(R)isk Revolution - Current trends and challenges in Credit & Operational Risk

Risk (R)evolution:

Current Trends & Challenges

in Credit and Operational Risk

Markus Krebsz 19 September 2012, London

Australian Senior Bankers’ Master class programme

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CONTENTS

PART 1: Risk (R)evolution Credit, Market and Operational risk - A changing landscape

Counterparty Risk challenges

Credit valuation adjustment

Wrong-way risk

Central counterparty risk

ERM Risk challenges

Data quality & Trade lifecycle

Legal Entity Identifiers

Product Taxonomy and UPIs

PART 2: Credit Ratings Crash Course CRA: What are they, how do they compare & which risks are captured?

Using CRAs analysis sensibly: Failures, Criticism and mitigants?

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PART 1:

RISK (R)EVOLUTION

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CREDIT, MARKET & OPERATIONAL RISKS

(R)evolution?:

Credit Risk Counterparty Risk

Market Risk Liquidity Risk

Operational Risk ERM (Enterprise Risk)

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COUNTERPARTY RISK CHALLENGES

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• Lehman Brothers

Counterparty Risk Mgmt Systems failed

• Payments

continued to be made to bankrupt entities

• Sheer complexity of

calculating a reasonable measure of

the credit risk of an exposure

calculating credit valuation adjustments

valuing collateral (and determination of 2nd order credit risk)

• Models

Design, Choice of methodology and Selection of model parameters

• Credit Valuation Adjustment (CVA)

• Wrong way risk (WWR)

• Central Counterparties (CCP)

• Definition

Credit Valuation Adjustment (CVA) is the

Market value of Counterparty Credit of over-the-counter (OTC)

derivatives or in other words the difference between the risk-free

portfolio value and the true value reflecting the counterparty’s default

• Rationale

Mark-to-market losses due to CVA were not directly capitalised

2/3 of CCR losses were due to

CVA losses, only 1/3 due to actual

default

• Interpretation

Either as a ‘Price’ - not risk measure

or a ‘Reserve’, calculated using

empirical PDs and RRs rather than

market spreads

CREDIT VALUATION ADJUSTMENT (1)

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• Calculation

Conceptually simple, but actual calculation of CVA is akin to

pricing a very complex ‘illiquid’ instrument and

cannot be achieved with the same accuracy as standard derivatives

• CVA calculation challenges

Limited liquidity in spreads for Counterparties across term structure

CVA hedging is quite complex and expensive

Computationally highly intensive: i.e. portfolio of 50,000 positions,

2,000 scenarios and 100 time steps requires 10bn valuations

Exposures and Counterparty credit quality are NOT independent, but

modelling the co-dependence is difficult

• Unilateral vs. Bilateral CVA

Unilateral assumes bank doing CVA analysis is default-free. (BIII)

Bilateral accounts for potential default of both, bank and

counterparty. This is more in line with standard market practice at top

FIs for pricing & hedging as well as account rules.

CREDIT VALUATION ADJUSTMENT (2)

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WRONG-WAY RISK

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• Definition

Term describes dependence between the

Credit quality of a counterparty and

Credit exposure of a bank to that counterparty

I.e. Exposure high when Probabilities of Default are high

• Types of WWR

General WWR: Cpty credit quality is correlated for non-specific

reasons with macro-economic factors that also affect the value of the

underlying portfolio

Specific WWR: Cpty exposure is highly correlated with its default

likelihood caused by idiosyncratic factors

• Impact

Can have significant impact on CVA and economic capital.

CENTRAL COUNTERPARTY RISK (1)

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• Motivation

Reduction of bilateral counterparty risk

Increased transaction transparency (pre- but mainly post-trade)

Avoidance of contagion (systemic crisis) in case of large Bank default

• Designed to reduce counterparty risk through

Multilateral netting

High levels of over-collateralisation

Loss mutualisation

• Three Historic CCP defaults to date

Typically, a rare event – but:

• Caisse de Liquidation Paris (1974)

• Kuala Lumpur Commodity Clearing House (1983)

• Hong Kong Futures Exchange (1987)

CENTRAL COUNTERPARTY RISK (2)

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• Clearing House Risk

Clearing Houses are not riskless, in fact they are

Risk-sharing arrangements whereby

Each member is liable for the performance of ALL other members

Absolute exposure amounts are likely to be very substantial

Tail loss insurance of all clearing members

Exposures are naturally hedged

• Risk Waterfall/ CCP layers of protection

Variation margin: charged daily to cover any portfolio M-t-M changes

Initial margin: Posted by members to cover any losses during unwind

CCP equity: Equity buffers provided by clearing house shareholders

Guarantee fund (funded): Mutualised insurance for uncollateralised

losses

Guarantee fund (unfunded): Member’s commitment to provide

additional funds if required (in some cases uncapped liability)

ERM CHALLENGES Opaqueness of transactions, particularly over-the-counter (OTC)

derivatives

Lack of transparency concerning products, valuations, model use –

and ultimately risk management of those products

No common language no communication no understanding

Product classification (or lack of)

Model risk

Regulatory risk

Risk of Unintended consequences

Etc. etc.

