matrices 

Aggregating equations 

Bank-level behavioural 

equations 

E.g. country-level loan volumes as a sum of bank 

level loan volumes 

E.g. dividend distribution depends on profitability, 

economic situation, bank capitalisation, riskiness 

- 

Bank-level panel estimation: different 

methods*** incl. dynamic homogeneity where 

applicable 

Bank-level regulatory limits E.g. MDA Calibration on the basis of existing regulation 

Bank-level ‘satellite’ risk 

models 

E.g. PDs 

Bank-level panel estimation: different 

methods* 

Bank-level identities E.g. calculation of loan-loss provisions IRFS9 and other balance sheet identities 

* Jarociński, M. (2010). Responses to monetary policy shocks in the east and the west of Europe: a comparison. Journal of Applied Econometrics, 25(5), 833-868., George, E. I., Sun, D., & Ni, S. (2008). Bayesian stochastic search for VAR model 

restrictions. Journal of Econometrics, 142(1), 553-580., Waggoner, D. F., & Zha, T. (2003). A Gibbs sampler for structural vector autoregressions. Journal of Economic Dynamics and Control, 28(2), 349-366. 

** Villani, M. (2009). Steady‐state priors for vector autoregressions. Journal of Applied Econometrics, 24(4), 630-650. 

*** Exploring datasets such as SUBA (supervisory reporting), additional supervisory information from STE exercises, iBSI/iMIR, EBA/SSM stress test templates, SNL and other private data providers 

6 


Macro block 

• The dynamics of economies represented by the structural panel VAR: 

12 endogenous: 

Real Gross Domestic Product 

Unemployment rate 

CPI 

Nominal residential property prices 

Long-term nominal interest rate 

Equity price index 

Import volumes 

Export prices 

Bank lending rates 

Bank loan volumes 

Short-term money market rates 

Central bank assets 

pp 

YY ii,tt = cc ii + AA ii,jj YY ii,tt−1 + BB ii,jj XX ii,tt + ε i,t 

jj=1 

2 exogenous: 

Foreign demand 

Competitor prices 

9 structural shocks: 

Aggregate demand 

Aggregate supply 

Conventional monetary policy 

Unconventional monetary policy 

Residential housing demand 

Bond yield 

Stock price 

Credit supply 

Credit demand 

• Bayesian reduced-form estimator proposed by Jarocinski (2010) with structural shocks derived from sign and zero 

restrictions along with Arias et al. (2018) 

• Estimation sample 1999:2019 

• Long term priors support model’s convergence to ‘long-run equilibria’ by Villani, M. (2009) 

7 


Granular representation of banks’ assets 

Loans to central banks 

and public sector 

Loans to financial 

institutions 

Loans to corporates 

(secured and not secured 

by real estate) 

Loans to households 

backed by real estate 

Loans to households not 


Equity 

Securitisation 

Other 

• Three modelling granularity levels 

• Sector-level: loans to central banks, public sector, financial 

institutions 

• Sector-country level: loans to the non-financial private sector 

• Simplified dynamics 

• Modelling asset quality 

• Loans (in each portfolio) subdivided into Stage1, Stage2 and 

Stage3 (non-performing) assets 

• Endogenous transition probabilities between risk categories, 

LGDs and risk-weight parameters 

• Dynamic balance sheet 

• Empirical modelling of lending volumes 

• New loans depend on the ‘optimal’ level of outstanding loans 

and maturity structure of the loan portfolio 

8 


Banks' lending volumes 

Loan demand factors 

Dynamic 

homogeneity 

PP 

PP 

ss 

∆LLLLLLLLLL iiii,tt = αα ss ss 

pp ∆LLLLLLLLLL iiii,tt−pp + ββ ss ss 

pp ∆GGGGGG jj,tt−pp + γγ ss pp IIIIIIII jj,tt−pp + δδ ss X jj,tt + 

pp=1 

pp=1 

pp=1 

μμ ss 1 CCCCCCC ii,tt−1 − CCCCCCC ii,tt−1 + μμ ss 2 CCCCCCC ii,tt−1 − CCCCCCC ii,tt−1 II CCCCCCC ii,tt−1 − CCCCCCC ii,tt−1 + 

