Measuring and managing systemic risks : An economic perspective of stressed dynamics
Alexis Hamar Moody’s Analytics
Agenda
2
1. Context & Motivations
2. Approaches for measuring systemic Risk
– Network Analysis- interconnectedness
– A Portfolio Model approach with market data
– Macro-Stress Testing/Scenario Analysis approach
3. Plausible/possible mitigation strategies
– Hedging Strategies
– Collateral Management Strategies
4
Context & Motivations
» Financial crisis has revealed that:
– Market, systems and Financial Institutions have reached an unprecented reach of
interconnectedness
– Lack of indicators and to some extent relevant data for measuring and monitoring systemic risks
– Therefore the importance to systemic risks and contagion effects
» Few questions to ponder :
– How much capital would a firm need if we have another financial crisis ?
– How might banks behave (raise lending standards, liquidity shortage,/hoard ,asset prices
decrease) ?
– How much the banking sector could lose if real estate values decline by 20% ?
– What impacts in the financial sector given a possible Sovereign Default ?
Systemic Risk and its contributors
» Can be defined as the risk that shocks affect a significant portion of the financial system
and triggers endogenous adverse situations.
» Credit Counterparty Risk
– CDS and wrong way risk
– Structured Credit Notes
» Contagion Effects:
– Size of interbanks exposures
– Liquidity Mistmatch
– Leverage
» Correlations
– Across firms and asset classes
– Contagion effects with exposure sizes and directions
Source BIS Paper 296
Local/Regional Contagion
» Super Nodes:
– Nodes represent a
state, bank,
Corporate, SME,
Real Estate and
Retail ;
– Nodes link are
weighted based on
exposures
(liabilities)
– Each node actor has
a liquidity buffer and
allocated Tier 1
capital
Local Contagion Example
0
0,01
0,02
0,03
0,04
0,05
0,06
0,07
0,08
0,09
0,1
France Nation Muni Bank Large Corporate SME Real Estate Retail
Impact on French economy conditional to a Greek Default
Joint Default Correlation (Market based) Joint Default Correlation (Rating based)
Source Moody’s Analytics
Portfolio Model
» A portfolio model fed with market risk based data is useful for:
– Identifing and measuring common sources of risk;
– Modeling Conduits of Contagion through a unified correlation structure;
– Measuring Tail Risk effects (Expected Shortfall) and its risk contributors;
– Assessing Funding and Contigency Liquidity;
– Build/Design Collateral Management Strategies
– Hedge riskiest systemic contributors through Optimisation Strategies
Portfolio-Based Systemic Risk Measure
0
0,1
0,2
0,3
0,4
0,5
0,6
0,7
0,8
199701
199705
199709
199801
199805
199809
199901
199905
199909
200001
200005
200009
200101
200105
200109
200201
200205
200209
200301
200305
200309
200401
200405
200409
200501
200505
200509
200601
200605
200609
200701
200705
200709
200801
200805
200809
200901
200905
200909
201001
201005
201009
Portfolio Expected Shortfall Rate (target probability = 10 bps)
100 largest financial firms worldwide
Lehman Failure
Bank stress tests
Dot.com Crash
911 Attack
Asian Financial Crisis
Russian Crisis
Greece downgrade
Lehman Failure
Bank Stress Tests
Greece Downgrade
Bear Stearns Hedge Funds Liquidate
Source Moody’s Analytics
Financial System Has Become Increasingly Concentrated
12
0,4
0,45
0,5
0,55
0,6
0,65
1/1
/97
6/1
/97
11
/1/9
7
4/1
/98
9/1
/98
2/1
/99
7/1
/99
12
/1/9
9
5/1
/00
10
/1/0
0
3/1
/01
8/1
/01
1/1
/02
6/1
/02
11
/1/0
2
4/1
/03
9/1
/03
2/1
/04
7/1
/04
12
/1/0
4
5/1
/05
10
/1/0
5
3/1
/06
8/1
/06
1/1
/07
6/1
/07
11
/1/0
7
4/1
/08
9/1
/08
2/1
/09
7/1
/09
12
/1/0
9
5/1
/10
10
/1/1
0
Concentration Risk
For each monthly portfolio, we select 29 financial institutions with the highest TRCs (Tail
Risk Contribution), compute the sum of their TRCs, then obtain the ratio of the sum to the
corresponding portfolio capital.