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DATA QUALITY & TRADE LIFECYCLE

Who booked the trade, when in which system and why?

Which trade system/repository is the “Golden source”?

How is the trade risk managed, when and how often is reported, to

whom?

How are risks aggregated, identified and transferred/exited?

What measures are taken to price risks adequately?

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MODEL RISK

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• Design Risk

Model = Simplified description/simulation of more complex reality

all models are ‘wrong’, but some are ‘harmful’ (says Nassim T.)

Simpler models can be preferable to over-complex ones (which often

are not robustly calibrated)

• Parameter uncertainty

Following model selection ALL parameters must be estimated

Done via point estimates, often leading to pseudo-accuracy

Creating model risk even for otherwise perfect models

• Models inflexibility

Not capable to handle permanent shifts and structural market

disruptions i.e. caused by default of a systemic counterparty (CCP)

Detection of such shifts can not be based on statistical analysis alone

and judgemental components may become more important

PRODUCT TAXONOMY (UPIs) V a n i l l a S t r u c t u r e d E x o t i c < Flow products > < Templated > < Non-templated >

‘Locked-down’ Building blocks Freely scripted = Tea-Bag = Hot water + Espresso = Cappuccino with Soy milk, + Semi-skimmed milk fair-trade coffee, sugar-free Hazelnut syrup and …

OBJECTIVES Fully classified product suite across Banks or FIs: i.e. Fixed Income, Equities, F/X, Structured Rates & Credit, Commodities divisions

Ability to model Risks & monitor Model performance: Models, Model data, Product certifications, Valuation adjustments and P&L explain

PART 2:

CREDIT RATINGS CRASH COURSE

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Q) Who uses credit ratings – and why?

Source: www.greenbaypressgazette.com/joeheller

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18

Q) How many CRAs exist globally?

Source: www.defaultrisk.com New ‘concept’: Wikirating (www.wikirating.com) 19

Q) D (F) = D (S&P) = D (M)

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RATINGS ‘MAPPING’ TABLE

F i t c h R a t i n g s

Long-term rating Short-term rating

B-

B

CCC

CCC+

CC

CCC-

DDD, DD, D

C

BB

BB+

B+

BB-

AA-

AA

A

A+

BBB+

A-

BBB-

BBB

AA+

AAA

F1+

F1

F1+ or F1

F1 or F2

F3

F2 or F3

B

C

M o o d y ’ s

Long-term rating Short-term rating

B3

B2

Caa2

Caa1

Ca

Caa3

C

Ba2

Ba1

B1

Ba3

Aa3

Aa2

A2

A1

Baa1

A3

Baa3

Baa2

Aa1

Aaa

P1

P-1 or P-2

P-2

P-3

P-2 or P-3

Not Prime

S t a n d a r d & P o o r s

Long-term rating Short-term rating

B-

B

CCC

CCC+

CC

CCC-

D

C

BB

BB+

B+

BB-

AA-

AA

A

A+

BBB+

A-

BBB-

BBB

AA+

AAA

A-1+

A-1 or A-2

A-1

A-2

A-3

A-2 or A-3

B

Ranges within

B-1, B-2 and B-3

C

In

ve

st

me

nt

G

ra

de

Sp

ec

ul

at

iv

e

Gr

ad

e

F2

M a p p e d

i n t e r n a l

r a t i n g

iB-

iB

iCCC

iCCC+

iCC

iCCC-

iD

iC

iBB

iBB+

iB+

iBB-

iAA-

iAA

iA

iA+

iBBB+

iA-

iBBB-

iBBB

iAA+

iAAA

D DMoody’s: D

Source: Bloomberg, Fitch, Moody’s and S&P 21

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ANALYTICAL DIFFERENCES

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RATING PRINCIPLES

Fitch Ratings, Standard & Poor’s:

Probability of default (PD) = First dollar of loss

What is the ultimate default risk?

Moody’s:

Expected loss (EL) = [(PD) X (LGD)]

What is the amount of net loss suffered?

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STATISTICAL : Probability of Default

Q) SF Bond - Tranche 1 rated AAA

= SF Bond - Tranche 2 rated AAA?

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SF Bond

‘SUPER-SENIOR’ RATINGS

Tranche 1: AAAAA

Tranche 2: AAAA

Tranche 3: AAA

Tranche 4: AA+

Tranche 5: A

Tranche 6: BBB-

Tranche 7: BB

Tranche 8 B+

First Loss piece: NR

Source: http://blogtoonismiel.blogspot.com

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A) Benchmark measure B) Benchmark measure

for LGD for PD

C) Opinion

D) Not necessarily based on facts or

knowledge

Q) How would you define ‘rating’?

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RATING DEFINITION

• An opinion… * [Financial journalists]

• …on the relative ability…

• …of an entity to meet financial commitments.

*…view not necessarily based on fact or

knowledge

Ratings are benchmark measures of…

• Probability of default (PD)

• Expectations of Loss given default (LGD)

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Q) Which RISKS are captured by credit ratings?