ss 

+ μμ ss ss 

ss 

ss 

4 NNNNNN ii,jjjj−1 II NNNNNN ii,jjjj−1 − NNNNNN ii,jjjj−5 + μμ ss 5 RRRRRR ii,tt−1 + μμ ss ss 

6 RRRRRRRRRRRRRRRRRRRR ii,tt−1 + ϵ ii,jj,tt 

μμ 3 ss NNNNNN ii,jjjj−1 

PP 

Non-linearities 

Loan supply factors 

• Dependent variable: the quarterly growth rate of loans of bank i to sector s, in country j, in time t 

• Two stage estimation to deal with data limitations 

• Loan demand factors: fixed-effect dynamic panel regression with restrictions allowing for cross-sectional 

dependence (Pesaran, 2004) and employing iBSI/iMIR dataset (2007-2020) 

• Loan supply factors: regression with counterparty-sector-time fixed effects as in Khwaja and Mian (2008) 

employing SUBA data (2014-2020) 

9 


Banks’ liabilities 

Loans to central banks 

and public sector 

Loans to financial 

institutions 

Loans to corporates 

(secured and not 

secured by real 

estate) 




not backed by real 

estate 

Equity 

Securitisation 

Other 

Own funds 

Sight deposits to corporates 

Term deposits to corporates 

Sight deposits to 

households 

Term deposits to households 

Central bank deposits 

Government deposits 

Financial sector deposits 

Financial sector securities 

Other securities 

Other 

10 

• Three types of liabilities: 

• Slow moving: non-financial private sector 

deposits (supply driven and estimated) 

• Other supply-driven: central bank and 

governments (simplified dynamic rules) 

• Fast moving wholesale funding 

• Pecking order mechanics: 

• Banks take on non-financial private sector 

deposits as predicted from empirical 

equations 

• The remaining funding gap is first covered 

by deposits from central banks and 

governments which are in limited supply 

• Last, the bank turns to wholesale funding 


Prudential policies 

• CRDIV capital buffers incl. CCyB, CCB, SRB, O-SII/G-SII 

• Pillar I and Pillar II soft and hard supervisory requirements (P2R, P2G) 

• Profit distribution restrictions 

• Regulatory changes with macroprudential relevance: 

2017Q4 

Announcement 

of Basel III 

finalization with 

the introduction 

year 2021 

2018Q1 

NPL Guidance: 

supervisory 

expectations 

for new NPLs 

2018Q3 

NPL Guidance: 

supervisory 

expectations 

for legacy 

NPLs 

2020Q1 

Release of 

Pilar II buffers 

and the frontloading 

of 

CRD5/CRR2 

changes 

2020Q2 

Postponement 

of Basel III 

finalization until 

2022 

• Modification of RW incl. floors 

• Output floor 

• GSII leverage buffer 

• Loan-loss provisioning 

• Covid-19 mitigation policies with significant impact on banks’ balance sheets*: public 

guarantees and moratoria 

* Budnik, K et al. (2021) “Policies in support of lending following the Covid-19 

pandemic”, ECB Occasional Paper, May 

11 


Stochastic simulations 

Macro block 

Bank-level block 

Parameter 

Single draws from the posterior parameter 

distribution of the panel VAR 

Uncertainty 

Random draws from the estimated (normal) 

joint distributions of equation parameters 

Scenario / 

shocks 

Sequences of draws from the structural 

shock distribution (variance-stationary or 

incl. stochastic volatility with/without an 

indicator*) 

Geometric block and wild bootstrapping 

Limited idiosyncratic shocks e.g. 

operational risk 

* The volatility is an autoregressive process which can be 

extended by the Composite Indicator of Systemic Stress (CISS). 

