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STRESS TESTING Master Class
Risk Americas 2016
New York City, NY
May 5, 2016
Soner Tunay Head of Risk Analytics Group, Citizens Bank
Disclaimer: The views presented here are not of Citizens’ or its affiliates.
Table of Contents
• Stress Test – A New Modeling and Capital Management Paradigm
• Key Points from CCAR 2015 and Looking into CCAR 2016
• Comparison of Economic Capital and Stress Testing
• Application of Stress Test in Banks
• Scenario Selection and Development
• Retail Modeling Framework
• Wholesale Loss Estimation
• PPNR Modeling
• Interaction of PPNR and Credit Models
• Application for Portfolio Monitoring and Management
2
Stress Test – a new modeling
and capital management
paradigm
3
Risk Weighted Assets in Banks – whose calculation?
Banks’ or regulators?
… regulators are beginning to reverse
themselves, and limit banks’ discretion. The
Federal Reserve, for one, has long been
sceptical of banks’ in-house risk-weighting
efforts. Though American lenders have to
meet RWA-based capital requirements just as
others do, Fed officials seem to set more
store by the fearsome “stress tests” they
carry out each year, to assess how banks
would be affected by a range of hypothetical
setbacks. These tests, beefed up in the
aftermath of the crisis, also use risk models,
but ones that are devised and run by
regulators, not the banks themselves. To
prevent gaming, banks are left in the dark as
to how the models work.
Source: Economist, Sept. 19, 2015
4
What Drives Bank Shareholder Value?
Bank Portfolio Value = Sum of the Values of all Loans (= ASSETS) in the Bank Portfolio
Loan Value depends on the Credit Quality of Borrowers. Lower the Credit Quality, lower the Loan Value.
5
Connecting Portfolio Risk Return to Valuation and
Credit Quality
6
A Brief History of Stress Testing
Stress Testing prior to 2009 SCAP was rather ad-hoc, either concentrated
on a single event or it was model centric
It was rarely on all risk types and never impacted both sides of the
balances sheet. Likewise it was never used as a measure of capital
adequacy.
From Geithner’s recent book “Stress Test”:
“For years, the banks had conducted ad-hoc stress tests built around
rosy scenarios they chose themselves; at the Fed I had pushed for more
rigor and less optimism, but I had never gotten much traction.” “…the
worst outcome considered in their internal stress tests did not even eat
through quarter worth of earnings.”
“There were two parts to the plan. First the Fed would design and
execute a uniform test for the for the largest firms, analyzing the size of
the losses each institution would face in a downturn comparable to the
Great Depression.”
7
SCAP was a Success
8
A Brief History of Stress Testing
During the volatile days of the crisis SCAP was effectively used by the
regulators to calm the markets and recapitalize the banking system
Three part plan was deployed
1. Stop the bleeding
Use Stress Test to determine the amount of capital injections
Public and private funds to buy distressed assets
TALF program extension to revive the credit markets
2. Prevent the future panics
Showing commitment to senior bond holders that Lehman or
WaMu experience would not be repeated
3. Ease the pressure on the victims of the crisis
First time home owner tax credits
Various loan modifications
Principle forgiveness programs, cram-down
9
Stress Test - definition
Definition
By Mayo Clinic Staff
A stress test, also called an exercise stress test, gathers information about how your
heart works during physical activity. Because exercise makes your heart pump
harder and faster than usual, an exercise stress test can reveal problems within
your heart that might not be noticeable otherwise.
An exercise stress test usually involves walking on a treadmill or riding a stationary
bike while your heart rhythm, blood pressure and breathing are monitored.
Your doctor may recommend an exercise stress test if he or she suspects you have
coronary artery disease or an irregular heart rhythm (arrhythmia). The test may
also be used to guide your treatment if you've already been diagnosed with a heart
condition.
10
Stress Test – definition applied to banks
11
Basics of Bank Stress Testing Application
Scenarios
Net Revenue (+) Net Interest Income – Non-Interest Income – Non-Interest Expense
Credit,
Counterparty/Trading,
Operational Risk and others
Losses (-)
Capital Actions
Capital Position
Dividends to stockholders,
Share buy-backs and other capital actions
Post-stress Capital position against the regulatory cushion
12
CCAR Covers both Assets and Liabilities
Balance SheetDate:
2014
Trading Account 50
Other investments -
50
Residential Mortgage 1,200
Consumer Loans 15,340
Commercial and Industrial 20,000
(Loan Loss Allowance) (1,200)
Intangible assets 100
35,440
Deferred income tax 10
Other
10
35,500
Demand Deposits 18,060
Saving Deposits 1,000
19,060
Long-term Debt 3,450
Short-term Debt 2,500
Other
5,950
Common Stock 6,000
Retained earnings 4,490
Other
10,490
35,500
Total current liabilities
Total Debt
Total long-term liabilities
Shareholder's Equity
Total owner's equity
Total Liabilities and Owner's Equity
Total fixed assets
Other Assets
Total Other Assets
Total Assets
Liabilities and Shareholder's EquityTotal Deposits
ABC Bank
AssetsInvestments and Trading Account
Total current assets
Loans
13
Linking CCAR to Capital Actions
14
Linking CCAR to Capital Actions – Case of Citi
“Citi CEO Faces His Biggest Test”, WSJ, June 21, 2014
Its failure to meet the Fed's standards has raised broad questions about its future, including whether
Citigroup—the epitome of a global bank, operating in 100 countries—could face pressures sufficient to
cause it to break up.
Michael Corbat: “If we don’t get this right, we don’t deserve to stay in business.”