A) Credit & Market risk B) Credit, Market & Operational risk

C) Credit, Market, Operational, Liquidity & Basis risk

D) None of the above

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Credit risk

only !

• by Basel II

• into banks’ credit rating models

• Investment guidelines and Asset management mandates

RATINGS…

…can capture: …do NOT capture:

Market risk

Liquidity risk

Operational risk

Basis risk (IR risk)

…but, even so, are ’hard-wired’…

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FAILURES

AIG, Bear Stearns, Bradford & Bingley, Enron, Icelandic

banks, Lehman Bros., Monolines, Northern Rock, Parmalat,

Sovereigns (Eurozone), Sub-prime bonds etc.

In their own words...

Fitch: “… did not foresee the magnitude of the decline…or

the dramatic shift in borrower behavior…”

Moody’s: “…We did not . . . anticipate the magnitude and

speed of the deterioration in mortgage quality or the

suddenness of the transition to restrictive lending...”

S&P: “…It is now clear that a number of assumptions used

in preparing ratings on mortgage-backed securities issued

between 2005 and mid-2007 did not work…”

Source: US Government Oversight and Reform Committee, Oct 2008

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OPERATIONAL RISKS

• Changing Rating methodologies and assumptions

• Time lag of rating actions

• Rating model risks

• ‘Fat fingers’, i.e. technical glitches

• Striking the right balance between non- and over-regulation

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RISK MITIGANTS

• Understanding the meaning & limitations of ratings

• Understanding instruments’ risks

• Independent analysis

• Internal ratings

• Disputing rating decisions with the agencies

• Awareness that agencies CAN and DO get their ratings

wrong (Operational risk scenario)

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SENSIBLE USE of CRAs’ Analysis

• Fully understand the instrument you are investing in –

particularly when using other peoples’ monies

• Understand ratings’ limitations and

know how to mitigate rating-related risks (previous slide)

• ‘Ignore’ ratings designators (i.e. AAA etc.) and

focus on CRAs’ analytical narrative instead

• Look out for what is NOT there in the narrative but should

e.g. Why are obvious issues missing in the analysis?

Why has this bond not been rated by all three CRAs?

• Apply common sense and trust your gut feeling 35

CLOSE

Thank you very much

for your attention, contribution and listening today!

________________________________________

________________________________________

CONTACT: + 44 (0) 79 85 065 045

krebsz.net | riskguide.net | creditratingsguide.com

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• ‘Securitisation & Structured Finance post Credit Crunch: A Best Practice Deal Lifecycle Guide’, John Wiley & Sons Inc., Apr. 2011

• ‘Product Taxonomy: A Key Tool for Understanding Risk/Return within the Banking Framework’ Qfinance chapter, exp. Jan 2012

• ‘Investor Requirements for 2011 and beyond: Due diligence and Risk analysis in a post-crisis world’, Euromoney Yearbook chapter

• Workbooks of the Chartered Institute for Securities & Investments (CISI): ‘Derivatives’ (Senior Reviewer), ‘IT in Investment

Operations’, (Senior reviewer), ‘Operational Risk’ , (Senior reviewer) & ‘Risk in Financial Services’, (Technical Reviewer)

• ‘Frontiers of Risk management – Chapter 14: Credit rating agencies and the IRB approach’, Euromoney Book, 2007

• Numerous special, research and criteria reports on Fitch Rating’s website as Performance & Rating analyst, Aug 2004 to Oct 2006

• SAP Risk Analyzer Manual (in-house publication, in German), Jan 2002

Markus Krebsz

• Freelance Consultant with nineteen years experience in banking & financial institutions - thereof ten years covering rating agencies

• Credit rating advisor for the World Bank as part of various large-scale projects involving GSEs of several African & Asian nations

• Industry expert in credit rating agency as well as Structured finance-related issues and frequent speaker on international conferences

• Author and passionate reviewer/editor of several risk workbooks

• Frequent contributor to various industry working groups consulting regulators, exchanges and central banks

Subject matter expert : Rating agencies & Securitisation

• Individually Chartered Member of the Chartered

Securities and Investment Institute (CISI)

• Bachelor of Banking Services and Operations, CCI

• ‘Train the Trainers’ Certificate

• ‘Banking in Britain’ Certificate

• German Banking Certificate (‘Bankkaufmann’)

• Volunteer at and Member of the Professional Risk

Manager’s International Association (PRMIA)

• Member of the Global Association of Risk

Professionals (GARP)

Professional qualifications & affiliations

Publications

• The World Bank

• Deutsche Bank & UBS

• Lloyds Banking Group

• Bank of Scotland Treasury

• The Royal Bank of Scotland Group

• HypoVereinsbank / Unicredit

• Dresdner Bank

• Primary insight (Subsidiary of Bear Stearns)

• De Matteo Monness (Subsidiary of Goldman Sachs)

• Fitch Ratings

• Vista Research (Subsidiary of Standard & Poor’s)

Assignments (Past & current)

www.markuskrebsz.info /www.markuskrebsz.co.uk 37

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