12 


Differing simulation properties 

13 


Macroprudential stress testing & COVID-19 

• Complementary metrics for judging the 

resilience of the banking sector 

Macroprudential stress test 2020 (and 

2021) 

• Informs about system-wide 

consequences of banks’ most likely 

decisions, possibly reducing 

coordination failures 

• Validates the calibration, phase-in and 

out of policy measures 

Communication on buffer use: Enria, A. 

(2020), “The coronavirus crisis and ECB Banking 

Supervision: taking stock and looking ahead”, The 

Supervision Blog, ECB, July 

Ex ante impact assessment of COVID-19 

measures: Budnik et al. (2021), “Policies in 

support of lending following the Covid-19 

pandemic”, ECB Occasional Paper, May 

14 


Perspective at the onset of COVID-19 crisis 

High multidimensional uncertainty 

• The banking sector entered the crisis: 

• Better capitalised than in 2008 

• After the protracted period of low 

profitability 

• Paramount level of uncertainty about the 

depth and the duration of the crisis 

• New wave(s) of lockdowns? 

• Effectiveness of macroeconomic 

policies? 

• Breaking supply chains? 

Multiple targeted policy packages 

• Capital release: P2G and front-loading 

of changes in P2R composition 

• National macroprudential decisions on 

the release of CCyB and occasionally 

other buffers 

• Profit distribution restrictions 

• (More) over-the-cycle treatment of a 

range of IRFS9 parameters and national 

moratoria 

• National guarantees 

15 


Looking at the full distribution of possible outcomes 

Full distribution of GDP growth (y-o-y) 

Full distribution of banks solvency ratio 

• The model is used to generate the full distribution of economy-wide and bank-level 

outcomes 

• All scenario are plausible i.e. consistent with historical shock distributions 

16 


Narrative based selection 

GDP growth (y-o-y) of individual scenario paths 

• Selecting the subset of scenarios 

corresponding with the narrative of a deep 

contraction in output in 2020, adversity 

triggered by aggregate demand and supply 

shocks 

• Weighting function on individual simulated 

scenarios operating on variables of interest 

(both exogenous incl. shocks, and 

endogenous) 

• Averaging economy-wide and bank-level 

results across the subset of these scenarios 

• The outcome remains consistent with 

historical evidence and takes account of 

uncertainty about factors not pinned down by 

the narrative 

17 


Illustrative scenarios 

GDP growth with and without the feedback loop 

in the three scenarios 

• Three scenarios sharing the property on a deep 

contraction in GDP in 1-2Q 2020, and: 

• A gradual recovery (V-shape) 

• A sluggish recovery (U-shape) reflecting 

extended repercussions of the first COVID-19 

wave 

• A protracted recession (L-shape) assuming the 

second wave of COVID-19 lockdowns starting at 

the end of 2020 

• Though not an initial source of adversity the 

banking sector has an important role in its 

propagation 

18 


Lending impact 

Impact of policies on lending volumes to the nonfinancial 

private sector 2020-2022 

• Policies can moderate the contraction in lending 

volumes to the non-financial private sector (and 

help reduce the lending margins) 

• The strongest effect on the loan growth to the nonfinancial 

corporates can be attributed to capital 

release and the targeted impact of national 

guarantee schemes 

• The package may not be sufficient to support 

lending in a prolonged and severe recession 

scenario (with weak credit demand) 

19 


Semi-structural design versus G@R 

Regular application of semi-structural models 

to generate GDP fancharts… 

Reinterpreted from the Growth-at-Risk angle… 

Source: National Bank of Poland (2008), Fan charts of inflation and GDP, as based on Budnik et al 

(2009), An update of the macroeconometric model of the Polish economy NECMOD, NBP Working Paper 

No. 64. 

Note: Stylised representation. 

