© oliver wyman | lon-fsp11001-082 financial services modelling credit risk estimates prudential...

© Oliver Wyman | LON-FSP11001-082FINANCIAL SERVICES

MODELLING CREDIT RISK ESTIMATESPRUDENTIAL CREDIT RISK MODELLING FOR SUPERVISORY PURPOSES22 OCTOBER 2014

Martin RauchenwaldPhillip Escott

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Copyright © Oliver Wyman

3© Oliver Wyman | LON-FSP11001-082 3

Agenda and objectives of this session

AGENDA

1. Introduction to Oliver Wyman

2. Credit Risk modelling

3. Oliver Wyman perspectives on modelling Credit Risk– Non-granular portfolios– Granular portfolios– Other considerations

4. Challenges for supervisors

5. Questions and concluding remarks

OBJECTIVES

• Discuss Oliver Wyman views and experience prudential Credit Risk modelling

• Discuss methodological approaches and challenges

• Review lessons learned from past regulatory exercises

44© Oliver Wyman | LON-FSP11001-082

Oliver Wyman has extensive experience in supporting both regulators and banks in the modelling and assessment of Credit Risk

Selected overview of Oliver Wyman experience

PAN-EUROPEAN EXCERCISE NATIONAL EXERCISES (EMEA)

European Central Bank

“The ECB has appointed Oliver Wyman to support the preparation

and implementation of the Comprehensive Assessment of the

significant banks that will be directly supervised by the ECB.”

“Oliver Wyman has collaborated with relevant public bodies and

supported many banks during the execution of AQRs and stress

testing exercises”


Credit losses and provisions have a number of drivers

PROBABILITY OF ‘DEFAULT’

PD

EXPOSURE AT ‘DEFAULT’

EAD

LOSS GIVEN ‘DEFAULT’

LGD

X X

CYCLE


For defaulted assets, the critical determinant of provisions is the expected LGD, with differing approaches used across asset classes

CORPORATE SME RETAIL

SPECIFIC PROVISIONS

COLLECTIVE IMPAIRMENT

MODELS

ASSET CLASS

Expert basedData / model based


For corporate exposures, firms typically adopt either a ‘going concern’ approach or a ‘gone concern’ approachA ‘going concern’ approach relies on the estimated value of firm’s cash flows

Steady-state cash flows Two-step DCFOR

Recent accounts

Estimation of cash flow present value from exposures

Reliable forecasts

Discount cash flows with effective interest rate

Estimation of annual cash flows from exposure

1-period sustainable cash flows

EBITDA

Cash flow adjustment

Sustainability adjustment

Multiple

Step 1: Annual cash flow forecast (to 10 years)

Step 2: Terminal value+

+

x


In a ‘gone concern’ or asset based approaches, expected recoveries are based on up-to-date asset values and reflect forced sales discount and costs

Illustrative results from collateral adjustments

Bank val-uation

Indexed to Today

Bank val-uation

(Today)

3rd party appraisal

haircut

Indexed to default

Indexed to sale

Forced sales

discount

Recovery costs

Final value0

500000000

1000000000

1500000000

2000000000

2500000000

3000000000

3500000000

EU

R M

illi

on

s

Ha

irc

ut

• Important to have real values – while banks say they might not sell, the reality is that the asset should be close to market value in most circumstances where gone concern is considered

• Redevelopment values etc need strict rules


For performing portfolios, IFRS banks typically maintain Incurred But Not Recognised (IBNR) provisions …Common approaches to determining IBNR provisions

Roll-rate analysis

Vintage analysis

Markov chains

EL models

Macro-economic models

Historical averages

Complexity

Effo

rt

Laggards


… although most approaches have some challenges and overlays are often requiredCommon challenges in banks’ IBNR modelling approaches

• Non-granular approaches may fail to reflect changes in underlying portfolio, e.g.:– Credit quality / underwriting standards– Seasoning effects

• Care needs to be taken to ‘strip out’ implicit expansionary behaviour– Collateral value increases– Credit loosening

• Firms often fail to condition on the current state of the economy so increases in IBNR lag actual increases in risk

• Insufficient capture of PD-LGD or PD-EAD correlations

• Data issues

• Inability to capture effects ‘not yet in the data’ e.g.– Regulatory / legal changes– Bank process changes


Under ‘incurred loss’ approaches, provisions typically increase significantly, and rapidly, during a downturnKey drivers of losses / provision acceleration in a downturn

• Increased default rates, above and beyond ‘obvious’ macro-economic impacts e.g.– Reduced pre-payment– Difficulty refinancing bullet loans– Portfolio with seasoning effects becoming ex-growth

• Increased EAD as obligors call on lines to try to avoid default

• Reduced cure rates– Reduced ability to re-finance out of trouble– Cash-flow issues likely to persist (unemployment, creditors)

• Reduced recoveries– ‘going concern’ values impacted by profitability and market multiples– Collateral values– Forced sale discounts

• Longer workout / reduced speed of write-off increases stock of defaults


In systemic crises, the market often starts to question banks’ provisions, focusing on whether defaults are fully recognised and sufficiently provided for

KEY QUESTIONS IN A CRISIS

ARE ANY TROUBLED ASSETS BEING HIDDEN?

HAVE SUFFICIENT PROVISIONS BEEN RAISED FOR TROUBLED ASSETS?

• Definition of ‘default’

– Independence / objectiveness?

– Consistency across banks?

• Does forbearance = ‘Extend and Pretend’?

• ‘Going concern’ values

– Overly optimistic?

– Consistent (within and across banks)?

• Are collateral values unduly optimistic?