15
Linking CCAR to Capital Actions
16
Linking CCAR to Capital Actions – Case of BOA
From Forbes on April 28, 2014, Morgan Stanley analyst Betsy Graseck:
But while the size of the math blunder might not warrant such a big selloff in its shares, the
incident seems to have yet again spooked investors. “Fundamentally, the news today tells us
simply that BAC has $4 billion less excess capital today than what we thought yesterday,”
Citigroup’s Keith Horowitz wrote in a note. “The market appears to be pricing in 1) higher
likelihood of additional one timers to arise in the future and 2) a potential impact on 2015 CCAR
in that BAC will need to be more conservative.” Horowitz doesn’t think today’s move in Bank of
America’s shares is “overdone” short-term, but says the incident “does not materially change our
views on the internal controls at BAC.”
17
Key Points from 2015 CCAR
31 Participating Bank Holding Companies
18
Capital Ratios Summary
19
Capitalization Improved for the Industry as a Whole
20
Capital Consumption
0
5
10
15
20
25
30
35
BB
&T
BB
VA
BM
O
Co
me
rica
Fift
h T
hir
d
Hu
nti
ngt
on
Ke
yCo
rp
M&
T
PN
C
CFG
Re
gio
ns
San
tan
de
r
Sun
Tru
st
MU
FG
U.S
. Ban
corp
Zio
ns
Ban
k o
f A
me
rica
Cit
igro
up
JPM
org
an
We
lls F
argo Ally
Am
eri
can
Exp
ress
BN
Y M
ello
n
Cap
ital
On
e
Dis
cove
r
Go
ldm
an S
ach
s
HSB
C
Mo
rgan
Sta
nle
y
No
rth
ern
Tru
st
De
uts
che
Ban
k
Stat
e S
tre
et
Regional Banks National Banks Other Banks
Tier 1CommonRatio (%)
Bank Holding CompaniesThe capital ratios are calculated using capital action assumptions provided within the Dodd-Frank stress testing rule
Capital Ratios in Severely Adverse ScenarioTier 1 Common Ratio: Actual Q3 2014
Actual 2014:Q3
Ending
Minimum Requirement
Source: Fed DFAST disclosure.
21
Year over Year Comparison
Tier 1 Common Equity Ratios - Actual
0.0%
5.0%
10.0%
15.0%
20.0%
25.0%
30.0%
35.0%
40.0%
BB
&T
BB
VA
Co
mp
ass
BM
O
Co
me
rica
Fift
h T
hir
d
Hu
nti
ngt
on
Ke
yCo
rp
M&
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PN
C
CFG
Re
gio
ns
San
tan
de
r
Sun
Tru
st
MU
FG
U.S
. Ban
corp
Zio
ns
Ban
k o
f A
me
rica
Cit
igro
up
JPM
org
an
We
lls F
argo Ally
Am
eri
can
Exp
ress
BN
Y M
ello
n
Cap
ital
On
e
Dis
cove
r
Go
ldm
an S
ach
s
HSB
C
Mo
rgan
Sta
nle
y
No
rth
ern
Tru
st
De
uts
che
Ban
k
Stat
e S
tre
et
Regional Banks National Banks Other Banks
Tier 1Common
Ratio
Bank Holding CompaniesThe capital ratios are calculated using capital action assumptions provided within the Dodd-Frank stress testing rule
Capital Ratio in Severely Adverse Scenario2014/2015 BHC Comparison: Tier 1 Common Ratio, Actual Q3 2013/2014
BHC 2014
BHC 2015
Median (Regional, 2013)
Median (National, 2013)
Median (Other, 2013)
Median (Regional, 2014)
Median (National, 2014)
Median (Other, 2014)
Source: BHC-specific DFAST disclosures.
22
Year over Year Comparison
Tier 1 Common Equity Ratios - Ending
0.0%
5.0%
10.0%
15.0%
20.0%
25.0%
30.0%
35.0%
40.0%
BB
&T
BB
VA
Co
mp
ass
BM
O
Co
me
rica
Fift
h T
hir
d
Hu
nti
ngt
on
Ke
yCo
rp
M&
T
PN
C
CFG
Re
gio
ns
San
tan
de
r
Sun
Tru
st
MU
FG
U.S
. Ban
corp
Zio
ns
Ban
k o
f A
me
rica
Cit
igro
up
JPM
org
an
We
lls F
argo Ally
Am
eri
can
Exp
ress
BN
Y M
ello
n
Cap
ital
On
e
Dis
cove
r
Go
ldm
an S
ach
s
HSB
C
Mo
rgan
Sta
nle
y
No
rth
ern
Tru
st
De
uts
che
Ban
k
Stat
e S
tre
et
Regional Banks National Banks Other Banks
Tier 1Common
Ratio
Bank Holding CompaniesThe capital ratios are calculated using capital action assumptions provided within the Dodd-Frank stress testing rule
Capital Ratio in Severely Adverse Scenario2014/2015 BHC Comparison: Tier 1 Common Ratio, Ending
BHC 2014
BHC 2015
Median (Regional, 2013)
Median (Regional, 2014)
Median (National, 2014)
Median (National, 2013)
Median (Other, 2014)
Median (Other, 2013)
Source: BHC-specific DFAST disclosures.