Source Moody’s Analytics
Capturing G-SIFIs Systemic Risk
» Treat the 29 G-SIFIS as a portfolio, the portfolio’s tail risk (measures the contribution to the
risk of extreme loss) can be considered as the systemic risk measure as well as Expected
Shortfall
» Take the 29 G-SIFIS worldwide, and the sum of their liabilities is treated as a credit portfolio:
– Their reported liabilities as the exposure size balance sheet, with five year maturity.
– PIT EDF/SIEDF as PDs
– LGD = 100%
– Correlations from Moody’s Analytics Global Correlation Factor Model
– Time period: 1 December 2011
– Analysis performed using Moody’s Analytics Portfolio Model
Ranking G-SIFIS by their systemic risk
0
0,5
1
1,5
2
2,5
3
Ba
rcla
ys
BN
P
Bo
Chin
a
Bo
fA
BP
CE
CA
SA
Citi
Com
me
rzb
an
k
Cre
dit S
uis
se
Deu
tsch
e B
an
k
Dexia
Go
ldm
an
Sa
ch
s
HS
BC
ING
JP
Mo
rga
n
Llo
yd
s T
SB
Mitsu
bis
hi
Miz
uh
o
Mo
rga
n S
tan
ley
New
Yo
rk M
ello
n
Nord
ea
RB
S
Sa
nta
nd
er
So
cge
n
Sta
te S
tree
t
Su
mito
mo
UB
S
Unic
red
it
Wells
Farg
o
Tail Risk Contribution
Expected Shortfall
Source Moody’s Analytics
Forward looking systemic risk measurement and management: Stress Testing & Scenario Analysis
» Objective is to obtain a forward looking measure of systemic risk by simulating various
«states of the world » given transversal macro economic and financial shocks applied at
regional and sectorial level.
» Scenario Analysis based on PD and correlations (R-Squared) sensitivities:
– Forecast Scenario (up to March 2014)
» Correlated macro and financial Variables
– 42 financial and macro Variables
– 10 geographical zones (Australia & New Zealand, Central & South Africa, Eastern Europe & Russia, Japan,
Mexico & South America, Middle East & North Africa, South Asia, South East Asia, US & Canada, Western
Europe
» Combined with « break-up » Scenarios eg (Sovereign Default/Distress) and Major SIFIS
in distress:
» Greece
» Italy
» Ireland
Ranking G-SIFIS by their systemic risk (stressed vs non stressed)
-
0,50
1,00
1,50
2,00
2,50
3,00 D
exia
Ba
nq
ue
RB
S
De
uts
ch
e B
an
k
CA
SA
Co
mm
erz
ba
nk
UB
S
So
cg
en
Ba
rcla
ys
Miz
uh
o
Bo
fA
Un
icre
dit
ING
Mitsu
bis
hi
Llo
yd
s T
SB
Cre
dit S
uis
se
Mo
rga
n S
tan
ley
Su
mito
mo
HS
BC
Go
ldm
an
Sa
ch
s
JP
Mo
rga
n
BN
P
Bo
Ch
ina
Sa
nta
nd
er
No
rde
a
BP
CE
Ne
w Y
ork
Me
llon
We
lls F
arg
o
Citi
Sta
te S
tre
et
Expected Shortfall Rate Tail Risk Contribution
Stressed Expected Shortfall Rate Stressed Tail Risk Contribution
Source Moody’s Analytics
Stressed Sovereign R-Squareds
France
Spain
Germany
Italy
Ireland
Netherlands
Great Britain
Portugal
Belgium
Denmark
Norway
Sweden
Austria
United States
Brazil
China
Greece
Finland
Japan
Canada
Stressed RSQ
RSQ
Source Moody’s Analytics
SIFIS – Greece/Ireland/Italy Cross Asset Correlations
Credit_Suisse BPCE
Commerzbank
Goldman_Sachs
RBS
UBS
Barclays
BofA
BoChina
New_York_Mellon
BNP
Citi
Deutsche_Bank
CASA HSBC ING
JP_Morgan
Lloyds_TSB
Mitsubishi
Mizuho
Morgan_Stanley
Nordea
Santander
Socgen
State_Street
Sumitomo
Unicredit
Wells_Fargo
DexiaBanque
Asset Correlation Greece
Asset Correlation Italy
Asset Correlation Ireland
Source Moody’s Analytics
Mitigating Systemic Risks ?