• Is there a significant disconnect between ‘going concern’ and ‘gone concern’?


In undertaking system wide provisioning assessments, high quality data matters, as well as a clear methodology and definitions to support them

Essentials for data collection

QUALITY

INTEGRITY

CHALLENGE

• Collection of high-quality data

• Establishment of clearly defined processes

• Standardised methodology and definitions required in order to assure basic data quality

• Ensure review of data for basic integrity:

– Internal consistency

– Distributions

– Inter-temporal consistency

Example methodology document: AQR Phase 2 Manual

• Challenge data effectively, including

– Plausibility checks

– Basic reconciliations with external sources


In reviewing the adequacy of banks’ provisions in a system-wide review, differing approaches are taken for granular and non-granular portfolios

ASSESSING PROVISIONS

NON-GRANULAR PORTFOLIOS GRANULAR PORTFOLIOS

• Small and concentrated portfolios

• Typical examples: Project Finance or Shipping

• No sampling would be required to review the entire portfolio

• Larger, less concentrated portfolios

• Typical examples: Retail or SME portfolios

• Sampling in order to:

– Identify common issues

– Allow extrapolation to full portfolio


When individual cases are to be reviewed, it is important to focus on cases which are ‘most likely to be wrong’

Illustration: ECB AQR Phase 2 Manual, March 2014

SELECTION OF THE SAMPLING METHOD

SAMPLE SELECTIONSAMPLE REVIEW AND PROJECTION TO THE WIDER PORTFOLIO

• Sampling trades off:

– Coverage

– Effort

• Sampling reflects

– Exposure profile

– Risk profile

• Sampling rates selected to produce acceptable error rates

• Stratification:

– Exposure size

– Risk (credit + ‘wrongness’)

• Higher sampling rates in larger / riskier strata

• Low / no sampling for smallest cases

Illustrative sampling rates per strata for a portfolio

• Need to assess whether cases are outliers

• Findings extrapolated to full portfolio


Credit File Reviews are the foundation of estimating losses in non-granular portfolios

Beyond risk assessment, the quality of the credit files can contribute additional information on the banks’ processes and data

Typical elements reviewed as part of Credit File Reviews

ASSET SEGMENT CLASSIFICATION GROUP EXPOSURE PERIMETER

PROVISION

• Review of the regulatory or otherwise defined asset segment/ exposure class

• Identification of the requirement to reclassify misclassi-fied exposures or structural misclassifications

• Exposure classification as performing/non-performing according to regulatory/internal definitions

• Identification of the ability to identify performance status appropriately

• Assessment of related parties for the exposures

• Review of the interconnections of exposures and ability to identify group of connected clients

• Assessment of relevant impairment triggers or future loss criteria

• Determination of the provisioning level

– Going concern

– Gone concern


Given the central role of real estate as assumed risk mitigant, it is critical that robust, independent market based valuations are used

INDEPENDENT VALUATION OF COLLATERAL

STANDARD APPROACH TO REAL ESTATE VALUATION

EMPHASIS ON MARKET VALUES

• Independent valuation required in order to ensure valuations are performed without bias

• “Independent” will usually imply a third-party (specialist firm) valuation

• Standardisation of methodology, valuation process, definitions and interpretations is key

• It is required to ensure a consistent and comparable set of findings in order to facilitate the further analysis

• Assets should ideally be valued on the basis of market value (unless more conservative approaches are available nationally)

• Prevents application of excessive hope value built from discounted cash flow forecasts


Where issues are identified, supervisors will need to address a few issues

Whether to be more prescriptive around approaches and expectations for provisioning approaches (e.g. CBI)

How to account for the RWA impact that results from higher losses identified with any Credit Risk assessment

How to adjust available capital and EL vs. provisions comparisons to reflect increased defaults / higher provisions

Whether historical data remains ‘relevant’ or embeds too benign / severe a view of what will happen in the future


Wrap-up

Questions


Contacts

Phillip Escott

Partner

Financial Services

55 Baker Street

London, W1U 8EW, UK

+44 20 7852 7526

+44 7970 141 272

[email protected]

www.oliverwyman.com

Martin Rauchenwald

Partner

Financial Services

Tessinerplatz 5

8027 Zurich, Switzerland

+41 44 553 3526

+43 664 515 4880

[email protected]

www.oliverwyman.com

QUALIFICATIONS, ASSUMPTIONS AND LIMITING

CONDITIONS

This report is for the exclusive use of the Oliver Wyman client named herein. This report is not intended for general circulation or publication, nor is it to be reproduced, quoted or distributed for any purpose without the prior written permission of Oliver Wyman. There are no third party beneficiaries with respect to this report, and Oliver Wyman does not accept any liability to any third party. Information furnished by others, upon which all or portions of this report are based, is believed to be reliable but has not been independently verified, unless otherwise expressly indicated. Public information and industry and statistical data are from sources we deem to be reliable; however, we make no representation as to the accuracy or completeness of such information. The findings contained in this report may contain predictions based on current data and historical trends. Any such predictions are subject to inherent risks and uncertainties. Oliver Wyman accepts no responsibility for actual results or future events.The opinions expressed in this report are valid only for the purpose stated herein and as of the date of this report. No obligation is assumed to revise this report to reflect changes, events or conditions, which occur subsequent to the date hereof.

All decisions in connection with the implementation or use of advice or recommendations contained in this report are the sole responsibility of the client. This report does not represent investment advice nor does it provide an opinion regarding the fairness of any transaction to any and all parties.

© oliver wyman | lon-fsp11001-082 financial services modelling credit risk estimates prudential...

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