23
Year over Year Comparison
Tier 1 Common Equity Ratios - Actual
0.0%
5.0%
10.0%
15.0%
20.0%
25.0%
30.0%
35.0%
40.0%
BB
&T
BB
VA
Co
mp
ass
BM
O
Co
me
rica
Fift
h T
hir
d
Hu
nti
ngt
on
Ke
yCo
rp
M&
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PN
C
CFG
Re
gio
ns
San
tan
de
r
Sun
Tru
st
MU
FG
U.S
. Ban
corp
Zio
ns
Ban
k o
f A
me
rica
Cit
igro
up
JPM
org
an
We
lls F
argo Ally
Am
eri
can
Exp
ress
BN
Y M
ello
n
Cap
ital
On
e
Dis
cove
r
Go
ldm
an S
ach
s
HSB
C
Mo
rgan
Sta
nle
y
No
rth
ern
Tru
st
De
uts
che
Ban
k
Stat
e S
tre
et
Regional Banks National Banks Other Banks
Tier 1Common
Ratio
Bank Holding CompaniesThe capital ratios are calculated using capital action assumptions provided within the Dodd-Frank stress testing rule
Capital Ratio in Severely Adverse Scenario2014/2015 FRB Comparison: Tier 1 Common Ratio, Actual Q3 2013/2014
Fed 2014
Fed 2015
Median (Regional, 2013)
Median (National, 2013)
Median (Other, 2013)
Median (Regional, 2014)
Median (National, 2014)
Median (Other, 2014)
Source: Fed DFAST disclosures.
24
Year over Year Comparison
Tier 1 Common Equity Ratios - Ending
0.0%
5.0%
10.0%
15.0%
20.0%
25.0%
30.0%
35.0%
40.0%
BB
&T
BB
VA
Co
mp
ass
BM
O
Co
me
rica
Fift
h T
hir
d
Hu
nti
ngt
on
Ke
yCo
rp
M&
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PN
C
CFG
Re
gio
ns
San
tan
de
r
Sun
Tru
st
MU
FG
U.S
. Ban
corp
Zio
ns
Ban
k o
f A
me
rica
Cit
igro
up
JPM
org
an
We
lls F
argo Ally
Am
eri
can
Exp
ress
BN
Y M
ello
n
Cap
ital
On
e
Dis
cove
r
Go
ldm
an S
ach
s
HSB
C
Mo
rgan
Sta
nle
y
No
rth
ern
Tru
st
De
uts
che
Ban
k
Stat
e S
tre
et
Regional Banks National Banks Other Banks
Tier 1Common
Ratio
Bank Holding CompaniesThe capital ratios are calculated using capital action assumptions provided within the Dodd-Frank stress testing rule
Capital Ratio in Severely Adverse Scenario2014/2015 FRB Comparison: Tier 1 Common Ratio, Ending
Fed 2014
Fed 2015
Median (Regional, 2013)
Median (Regional, 2014)
Median (National, 2014)
Median (National, 2013)
Median (Other, 2014)
Median (Other, 2013)
Source: Fed DFAST disclosures.
25
Regional Banks Capital Ratios
Medians
Tier 1 Common Ratio (%) Total-Risk Based Capital Ratio (%)
Actual Q3 2014 Ending Minimum Actual Q3 2014 Ending
BHC Model 11.00% 8.60% 8.60% 15..00% 12.40%
Fed Model 11.00% 8.45% 8.40% 14.80% 11.55%
Bank Holding Company
Tier 1 Common Ratio (%) Total-Risk Based Capital Ratio (%)
Actual Q3 2014 Ending Minimum Actual Q3 2014 Ending
BHC Model Fed Model BHC Model Fed Model BHC Model Fed Model BHC Model Fed Model BHC Model Fed Model
BB&T 10.50% 10.50% 7.30% 8.10% 7.30% 8.10% 15.10% 15.20% 10.50% 11.80%
BBVA Compass 11.04% 11.00% 8.51% 6.30% 8.51% 6.30% 13.30% 13.30% 10.31% 8.70%
BMO 11.52% 11.50% 7.31% 9.00% 7.31% 9.00% 15.50% 15.50% 11.06% 10.30%
Comerica 10.60% 10.60% 9.30% 9.00% 9.30% 9.00% 12.80% 12.80% 10.90% 10.50%
Fifth Third 9.60% 9.60% 8.40% 7.90% 8.40% 7.90% 14.30% 14.30% 12.40% 11.50%
Huntington 10.31% 10.30% 7.67% 9.00% 7.67% 9.00% 13.72% 13.70% 10.75% 11.60%
KeyCorp 11.30% 11.30% 9.80% 9.90% 9.40% 9.90% 14.10% 14.10% 12.80% 12.10%
M&T 9.80% 9.80% 9.60% 7.30% 9.10% 7.30% 15.40% 15.40% 13.90% 11.60%
PNC 11.00% 11.00% 10.70% 9.50% 10.70% 9.50% 16.10% 16.10% 13.40% 12.50%
CFG 12.90% 12.90% 11.40% 10.70% 11.30% 10.70% 16.10% 16.10% 14.50% 14.30%
Regions 11.80% 11.80% 9.60% 8.30% 9.60% 8.30% 15.50% 15.50% 13.20% 11.40%
Santander 11.00% 11.00% 7.90% 9.40% 7.90% 9.40% 15.00% 15.00% 12.40% 12.50%
SunTrust 9.60% 9.60% 8.60% 8.20% 8.60% 8.20% 12.30% 12.30% 11.40% 10.80%
MUFG NA 12.70% NA 8.00% NA 8.00% NA 14.60% NA 10.20%
U.S. Bancorp 9.50% 9.50% 7.90% 8.60% 7.90% 8.50% 13.60% 13.60% 11.20% 11.70%
Zions 11.90% 11.90% 8.60% 5.10% 8.60% 5.10% 16.30% 16.30% 12.70% 9.40%
Source: Fed DFAST disclosure and BHC-specific DFAST disclosures.
