[PDF] STAMP€: Stress-Test Analytics for Macroprudential Purposes in the

View - Download STAMP€: Stress-Test Analytics for Macroprudential Purposes in the

Previous PDF

Next PDF

STAMP€:

February 2017

Foreword

5

Chapter 1 Editors' introduction

10 Chapter 2 Stress-Test Analytics for Macroprudential Purposes:

Introducing STAMP€

13 Chapter 3 Applying STAMP€ - a macroprudential extension of the 2016

EU-wide stress test

31

SATELLITE MODELS

Chapter 4 Credit risk satellite models

45
Chapter 5 Satellite models for bank interest rates and net interest margins 57

Chapter 6 Top-down modelling for market risk

71
Chapter 7 Satellite model for top-down projections of banks' fee and commission income 87
Chapter 8 Operational risk module of the top-down stress test framework 95

Chapter 9 Loan flow satellite models

106

ESTIMATING MACROECONOMIC FEEDBACK

Chapter 10 Estimating the macroeconomic feedback effects of macroprudential measures - Dynamic Stochastic General Equilibrium (DSGE) models 115
Chapter 11 Assessing second-round effects using a Mixed-Cross-

Section GVAR model

ĩ 131

ESTIMATING CONTAGION IMPACTS

Chapter 12 Interbank contagion

147

Chapter 13 Cross-sector contagion

161

FURTHER EXTENSIONS

Chapter 14 A top-down liquidity stress test framework 168
Chapter 15 The Integrated Dynamic Household Balance Sheet (IDHBS)

Model of the euro area household sector

192
Chapter 16 Prospects for further developments of STAMP€

ĩ 207

ECB Occasional Paper No 152

Financial Stability Review

"A Survey of Systemic Risk Analytics"Office of Financial Research Working Paper No 1 "A composite indicator of systemic stress in the financial system"ECB Working Paper No 1426 "Does systemic risk in the financial sector predict future economic development?",The Review of Financial Studies, Vol. 25, No 10 "Characterising the financial cycle: a multivariate and time-varying approach" ECB Working Paper No 1846, "Characterising the financial cycle: don't lose sight of the medium term!"BIS Working Paper No 380

Macroprudential Bulletin,

Issue 2

dynamic dimension interaction with the real economy interconnections between financial institutions system-wide liquidity assessment interaction with non-financial sectors impact assessment of macroprudential policy instruments

Occasional Paper Series

Macroprudential Bulletin

The macroprudential policy function has added a new dimension to stress testing that goes well beyond the examination of individual bank results. ECB staff has over the years developed a stress-testing framework for micro- and macroprudential purpo ses (see Henry and Kok, 2013). This chapter focuses on the macroprudential dimension of stress testing exercises and introduces the updated and extended stress-testing infrastructure. STAMP€ (Stress-Test Analytics for Macroprudential Purposes for the euro area) embeds various components that can be activated, for a given macro-financial scenario, in a centralised manner (otherwise called "top- down"). Beyond computing possible capital shortfalls for an individual bank under stress, which is commonly done also for microprudential purposes, the framework encompasses additional channels that are fundamental to macroprudential analyses, such as banks' reactions, contagion and feedback loops with the real economy. Further extensions include a liquidity stress test component and interactions with other parts of the wider financial sector. These additional analytical elements are described and corresponding simulation results provided, illustrating the extra information and value added of the extensions

Chart 2.1

ScenarioBalance sheetFeedbackSatellite models

Dynamic adjustment

model Chart 2.2 Chart 2.3

Y-axis: CET1 capital ratio after

2nd round macro feedback

impact (in Pct.)

X-axis: CET1 capital ratio after

1st round impact (in Pct.)

Chart 2.4

Y-axis: CET1 capital ratio ex-post

interbank contagion

X-axis: CET1 capital ratio under

adverse scenarios Chart 2.5 Table 2.1

European Journal of Political

Economy,

Working Paper Series

BCBS Working Papers

Bank of England Financial Stability Paper

Working Paper Series

Working Paper Series

International Journal of Central Banking

Journal of Applied Econometrics

STAMP€: Stress-Test Analytics for Macroprudential Purposes in the euro area -

Chapter

2 Stress-Test Analytics for Macroprudential Purposes: Introducing STAMP€

30
Gross, M., Kok, C. and ĩochowski, D. (2016), "The impact of bank capital on economic activity Evidence from a Mixed-Cross-Section GVAR model", Working

Paper Series, No 1888, ECB.