20 


Background 

• The phase-in of Basel III finalisation deferred by one year to January 2023 amid the Covid-19 

pandemic 

• European Commission asked the European Banking Authority (EBA) and the ECB back in 2019 for 

the impact assessment of the reforms: Basel III reforms: impact study and key recommendations 

• The new call for the constant balance sheet (EBA) and macroeconomic (ECB) impact assessment in 

2020 

2019 

The impact assessment 

for ‘normal’ 

macroeconomic 

conditions 

2021 

The impact assessment 

for two macroeconomic 

scenarios: pre-Covid 

and Covid pandemics 

Focus on ‘plain vanilla’ 

Basel III design 

Starting point 2017 

‘Plain vanilla’ and EUspecific 

designs 

Starting point 2019 

21 


Basel III framework 

Initial phase 

• Higher quality of bank regulatory capital (a 

greater focus on CET1 capital); 

• Higher level of capital requirements; 

• Macroprudential elements: (i) CCB, CCyB 

capital buffers; (ii) G-SII capital buffer; (iii) a 

large exposures regime for interlinkages across 

financial institutions; 

• A minimum leverage ratio requirement; 

• Standards for market risk, counterparty credit 

risk and securitisation; 

• Framework for liquidity risk and maturity 

transformation: LCR, NSFR. 

Finalisation 

• Higher risk sensitivity of the standardised approach 

for credit risk; 

• Constraining the use of the IRB: (i) limiting the use 

of A-IRB, (ii) placing limits on inputs used under 

IRB; 

• Revising the output floor: (i) tightening to 72.5%; (ii) 

based on new standardised approaches 

• A leverage ratio buffer for G-SIIs; 

• Streamlining the calculation of CVA and operational 

risk. 

Seeks to restore credibility in the calculation of RWAs and 

improve the comparability of banks’ capital ratios 

Higher capital ratios 

Higher state- and institution-dependent 

risk weights 

22 


Cost and benefit assessment so far 

Lower credit supply and output on 

the transition to higher capital 

requirements 

Macroeconomic Assessment Group 

report (2010) An assessment of the 

long-term economic impact of 

stronger capital and liquidity 

requirements 

Lower probability of a financial crisis 

LEI (2010) An assessment of the longterm 

economic impact of stronger capital 

and liquidity requirements 

Lower credit and output losses during a 

financial crisis and/or economic recession 

(Budnik et al., 2019, The benefits and 

costs of adjusting bank capitalisation: 

evidence from euro area countries) 

‘Buts’: 

• (Unadjusted) discreet models 

sharply underestimate the 

probability of rare events 

(Cosslett, 1981) 

• Benefit estimates rely on 

inflated and generally 

regulation-invariant estimates 

of the costs of crisis events 

• How suitable for Basel III 

finalisation package? 

23 


Growth-at-Risk based cost-benefit assessment 

Growth forecast of the euro area economy with the 

emphasis on the mean and lower tails of the distribution 

Comparing two GDP growth distributions: with and 

without the Basel III implementation 

• Looking at the mean to measure short term costs, and at tails to measure long term 

benefits 

24 


Initial impact of Basel III finalization 

The effect of Basel III finalisation on the euro area 

CET1 ratio 

The effect of Basel III finalisation on the total euro area REA 

Source: Budnik et al (2021) The growth-at-risk perspective on the system-wide impact of Basel III finalization in the euro area 

• An increase in REA translates into the initial reduction in the euro area CET1 ratio of around 2.5 pp (and 

about 0.3 pp less with a gradual implementation of the output floor) 

• The revision of methodologies for credit, market risk and the output floor each contributes about 7pp to the 

increase in REA, while the revision of operational risk adds additional 4pp 

25 


Banks’ adjustments 

CET1 built-up via retained earnings vs. 

the reduction in REA 

Bank-level adjustment through retained earnings 

vs. deleveraging 

Initial capitalisation vs. cumulative loan 

growth 

Legend: All changes expressed cumulatively for 10 years after the reform phase-in. 