26
National Banks Capital Ratios
Medians Tier 1 Common Ratio (%) Total-Risk Based Capital Ratio (%)
Actual Q3 2013 Ending Minimum Actual Q3 2013 Ending
BHC Model 11.00% 8.7% 8.4% 15.7% 11.35%
Fed Model 11.00% 7.5% 7.35% 15.7% 10.00%
Bank Holding Company
Tier 1 Common Ratio (%) Total-Risk Based Capital Ratio (%)
Actual Q3 2014 Ending Minimum Actual Q3 2014 Ending
BHC Model Fed Model BHC Model Fed Model BHC Model Fed Model BHC Model Fed Model BHC Model Fed Model
Bank of America 11.30% 11.30% 8.60% 7.40% 8.10% 7.10% 15.80% 15.80% 11.60% 10.40%
Citigroup 13.40% 13.40% 8.70% 8.20% 8.70% 8.20% 17.70% 17.70% 10.50% 9.50%
JPMorgan 10.90% 10.90% 8.70% 6.50% 7.50% 6.50% 15.00% 15.00% 11.10% 9.60%
Wells Fargo 10.80% 10.80% 10.70% 7.60% 9.50% 7.60% 15.60% 15.60% 14.00% 11.10%
Source: Fed DFAST disclosure and BHC-specific DFAST disclosures.
27
Other Bank Capital Ratios
Medians
Tier 1 Common Ratio (%) Total-Risk Based Capital Ratio (%)
Actual Q3 2013 Ending Minimum Actual Q3 2013 Ending
BHC Model 13.95% 13.15% 11.95% 18.451% 14.35%
Fed Model 13.9% 12.40% 12.00% 17.8% 13.6%
Bank Holding Company
Tier 1 Common Ratio (%) Total-Risk Based Capital Ratio (%)
Actual Q3 2014 Ending Minimum Actual Q3 2014 Ending
BHC Model Fed Model BHC Model Fed Model BHC Model Fed Model BHC Model Fed Model BHC Model Fed Model
Ally NA 9.70% NA 7.90% NA 7.90% NA 13.50% NA 11.60%
American Express 13.20% 13.20% 16.30% 15.50% 13.10% 12.60% 15.10% 15.10% 18.60% 17.30%
BNY Mellon 13.90% 13.90% 15.90% 16.00% 13.20% 12.60% 17.00% 17.00% 14.90% 16.50%
Capital One 12.70% 12.70% 11.20% 9.50% 10.40% 9.50% 15.20% 15.20% 14.00% 11.80%
Discover 14.80% 14.80% 13.60% 15.30% 13.10% 13.90% 17.80% 17.80% 15.90% 16.90%
Goldman Sachs 15.20% 15.20% 13.70% 9.90% 12.00% 6.70% 19.80% 19.80% 13.50% 10.00%
HSBC 14.00% 14.00% 7.30% 8.90% 7.30% 8.90% 26.10% 26.10% 14.70% 14.80%
Morgan Stanley 15.00% 15.00% 8.40% 8.80% 8.20% 6.20% 19.90% 19.80% 11.50% 11.30%
Northern Trust 12.80% 12.80% 12.70% 12.40% 11.90% 12.30% 16.00% 16.00% 13.60% 13.60%
Deutsche Bank 36.60% 36.60% 36.90% 34.70% 33.00% 34.70% 37.00% 37.00% 28.00% 29.80%
State Street 13.70% 13.90% 10.80% 14.30% 9.60% 12.00% 19.10% 19.10% 12.90% 13.10%
Source: Fed DFAST disclosure and BHC-specific DFAST disclosures.
28
Loan Losses
31 Participating Bank Companies
29
All Loan Losses
Median for FED model is calculated from banks with both BHC data and FED data
0.0%
2.0%
4.0%
6.0%
8.0%
10.0%
12.0%
14.0%
BB
&T
BB
VA
Co
mp
ass
BM
O
Co
me
rica
Fift
h T
hir
d
Hu
nti
ngt
on
Ke
yCo
rp
M&
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PN
C
CFG
Re
gio
ns
San
tan
de
r
Sun
Tru
st
MU
FG
U.S
. Ban
corp
Zio
ns
Ban
k o
f A
me
rica
Cit
igro
up
JP M
org
an
We
lls F
argo Ally
Am
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can
Exp
ress
BN
Y M
ello
n
Cap
ital
On
e
Dis
cove
r
Go
ldm
an S
ach
s
HSB
C
Mo
rgan
Sta
nle
y
No
rth
ern
Tru
st
De
uts
che
Ban
k
Stat
e S
tre
et
Regional Banks National Banks Other Banks
Percent ofAverageBalances
Bank Holding CompaniesAverage loan balance estimates exclude loans held for investment under the fair-value option and are calculated over nine quarters
Severely Adverse Loan Loss (Percent) for Q4 2014 through Q4 20162014/2015 BHC Comparison: All Loan Losses
BHC 2015
BHC 2014
Median (Regional, BHC)
Median (Regional, FED)
Median (National, BHC)
Median (National, FED)
Median (Other, BHC)
Median (Other, FED)
Ratio: 2014 BHC Model/ 2015 BHC Model
BB&T BBVA
Compass BMO Comerica Fifth Third Huntington KeyCorp M&T PNC RBS Citizens Regions Santander SunTrust MUFG U.S. Bancorp
0.90 1.07 0.78 1.12 1.16 0.84 0.84 1.22 1.19 1.12 1.37 NA 1.15 NA 1.06