Gross, M., Henry, J., and Semmler, W. (2017), "Destabilising effects of bank overleveraging on real activity - An analysis based on a Threshold Mixed-Cross- Section (T-MCS-) GVAR Model", Macroeconomic Dynamics, forthcoming. Gross, M. and Población, J. (2017a), "Implications of model uncertainty for bank stress testing

Journal of Financial Services Research

, forthcoming (working paper available in ECB Working Paper Series, No. 1845, 2015, "A false sense of security in applying handpicked equations for stress test purposes"). Gross, M. and Población, J. (2017b), "Assessing the efficacy of borrower-based macroprudential policy using an integrated micro-macro model for European households", Economic Modelling, Vol. 61, pp. 510-528. (2013), "Optimal asset structure of a bank - Bank reactions to stressful market conditions", Working Paper Series, No 1533, ECB. and Kok, C. (2014), "Emergence of the EU corporate lending network", The Journal of Network Theory in Finance, Vol. 1. (2016), "Systemic liquidity stress testing", forthcoming. G. (2016), "Dynamic balance sheet model with liquidity risk", Working Paper

Series, No 1896, ECB.

Hardy, D. and Schmieder, C. (2013), "Rules of thumb for bank solvency stress testing",

IMF Working Paper, No 13/232.

Henry, J. (2015), "Macrofinancial Scenarios for System-Wide Stress Tests: Process and Challenges", in Quagliariello

M. (ed.), Europe's New Supervisory Toolkit: Data,

Benchmarking and Stress Testing for Banks and their Regulators, Risk Books,

London.

Henry, J. and Kok, C., (eds.), (2013) "A macro stress testing framework for assessing systemic risk s in the banking sector", Occasional Paper Series, No 152, ECB.

Jeanne, O. a

nd

Korinek, A.

(2013), "Macroprudential Regulation Versus Mopping Up

After the Crash", NBER Working Papers 18675.

Kitamura, T., Kojima

S., Nakamura

K., Takahashi, K. and Takei, I. (2014), "Macro

Stress Testing at the Bank of Japan", BOJ Reports & Research Papers. Kok, C., Mirza, H. and Pancaro, C. (2017) "Macro Stress Testing European Banks' Fees and Commissions", Working Paper Series, forthcoming, ECB. Ong, L.L. (ed.), (2014) "A Guide to IMF Stress Testing: Methods and Models",

International Monetary Fund

, Washington, D.C. As illustrated by the recent EU-wide stress test conducted by the EBA, the estimated impact of an adverse scenario can be quite severe. 19

For the 37 largest euro area

banks included in the 2016 EU-wide stress test, the aggregate Common Equity Tier 1 (CET1) capital ratio is expected to drop by 390 basis points under the adverse scenario, from about 13.0% in 2015 to about 9.1% at the end of 2018.

At the same time

the se sizeable effects cover only first-round stress impacts on banks' balance sheets. They do not account for the endogenous reaction of banks to anticipated higher capital needs, nor for the interaction of banks with one another and with other economic sectors. In addition, the EBA stress testing methodology is based on a static balance sheet assumption, whereby the total volume and composition of all bank asset and liability items should remain unchanged over the stress test horizon, and maturing items should be replaced by identical positions. The modular framework of STAMP€, which connects several standalone models and tools, has the capacity to deliver a more complete and enriched picture of what the overall macrofinancial impact of stress on the banking sector could represent, by incorporating additional amplification channels. The results presented here should, nonetheless, be treated as illustrative, given that some of the findings discussed in this chapter rely, to a large extent, on specific and possibly strong assumptions which would call for further robustness analyses. Chart 3.1

Macroeconomic

scenario

Static balance sheet:

BU solvency impact

Satellite loan flow

models

Dynamic balance sheet:

TD solvency impact

Capital target shortfall

Interbank contagion

Cross-sector spillovers

Macroeconomic impact

(DSGE/GVAR)

Satellite models: PD,

LGD, IR, loan flows

Second-round TD

solvency impactSecond-round effectsInterconnectedness effectsFirst-round effects

Chart 3.2

Chart 3.5

change in weighted average cost of liabilities change in total liabilities Chart 3.3

Chart 3.4

all banks adjust loan volumes benefittingbanks adjust loan volumes change in weighted average asset yield change in total assets