• Retained earnings are twice as important as the reduction in REA; the bulk of adjustment via earning 

retention in the first 6 years after the reform, and via deleveraging in the first 3 years 

• Banks with high income generating capacity cover capital needs by earning retention; with lower profitability 

or an initial capital shortfall are more likely to deleverage 

26 


Debt funding costs after transition 

Effect on wholesale funding costs across multiple 

economic scenarios 

Effect on liability structure 


• The marginal effect of additional 1% of capital (compared to assets) on wholesale funding costs is 10bp 

in the median, but becomes more substantial in adverse scenarios (25bp in 5% most adverse scenarios) 

• Adopting the Basel III package reduces banks’ demand for wholesale funding (reflecting increasing role 

of own funds on the liability side of banks’ balance sheets) 

27 


Importance of tailored assessment 

Difference in leverage ratio, funding costs and ROA 

in 2027 compared to current regulation 

Difference in leverage ratio, funding costs and ROA 

in 2027 following an increase in target capital ratios 

compared to current regulation 


• Basel III triggers a permanent 1pp increase in the euro area leverage ratio; the effect is halved (and 

gradually diminishes) when an increase in regulatory capital happens via a proportional increase in 

regulatory capital ratios 

• A broad-based increase of risk weights (incl. those on generally lower risk weighted portfolios) limits 

banks’ ability to reduce the overall capital charges via portfolio composition 

28 


2021 and three EU Basel III implementation designs 

Three constant elements of the EU-specific design 

Three variants of the output floor with different 

treatment of P2 requirements and SRB buffers 

the application of SME supporting factors on top of 

the Basel SME preferential risk weight treatment 

CVA exemptions 

excluding the bank-specific historical loss 

component from the calculation of the capital for 

operational risk (ILM=1) 

Note: the presentation uses the updated EBA nomenclature i.e. main EU specific (OF 

approach 1), alternative EU-specific (OF approach 2), parallel stacks (OF approach 3) 

29 


2021 and macroeconomic scenarios 

Pre-Covid scenario 

•December 2019 forecasts 

(baseline of the SSM 

Vulnerability Analysis) for 

2020-2022 

•Basel III finalization phased-in 

in 2020 (balance sheets end 

2019) 

Covid scenario 

•June 2020 forecast (central 

scenario of the SSM 


2020-2022 

•Covid-19 mitigation policies 

(supervisory and 

governmental) 

•P2G replenishment in 2022- 

2023 


in 2020 (balance sheets end 

2019) 

Post-Covid scenario 

•June 2020 forecast (central 

scenario of the SSM 


2020-2022 

•Covid-19 mitigation policies 

(supervisory and 

governmental) 

•P2G replenishment in 2022- 

2023 


in 2023 (forecasted balance 

sheets end 2022) 

• Model extensions compared to 2019: non-trivial role of relative risk weights, new NPL reduction 

policies in the euro area, selected Covid-19 policies i.e. public guarantees and moratoria 

30 


Plain vanilla design and scenario sensitivity 

Lending to the non-financial private sector y-o-y 

GDP y-o-y 

Legend: The impact of plain vanilla (original Basel standards) implementation in the pre-Covid macroeconomic scenario starting in 2020 (bars), in the Covid scenario starting in 2020 (dots) and 

starting in 2023 (triangles). 

Source: Budnik et a (2021) Macroeconomic impact of Basel III finalisation in the euro area 

• Contained costs of the introduction of Basel III implementation under pre-Covid-19 scenario 

• Higher costs of the package under Covid-19 scenario, however the introduction of the Basel III 

implementation in 2023 can be better approximated by the pre-Covid estimate 

31 


Costs of EU-specific versus plain vanilla Basel III 

Lending to the non-financial private sector y-o-y 

GDP y-o-y 

Legend: The impact of plain vanilla (original Basel standards) and three EU-specific designs is derived as the difference between the mean of simulations assuming the introduction of the reforms in the pre-Covid 

macroeconomic scenario and the mean of simulations assuming no policy change in the same macroeconomic conditions. 