Zions Bank of
America Citigroup JP Morgan Wells Fargo Ally
American
Express BNY Mellon Capital One Discover
Goldman
Sachs HSBC Morgan Stanley Northern Trust Deutsche Bank
1.30 1.00 1.15 1.02 1.06 NA NA 0.80 1.01 1.07 0.64 NA 0.93 1.21 NA
State
Street
0.38
30
All Loan Losses – BHC view
Median for FED model is calculated from banks with both BHC data and FED data
31
0.0%
2.0%
4.0%
6.0%
8.0%
10.0%
12.0%
14.0%
BHC2013
BHC2014
BHC2015
Median, BHC2013
Median, BHC2014
Median, BHC2015
Median, FRB2012
Median, FRB2013
Median, FRB2014
Median, FRB2015
Percent of Average Balances
Severely Adverse Loan Loss (Percent) 2013/2014/2015 BHC Comparison: All Loan Losses
Regional Banks
All Loan Losses – FRB view
Median for FED model is calculated from banks with both BHC data and FED data
32
0.0%
2.0%
4.0%
6.0%
8.0%
10.0%
12.0%
FRB2012
FRB2013
FRB2014
FRB2015
Median, BHC2013
Median, BHC2014
Median, BHC2015
Median, FRB2012
Median, FRB2013
Median, FRB2014
Median, FRB2015
Percent of Average Balances
Severely Adverse Loan Loss (Percent) 2012/2013/2014/2015 FRB Comparison: All Loan Losses
Regional Banks
All Loan Losses – BHC vs FRB comparison
Median for FED model is calculated from banks with both BHC data and FED data
33
All Loan Losses – BHC vs Historical comparison
Median for FED model is calculated from banks with both BHC data and FED data
34
First Lien Mortgages, Domestic
Median for FED model is calculated from banks with both BHC data and FED data
0.0%
1.0%
2.0%
3.0%
4.0%
5.0%
6.0%
7.0%
8.0%
9.0%
10.0%B
B&
T
BB
VA
Co
mp
ass
BM
O
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Fift
h T
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d
Hu
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Ke
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M&
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C
CFG
Re
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San
tan
de
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Sun
Tru
st
MU
FG
U.S
. Ban
corp
Zio
ns
Ban
k o
f A
me
rica
Cit
igro
up
JP M
org
an
We
lls F
argo Ally
Am
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can
Exp
ress
BN
Y M
ello
n
Cap
ital
On
e
Dis
cove
r
Go
ldm
an S
ach
s
HSB
C
Mo
rgan
Sta
nle
y
No
rth
ern
Tru
st
De
uts
che
Ban
k
Stat
e S
tre
et
Regional Banks National Banks Other Banks
Percent ofAverageBalances
Bank Holding CompaniesAverage loan balance estimates exclude loans held for investment under the fair-value option and are calculated over nine quarters
Severely Adverse Loan Loss (Percent) for Q4 2014 through Q4 20162014/2015 BHC Comparison: First Lien Mortgage, Domestic
BHC 2015
BHC 2014
Median (Regional, BHC)
Median (Regional, FED)
Median (National, BHC)
Median (National, FED)
Median (Other, BHC)
Median (Other, FED)
Ratio: 2014 BHC Model/ 2015 BHC Model
BB&T BBVA
Compass BMO Comerica
Fifth
Third Huntington KeyCorp M&T PNC RBS Citizens Regions Santander SunTrust MUFG
U.S.
Bancorp
0.85 1.20 0.69 1.43 1.25 1.09 1.59 1.75 2.78 1.17 0.71 NA 1.53 NA 1.55
Zions Bank of
America Citigroup JP Morgan
Wells
Fargo Ally
American
Express BNY Mellon Capital One Discover
Goldman
Sachs HSBC Morgan Stanley Northern Trust
Deutsche
Bank
1.45 1.00 1.56 0.70 1.18 NA NA 3.00 1.00 NA 0.75 0.52 2.00 1.94 NA
State
Street
NA
35
Junior Liens and HELOCs, Domestic
Median for FED model is calculated from banks with both BHC data and FED data
0.0%
5.0%
10.0%
15.0%
20.0%
25.0%
30.0%B
B&
T
BB
VA
Co
mp
ass
BM
O
Co
me
rica
Fift
h T
hir
d
Hu
nti
ngt
on
Ke
yCo
rp
M&
T
PN
C
CFG
Re
gio
ns
San
tan
de
r
Sun
Tru
st
MU
FG
U.S
. Ban
corp
Zio
ns
Ban
k o
f A
me
rica
Cit
igro
up
JP M
org
an
We
lls F
argo Ally
Am
eri