» Hedging Strategies ?
– Single Name Hedging
» CDS have proved to increase « by nature » systemic risk
» Wrong Way Risk
» CVA Hedging
– Portfolio Tail Risk Hegding - through Optimisation strategies under constraints:
» Economic (available/required budget)
» Risk (limits)
» Liquidity constraints
» Collateral Management Strategies ?
– Segregation of collateral in central repositories
– Haircut Valuation taking into consideration double default
» Central Clearing Counterparties ?
Collateral Management Strategies – Haircut Valuation
» Counterparty Credit Risk can be mitigated through dynamic collateralized strategies
whereby:
– The objective is to obtain a reliable and consistent measure of haircut level, as most often the link
between counterparty and collateral tend to be ignored.
» Haircut Valuation is used in the context of short term borrowing Strategy (stock lending
programs)
– Repos deals;
– ABCP;
» Stressed Haircuts bring value for pricing liquidity and assess liquidity mistmatched
– Balanche sheet resilience
– Funding in distressed markets
22
Collateral Management – Modeling Framework
» At inception, BANK “B” lends two bonds,“B1” and “B3”, to the counterparties Royal
Bank of Scotland and UBS, “C1” and “C2”
» BANK B in exchange receives as a collateral two bonds,“B2” and “B4”, plus an extra
amount set by the haircut level “ht”
» We model and price changes in the credit quality of the bonds, counterparties and
migration effects at a portfolio level during the life of the transaction, which are
incorporated into the haircut values: The borrowers are allowed to default and to
migrate to a different credit quality at any point during the life of the transaction
Business Case – Deal Structure
BANK B
UBS
RBS
Bond 1: XS0289185182
Bond 3: FR0010136382
Bond 2: XS0625359384
Bond 4: XS0231436238
Business Case – Haircuts
Counterparty Collateral ID
Baseline Scenario Stress Testing Scenario
(Counterparty -1 Notch)
Target Probability Target Probability
90% 95% 99% 90% 95% 99%
UBS Bond 2: XS0625359384 3% 6% 8% 6% 9% 14%
RBS Bond 4: XS0231436238 5% 9% 12% 8% 10% 15%
Counterparty Collateral ID
Baseline Scenario Stress Testing Scenario (Collateral
-1 Notch)
Target Probability Target Probability
90% 95% 99% 90% 95% 99%
UBS Bond 2: XS0625359384 3% 6% 8% 7% 11% 16%
RBS Bond 4: XS0231436238 5% 9% 12% 9% 12% 17%
Haircut on collateral assets (ECB source)
Credit Rating Haircut
Aaa to Aa3 3%
Aa3 to Baa3 18%
Below Baa3 Not Eligible Source Moody’s Analytics
Conclusions » Measures of systemic risks need to account for correlation in stressed conditions across firms (and
sovereigns) + contagion effects due to counteparty exposures.
» Unlike many other proposed approaches, the portfolio-based approach uses both market-based
inputs, such as EDF, SIEDF, and non-market-based inputs, e.g., “exposure size,” etc.
» Stressed conditions must be apply to risk parameters as well as to liquidity shocks for capturing actual
and predictive systemic situations.
» Collateral Management strategies bring insight for:
– Risk mitigation (price volatility and counterparty credit risk)
– Funding margining (eg margin calls) and market liquidity mismacthes
» A Redesign requires:
– We need to look at reliable data through model for simulation (stressing) contingent systemic situation;
– Macro – prudential oversight;
– Focus on sources of systemic risks
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