This was presented as part of a Senior Australian Bankers' Master Class held at GCU London on 19 Sept 2012. Dr. Robert Webb was co-presenting on the UK & European Banking system.

1. Risk (R)evolution:
Current Trends & Challenges
in Credit and Operational Risk
Markus Krebsz
19 September 2012, London
Australian Senior Bankers’ Master class programme

2. 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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3. PART 1:
RISK (R)EVOLUTION
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4. CREDIT, MARKET & OPERATIONAL RISKS
(R)evolution?:
Credit Risk  Counterparty Risk
Market Risk  Liquidity Risk
Operational Risk  ERM (Enterprise Risk)
4

5. COUNTERPARTY RISK CHALLENGES
• 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)
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6. CREDIT VALUATION ADJUSTMENT (1)
• 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
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7. CREDIT VALUATION ADJUSTMENT (2)
• 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. 7

8. WRONG-WAY RISK
• 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.
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9. CENTRAL COUNTERPARTY RISK (1)
• 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)
9

10. CENTRAL COUNTERPARTY RISK (2)
• 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)
10

11. 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.
11

12. 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?
12

13. MODEL RISK
• 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
13

14. PRODUCT TAXONOMY (UPIs)
Vanilla Structured Exotic
< 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 …

15. 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

16. PART 2:
CREDIT RATINGS CRASH COURSE
16

17. Q) Who uses credit ratings – and why?
17
Source: www.greenbaypressgazette.com/joeheller

18. Q) How many CRAs exist globally?
18

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

20. Q) D (F) = D (S&P) = D (M)
20

21. RATINGS ‘MAPPING’ TABLE
Fitch Ratings Moody’s Standard & Poors Mapped
Long-term rating Short-term rating Long-term rating Short-term rating Long-term rating Short-term rating internal
r a t i n g
Investment Grade
AAA Aaa AAA iAAA
AA+ F1+ Aa1 AA+ iAA+
A-1+
AA Aa2 P1 AA iAA
AA- Aa3 AA- iAA-
F1+ or F1
A+ A1 A+ A-1 iA+
A F1 A2 P-1 or P-2 A A-1 or A-2 iA
A- F1 or F2 A3 A- iA-
P-2 A-2
BBB+ F2 Baa1 BBB+ iBBB+
BBB F2 or F3 Baa2 P-2 or P-3 BBB A-2 or A-3 iBBB
BBB- F3 Baa3 P-3 BBB- A-3 iBBB-
BB+ Ba1 BB+ iBB+
BB B Ba2 BB iBB
Speculative Grade
B
BB- Ba3 BB- iBB-
Ranges within
B+ B1 B+ iB+
B-1, B-2 and B-3
B B2 B iB
B- B3 Not Prime B- iB-
CCC+ Caa1 CCC+ iCCC+
C
CCC Caa2 CCC iCCC
CCC- Caa3 CCC- C iCCC-
CC Ca CC iCC
C C C iC
DDD, DD, D D
Moody’s: D D D iD
Source: Bloomberg, Fitch, Moody’s and S&P 21

22. 22

23. ANALYTICAL DIFFERENCES
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24. 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?
24

25. STATISTICAL : Probability of Default
25

26. Q) SF Bond - Tranche 1 rated AAA
= SF Bond - Tranche 2 rated AAA? 26

27. ‘SUPER-SENIOR’ RATINGS
SF Bond
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
27

28. Q) How would you define ‘rating’?
A) Benchmark measure B) Benchmark measure
for LGD for PD
C) Opinion
D) Not necessarily based on facts or
knowledge
28

29. 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)
29

30. 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
30

31. RATINGS…
…can capture: …do NOT capture:
Credit risk  Market risk 
 Liquidity risk 
 Operational risk 
only !  Basis risk (IR risk) 
…but, even so, are ’hard-wired’…
• by Basel II
• into banks’ credit rating models
• Investment guidelines and Asset management mandates 31

32. 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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33. 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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34. 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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35. 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

36. 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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37. Markus Krebsz
Subject matter expert : Rating agencies & Securitisation
• 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
Publications
• ‘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
Professional qualifications & affiliations Assignments (Past & current)
• Individually Chartered Member of the Chartered • The World Bank
Securities and Investment Institute (CISI) • Deutsche Bank & UBS
• Bachelor of Banking Services and Operations, CCI • Lloyds Banking Group
• ‘Train the Trainers’ Certificate • Bank of Scotland Treasury
• ‘Banking in Britain’ Certificate • The Royal Bank of Scotland Group
• German Banking Certificate (‘Bankkaufmann’) • HypoVereinsbank / Unicredit
• Volunteer at and Member of the Professional Risk • Dresdner Bank
Manager’s International Association (PRMIA) • Primary insight (Subsidiary of Bear Stearns)
• Member of the Global Association of Risk • De Matteo Monness (Subsidiary of Goldman Sachs)
Professionals (GARP)
• Fitch Ratings
www.markuskrebsz.info /www.markuskrebsz.co.uk • Vista Research (Subsidiary of Standard & Poor’s)
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