can
Exp
ress
BN
Y M
ello
n
Cap
ital
On
e
Dis
cove
r
Go
ldm
an S
ach
s
HSB
C
Mo
rgan
Sta
nle
y
No
rth
ern
Tru
st
De
uts
che
Ban
k
Stat
e S
tre
et
Regional Banks National Banks Other Banks
Percent ofAverageBalances
Bank Holding CompaniesAverage loan balance estimates exclude loans held for investment under the fair-value option and are calculated over nine quarters
Severely Adverse Loan Loss (Percent) for Q4 2014 through Q4 20162014/2015 BHC Comparison: Junior Liens and HELOCs, Domestic
BHC 2015
BHC 2014
Median (Regional, BHC)
Median (Regional, FED)
Median (National, BHC)
Median (National, FED)
Median (Other, BHC)
Median (Other, FED)
Ratio: 2014 BHC Model/ 2015 BHC Model
BB&T BBVA
Compass BMO Comerica Fifth Third Huntington KeyCorp M&T PNC RBS Citizens Regions Santander SunTrust MUFG U.S. Bancorp
1.52 1.10 0.78 1.11 1.20 0.74 1.61 1.20 1.38 1.00 1.03 NA 1.04 NA 1.34
Zions Bank of
America Citigroup JP Morgan
Wells
Fargo Ally
American
Express BNY Mellon Capital One Discover
Goldman
Sachs HSBC Morgan Stanley Northern Trust
Deutsche
Bank
1.65 0.91 1.34 0.49 0.94 NA NA NA 1.16 NA 0.00 0.71 0.05 1.72 NA
State
Street
NA
36
Commercial and Industrial
Median for FED model is calculated from banks with both BHC data and FED data
0.0%
2.0%
4.0%
6.0%
8.0%
10.0%
12.0%B
B&
T
BB
VA
Co
mp
ass
BM
O
Co
me
rica
Fift
h T
hir
d
Hu
nti
ngt
on
Ke
yCo
rp
M&
T
PN
C
CFG
Re
gio
ns
San
tan
de
r
Sun
Tru
st
MU
FG
U.S
. Ban
corp
Zio
ns
Ban
k o
f A
me
rica
Cit
igro
up
JP M
org
an
We
lls F
argo Ally
Am
eri
can
Exp
ress
BN
Y M
ello
n
Cap
ital
On
e
Dis
cove
r
Go
ldm
an S
ach
s
HSB
C
Mo
rgan
Sta
nle
y
No
rth
ern
Tru
st
De
uts
che
Ban
k
Stat
e S
tre
et
Regional Banks National Banks Other Banks
Percent ofAverageBalances
Bank Holding CompaniesAverage loan balance estimates exclude loans held for investment under the fair-value option and are calculated over nine quarters
Severely Adverse Loan Loss (Percent) for Q4 2014 through Q4 20162014/2015 BHC Comparison: Commercial and Industrial
BHC 2015
BHC 2014
Median (Regional, BHC)
Median (Regional, FED)
Median (National, BHC)
Median (National, FED)
Median (Other, BHC)
Median (Other, FED)
Ratio: 2014 BHC Model/ 2015 BHC Model
BB&T BBVA
Compass BMO Comerica
Fifth
Third Huntington KeyCorp M&T PNC RBS Citizens Regions Santander SunTrust MUFG
U.S.
Bancorp
0.87 1.04 0.53 1.36 1.17 0.57 0.60 1.19 1.15 1.00 1.92 NA 1.33 NA 1.03
Zions Bank of
America Citigroup JP Morgan
Wells
Fargo Ally
American
Express BNY Mellon Capital One Discover
Goldman
Sachs HSBC Morgan Stanley Northern Trust
Deutsche
Bank
1.14 0.79 1.74 1.42 1.00 NA NA 0.81 1.13 NA 0.85 0.71 0.84 0.70 NA
State
Street
NA
37
Commercial Real Estate, Domestic
Median for FED model is calculated from banks with both BHC data and FED data
0.0%
2.0%
4.0%
6.0%
8.0%
10.0%
12.0%B
B&
T
BB
VA
Co
mp
ass
BM
O
Co
me
rica
Fift
h T
hir
d
Hu
nti
ngt
on
Ke
yCo
rp
M&
T
PN
C
CFG
Re
gio
ns
San
tan
de
r
Sun
Tru
st
MU
FG
U.S
. Ban
corp
Zio
ns
Ban
k o
f A
me
rica
Cit
igro
up
JP M
org
an
We
lls F
argo Ally
Am
eri
can
Exp
ress
BN
Y M
ello
n
Cap
ital
On
e
Dis
cove
r
Go
ldm
an S
ach
s
HSB
C
Mo
rgan
Sta
nle
y
No
rth
ern
Tru
st
De
uts
che
Ban
k
Stat
e S
tre
et
Regional Banks National Banks Other Banks
Percent ofAverageBalances
Bank Holding CompaniesAverage loan balance estimates exclude loans held for investment under the fair-value option and are calculated over nine quarters
Severely Adverse Loan Loss (Percent) for Q4 2014 through Q4 20162014/2015 BHC Comparison: Commercial Real Estate, Domestic
BHC 2015
BHC 2014
Median (Regional, BHC)
Median (Regional, FED)
Median (National, BHC)
Median (National, FED)
Median (Other, BHC)
