(r)isk revolution - current trends and challenges in credit & operational risk
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Risk (R)evolution:
Current Trends & Challenges
in Credit and Operational Risk
Markus Krebsz 19 September 2012, London
Australian Senior Bankers’ Master class programme
1
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?
2
PART 1:
RISK (R)EVOLUTION
3
CREDIT, MARKET & OPERATIONAL RISKS
(R)evolution?:
Credit Risk Counterparty Risk
Market Risk Liquidity Risk
Operational Risk ERM (Enterprise Risk)
4
COUNTERPARTY RISK CHALLENGES
5
• 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)
6
• 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)
7
WRONG-WAY RISK
8
• 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)
9
• 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)
10
• 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.
11
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
MODEL RISK
13
• 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
16
Q) Who uses credit ratings – and why?
Source: www.greenbaypressgazette.com/joeheller
17
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)
20
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
22
ANALYTICAL DIFFERENCES
23
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
Q) SF Bond - Tranche 1 rated AAA
= SF Bond - Tranche 2 rated AAA?
26
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
27
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’?
28
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
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
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’…
31
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
32
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
33
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)
34
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
36
• ‘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
More on Credit ratings and Analytical tools can be found here:
38
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