• (Already) low short-term costs of the Basel III implementation are further reduces with EU-specific 

designs 

• Different specifications of the output floor start to matter only starting from 3-4 year following the 

reform implementation 

32 


Benefits of EU-specific versus plain vanilla Basel III 

Growth-at-Risk for GDP y-o-y 

• In a longer perspective (9-10 year after the 

reform) the plain vanilla Basel III 

implementation translates into a 

permanent shift in GaR by around 0.1pp 

starting 

• The long-run benefits of the main EU 

approach are around 40% below the 

benefits under the plain vanilla 

implementation 

Legend: The difference between the annual euro area GDP growth with versus without Basel III implementation in the 10 th 

percentile of the corresponding output growth distributions. Note that the type of the scenario assumed for the first three years 

following the introduction of the Basel III implementation (i.e. pre-Covid-19 or Covid-19) does not affect this long-term estimate. 


• The benefits from the EU parallel stacks 

are only a fraction (one-fourth) of the longterm 

gains under the plain vanilla 


33 


Zooming into banks’ solvency developments 

CET1 ratio, leverage ratio and ROA 

CET1 capital in 2030 for banks employing 

either predominantly standardised or IRB 

approaches 

Legend: Results are for the pre-COVID-19 scenario. 


Legend: The capital stock in 2030 as the percentage difference from the capital stock without the 

Basel III implementation in place. The first group of banks includes banks that fully use the 

standardised approach, whereas the second group includes banks that at least partially use the IRB 

approach. Results are for the pre-COVID-19 scenario. 

Source: ECB 

34 


Conclusions: balancing costs and benefits 

GaR based cost-benefit assessment of the plain 

vanilla and main EU approach to Basel III 

• The short-term costs of the phase-in of the 

plain vanilla Basel III implementation are low, 

amounting to a reduction in output growth by 

less than 0.1pp in the first years following the 


• The Basel III implementation will permanently 

strengthen the resilience of the economy to 

adverse shocks, as reflected in the shift of the 

Growth-at-Risk measure by 0.1pp upwards 

Legend: Stylized representation. Solid dark blue line illustrates the mean GDP growth rate without Basel III implementation, solid 

red line stands for the mean GDP growth rate with the plain vanilla Basel III implementation, and solid light blue line for the mean 

GDP growth rate with the main EU specific Basel III implementation. Solid light blue field illustrates the reduction in the mean 

GDP growth following the plain vanilla Basel III implementation. Dotted dark blue line shows the Growth at Risk in the absence of 

Basel III implementation, dotted red line the Growth at Risk in the presence of the plain vanilla III implementation, while dotted 

light blue line shows the Growth at Risk for the main EU specific Basel III implementation. Green field illustrate the reduction in 

economic adversity in 10% tail events following the plain vanilla Basel III implementation. 


• The main EU-specific design reduces both 

costs and benefits of the reform. Further 

output floor modifications are unlikely to lower 

the initial costs of reforms, while e.g. parallel 

stacks cuts down the long-term resilience 

gains by half compared to the main EU 

approach. 

35 


Wrapping-up and moving forward 

• A new model and a new way of conducting 

macroprudential stress testing 

• BEAST links banks’ heterogeneity with the 

flexibility and economic rigour of the core 

forecasting models supporting monetary policy in 

central banks 

• Semi-structural stress testing allows to overcome 

many shortcomings of the hybrid approach, and 

inspires new evolutions (e.g. scenario design) 

• Yet, BEAST applications expand beyond stress 

testing: a core tool for policies assessment and 

scenario analysis (e.g. forecasting) 

• Expanding the use and comprehensiveness of 

stochastic simulations incl. G@R 

• The GaR perspective offers: 

• The same metrics to assess costs and 

benefits over time (the distribution of GDP 

growth), 

• Does not rest on rare observations of crisis 

events, 

• Can encapsulate other metrics offering a 

more complete picture (over time, and in 

terms of uncertainty of estimates). 

• The methodology can be and is applied also to 

combinations of monetary and prudential 

instruments e.g. assess a macroprudential 

stance. 

36 


Q&A 

37

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