Chart 3.6

Chart 3.7

Chart 3.8

European Journal of Political

Economy

Working Paper Series

ESRB Working Paper Series

Working Paper Series,

Financial Stability Review

International Journal of Central Banking,

Macroprudential Bulletin,

Macroprudential policy issues arising from

low interest rates and structural changes in the EU financial system

Macroeconomic Dynamics

Working

Paper Series

Working Paper Series

Working Paper Series

Occasional Paper Series

Macroprudential Bulletin,

SATELLITE MODELS

This chapter presents the methodology that has been used for developing top -down satellite models, with a specific focus on credit risk (CR) parameters, that is, country and loan portfolio-level probabilities of default (PDs) and loss given default (LGD) parameters. The parameter paths, derived from the satellite models, form the basis for projecting bank loan losses conditional on a macro-financial scenario. For the LGD parameters, a structural model is involved for housing -related portfolio segments, i.e. for the non-financial corporate real estate and the household mortgage loan portfolios. A Bayesian model averaging (BMA) technique has been employed to develop the PD satellite models, which, in total, comprise several hundreds of bridge equations linking these risk parameters to macro -financial variables. The credit risk satellite model system plays a crucial role in the overall stress test model suite (along with the BMA-based bank interest rate model package, presented in Chapter 5), as the risk of borrowers defaulting and not repaying their loans is one of the most material risks that banks face and for which they ought to build up an adequate level of loan loss reserves. It is, therefore, particularly important that the credit risk models are developed in a robust manner, to ensure that they provide precise estimates for PD paths conditional on an assumed macro -financial stress scenario. Quantifying the impact of this major channel is thereby needed for any macroprudential stress-test application. The chapter is divided into three parts. Sections 1 and 2 present, respectively, the PD and LGD model frameworks. Some illustrative scenario projections implied by the models are presented in Section 3. =lnݕ െln(1െ ݕ K L L I

I =෍ܭ

݅= 1,...,ܫ

i G i /(1െ ෍ ߩ y =expݔexpݔ L Table 4.1 GDP growth Private consumption growth Investment growth Export growth Stock price growth Unemployment rate changes Price inflation Long-term interest rate spreads (to DE) Short-term interest rate NFC -RE NFC -non-RE HH -HP HH -CC FIN SOV Chart 4.1 Chart 4.2

NFC-RE NFC-non-RE HH-HP HH-CC FIN SOV

Normalised LRM

-1.4-1.2-1-0.8-0.6-0.4-0.200.2

GDP growth

NFC-RE NFC-non-RE HH-HP HH-CC FIN SOV

Normalised LRM

00.20.40.60.811.21.41.6

Long-term interest rate spread (to DE)

HH-HPHH-CC

Normalised LRM

-0.200.20.40.60.811.21.4

Unemployment rate changes

NFC-RE NFC-non-RE

Normalised LRM

-1-0.8-0.6-0.4-0.200.2

Investment growth

T0i j L +NPL gL =(1 + g)L g NPL =NPL (1െw )+PD P െCure P = P (1െr െPD )+NB +Cure t w r t-1 t t LGD= ( (1െProbability of Cure) ή LGL ) +Costs 2ߨ

Chart 4.3

Chart 4.4

NFC-RE NFC-non-RE HH-HP HH-CCFINSOV0.511.522.533.54

Horizon-average PD multiples to T0 (adverse)

NFC-REHH-HP11.11.21.31.41.51.61.71.81.92

End-horizon LGD multiples to T0 (adverse)

Journal of Financial Services Research

American

Economic Review

Banks' core activities consist in the acceptance of deposits and the creation of loans. Thus, their balance sheets, to a large extent, comprise interest-bearing assets and interest expense-generating liabilities. Consequently, changes in interest paid or received are among the most material sources of variation affecting a bank's profits and losses and hence its solvency position. Therefore, the satellite models that address interest rate risk (along with the credit risk models, see Chapter 4) play an essen tial role in the overall stress test toolkit used for macroprudential assessment purposes. ECB staff have employed two complementary modelling approaches to translate macro-financial scenarios into developments in banks' net interest income, both being presented in this chapter. The first approach, presented in Section 1, uses country-level data on front-book interest rates, i.e. rates on new business, which are available as an input for different asset and liability segments. The satellite models provide, as an output, the country and segment -specific projections of front-book interest rates conditional on a given macro-financial scenario. These paths, once combined with the scenario-conditional evolution of gross and performing loan stocks (see Chapter 9), imply an interest income and expense flow which, in turn, can be expressed in the form of a net interest margin (NIM), i.e. the ratio of net interest income over interest-bearing assets and a key driver of banks' profitability. The second modelling strategy, presented in Section 2, relies on a dynamic panel approach to directly estimate the relationship between banks' NIM and a set of selected macro -financial variables, applying a variable-selection technique. The estimated model parameters are then used to project banks' NIM conditional on a given macro-financial scenario. This approach is less demanding in terms of the required data inputs and is suitable for macroeconomic analyses. In addition, the first approach is also suitable for quality assurance in the context of supervisory stress tests owing to the more granular data inputs required. Table 5.1 Real

GDP Real private

quotesdbs_dbs33.pdfusesText_39

[PDF] comment bien s'organiser en 4eme

[PDF] ? lécoute de chaque élève grâce ? la différenciation pédagogique

[PDF] cours de méthodologie secondaire

[PDF] questionnaire élève en difficulté

[PDF] surface dans oeuvre

[PDF] dossier pedagogique methode de travail

[PDF] profil de classe inspection

[PDF] ratio shob shon

[PDF] portrait de classe

[PDF] organisation et méthode de travail

[PDF] lean management pdf gratuit

[PDF] calcul de la surface utile

[PDF] organisation de travail dans l'entreprise

[PDF] chaques élèves

[PDF] lean manufacturing cours pdf