Median (Other, FED)
Ratio: 2014 BHC Model/ 2015 BHC Model
BB&T BBVA
Compass BMO Comerica Fifth Third Huntington KeyCorp M&T PNC RBS Citizens Regions Santander SunTrust MUFG U.S. Bancorp
0.82 0.89 0.90 0.71 0.87 0.82 0.96 1.13 0.93 2.33 2.09 NA 1.25 NA 1.28
Zions Bank of America
Citigroup JP Morgan Wells Fargo Ally American Express
BNY Mellon Capital One Discover Goldman Sachs HSBC Morgan Stanley Northern Trust Deutsche Bank
1.37 1.03 0.38 1.06 1.35 NA NA 1.10 1.04 NA 1.29 1.34 1.86 1.26 NA
State Street
NA
38
Credit Cards
Median for FED model is calculated from banks with both BHC data and FED data
0.0%
5.0%
10.0%
15.0%
20.0%
25.0%
30.0%
35.0%
40.0%
45.0%
50.0%B
B&
T
BB
VA
Co
mp
ass
BM
O
Co
me
rica
Fift
h T
hir
d
Hu
nti
ngt
on
Ke
yCo
rp
M&
T
PN
C
CFG
Re
gio
ns
San
tan
de
r
Sun
Tru
st
MU
FG
U.S
. Ban
corp
Zio
ns
Ban
k o
f A
me
rica
Cit
igro
up
JP M
org
an
We
lls F
argo Ally
Am
eri
can
Exp
ress
BN
Y M
ello
n
Cap
ital
On
e
Dis
cove
r
Go
ldm
an S
ach
s
HSB
C
Mo
rgan
Sta
nle
y
No
rth
ern
Tru
st
De
uts
che
Ban
k
Stat
e S
tre
et
Regional Banks National Banks Other Banks
Percent ofAverageBalances
Bank Holding CompaniesAverage loan balance estimates exclude loans held for investment under the fair-value option and are calculated over nine quarters
Severely Adverse Loan Loss (Percent) for Q4 2014 through Q4 20162014/2015 BHC Comparison: Credit Cards
BHC 2015
BHC 2014
Median (Regional, BHC)
Median (Regional, FED)
Median (National, BHC)
Median (National, FED)
Median (Other, BHC)
Median (Other, FED)
Ratio: 2014 BHC Model/ 2015 BHC Model
BB&T BBVA
Compass BMO Comerica Fifth Third Huntington KeyCorp M&T PNC RBS Citizens Regions Santander SunTrust MUFG U.S. Bancorp
1.15 0.99 0.77 NA 1.03 1.11 1.11 0.86 1.07 0.90 1.32 NA 0.47 NA 0.80
Zions Bank of
America Citigroup JP Morgan
Wells
Fargo Ally
American
Express BNY Mellon Capital One Discover
Goldman
Sachs HSBC Morgan Stanley Northern Trust
Deutsche
Bank
2.58 1.14 1.02 1.19 1.27 NA NA NA 1.02 1.08 NA 0.58 NA NA NA
State
Street
NA
39
Other Consumer
Median for FED model is calculated from banks with both BHC data and FED data
0.0%
5.0%
10.0%
15.0%
20.0%
25.0%
30.0%
35.0%B
B&
T
BB
VA
Co
mp
ass
BM
O
Co
me
rica
Fift
h T
hir
d
Hu
nti
ngt
on
Ke
yCo
rp
M&
T
PN
C
CFG
Re
gio
ns
San
tan
de
r
Sun
Tru
st
MU
FG
U.S
. Ban
corp
Zio
ns
Ban
k o
f A
me
rica
Cit
igro
up
JP M
org
an
We
lls F
argo Ally
Am
eri
can
Exp
ress
BN
Y M
ello
n
Cap
ital
On
e
Dis
cove
r
Go
ldm
an S
ach
s
HSB
C
Mo
rgan
Sta
nle
y
No
rth
ern
Tru
st
De
uts
che
Ban
k
Stat
e S
tre
et
Regional Banks National Banks Other Banks
Percent ofAverageBalances
Bank Holding CompaniesAverage loan balance estimates exclude loans held for investment under the fair-value option and are calculated over nine quarters
Severely Adverse Loan Loss (Percent) for Q4 2014 through Q4 20162014/2015 BHC Comparison: Other Consumer
BHC 2015
BHC 2014
Median (Regional, BHC)
Median (Regional, FED)
Median (National, BHC)
Median (National, FED)
Median (Other, BHC)
Median (Other, FED)
Ratio: 2014 BHC Model/ 2015 BHC Model
BB&T BBVA
Compass BMO Comerica
Fifth
Third Huntington KeyCorp M&T PNC RBS Citizens Regions Santander SunTrust MUFG U.S. Bancorp
0.95 1.34 1.44 0.57 1.71 0.64 1.03 1.07 1.12 1.03 1.09 NA 0.97 NA 1.00
Zions Bank of
America Citigroup JP Morgan
Wells
Fargo Ally
American
Express BNY Mellon Capital One Discover
Goldman
Sachs HSBC Morgan Stanley Northern Trust
Deutsche
Bank
1.75 1.05 0.70 1.36 0.96 NA NA 0.44 1.10 0.94 5.00 0.17 0.50 0.46 NA
State
Street
NA
40
Other Loans
Median for FED model is calculated from banks with both BHC data and FED data
0.0%
1.0%
2.0%
3.0%
4.0%
5.0%
6.0%
7.0%
8.0%
9.0%B
B&
T
BB
VA
Co
mp
ass
BM
O
Co
me
rica
Fift
h T
hir
d
Hu
nti
ngt
on
Ke
yCo
rp
M&
T
PN
C
CFG
Re
gio
ns
San
tan
de
r
Sun
Tru
st
MU
FG
U.S
. Ban
corp
Zio
ns
Ban
k o
f A
me
rica
Cit
igro
up
JP M
org
an
We
lls F
argo Ally
Am
eri
can
Exp
ress
BN
Y M
ello
n
Cap
ital
On
e
Dis
cove
r
Go
ldm
an S
ach
s
HSB
C
Mo
rgan
Sta
nle
y
No
rth
ern
Tru
st
De
uts
che
Ban
k
Stat
e S
tre
et
Regional Banks National Banks Other Banks
Percent ofAverageBalances
Bank Holding CompaniesAverage loan balance estimates exclude loans held for investment under the fair-value option and are calculated over nine quarters
Severely Adverse Loan Loss (Percent) for Q4 2014 through Q4 20162014/2015 BHC Comparison: Other Loans
BHC 2015
BHC 2014
Median (Regional, BHC)
Median (Regional, FED)
Median (National, BHC)
Median (National, FED)
Median (Other, BHC)
Median (Other, FED)
Ratio: 2014 BHC Model/ 2015 BHC Model
BB&T BBVA
Compass BMO Comerica Fifth Third Huntington KeyCorp M&T PNC RBS Citizens Regions Santander SunTrust MUFG U.S. Bancorp
0.76 1.30 1.67 NA 1.57 4.65 0.72 1.18 1.50 0.70 1.94 NA 0.96 NA 1.24
Zions Bank of
America Citigroup JP Morgan
Wells
Fargo Ally
American
Express BNY Mellon Capital One Discover
Goldman
Sachs HSBC Morgan Stanley Northern Trust
Deutsche
Bank
1.32 0.89 1.00 1.09 0.91 NA NA 0.50 1.47 NA 0.62 0.00 0.57 1.06 NA
State
Street
0.46
41
Loan Losses Summary Tables
42
Regional Bank Loan Losses – CCAR 2015
Medians Loan Losses First Lien Mortgages,
Domestic Junior Liens and
HELOCs, Domestic Commercial
and Industrial Commercial Real Estate
Credit Cards
Other Consumer
Other Loans
BHC Model 4.2% 2.0% 3.9% 4.3% 5.7% 15.6% 4.1% 2.85%
Fed Model 5.05% 3.0% 4.95% 4.55% 8.6% 14.1% 4.9% 2.65%
Bank Holding Company
Loan Losses First Lien
Mortgages, Domestic
Junior Liens and HELOCs,
Domestic
Commercial and Industrial
Commercial Real Estate
Credit Cards Other
Consumer Other Loans
BHC Model
Fed Model
BHC Model
Fed Model
BHC Model
Fed Model
BHC Model
Fed Model
BHC Model
Fed Model
BHC Model
Fed Model
BHC Model
Fed Model
BHC Model
Fed Model
BB&T 3.9% 4.6% 2.0% 2.7% 2.9% 3.6% 4.7% 4.1% 4.5% 7.0% 10.0% 13.6% 5.9% 6.0% 2.5% 2.0%
BBVA Compass 4.4% 5.7% 2.0% 2.9% 5.2% 6.8% 5.0% 4.6% 7.3% 12.5% 16.1% 14.4% 4.1% 4.0% 3.0% 1.5%
BMO 6.4% 4.6% 5.8% 3.5% 7.7% 5.0% 9.1% 4.8% 8.7% 7.9% 22.2% 10.7% 1.8% 2.8% 2.7% 3.4%
Comerica 4.2% 4.5% 1.4% 2.6% 5.3% 4.9% 3.3% 3.0% 9.9% 7.8% 0.0% 0.0% 5.1% 7.8% 0.0% 6.6%
Fifth Third 3.7% 5.6% 2.8% 4.4% 3.5% 5.7% 3.5% 5.0% 6.8% 13.2% 15.4% 14.3% 1.4% 2.7% 3.0% 3.4%
Huntington 4.4% 4.2% 3.5% 2.8% 3.9% 4.5% 6.8% 4.0% 5.7% 7.2% 12.5% 14.7% 2.2% 3.2% 1.7% 2.1%
KeyCorp 5.0% 5.0% 1.7% 4.3% 1.8% 4.5% 5.3% 4.0% 6.9% 8.0% 22.2% 12.8% 7.1% 8.8% 4.3% 2.5%
M&T 2.7% 5.2% 0.8% 3.7% 3.5% 6.1% 3.1% 3.8% 3.8% 7.5% 9.9% 14.7% 2.9% 6.2% 1.7% 2.5%
PNC 3.1% 4.7% 0.9% 1.7% 4.5% 3.0% 2.6% 5.7% 5.9% 9.3% 14.8% 12.1% 2.6% 3.2% 0.8% 1.5%
CFG 3.4% 5.1% 1.8% 2.8% 4.7% 7.2% 2.5% 3.9% 2.4% 11.3% 15.8% 12.5% 3.8% 3.4% 3.7% 1.9%
Regions 4.3% 6.9% 5.9% 4.7% 6.8% 6.5% 2.5% 4.8% 4.3% 14.7% 14.6% 13.9% 6.8% 5.8% 1.6% 2.8%
Santander 12.3% 9.6% 4.1% 4.5% 3.1% 4.5% 4.2% 3.6% 3.4% 9.0% 37.9% 14.7% 31.1% 17.2% 3.2% 3.8%
SunTrust 4.1% 4.5% 1.7% 4.0% 7.1% 7.1% 4.5% 4.5% 5.1% 6.9% 43.6% 13.9% 3.0% 3.4% 2.5% 1.5%
MUFG NA 5.0% NA 3.1% NA 4.2% NA 4.8% NA 9.0% NA 0.0% NA 14.7% NA 4.1%
U.S. Bancorp 6.2% 6.5% 2.0% 2.4% 3.5% 5.3% 7.0% 7.8% 7.5% 11.0% 19.5% 14.7% 4.2% 3.4% 4.2% 3.7%
Zions 3.7% 6.5% 1.1% 0.9% 2.3% 4.2% 4.3% 6.8% 4.3% 8.2% 6.2% 14.7% 4.8% 11.6% 3.1% 4.6%
Source: Fed DFAST disclosure and BHC-specific DFAST disclosures.
43
National Bank Loan Losses – CCAR 2015
Medians Loan Losses First Lien Mortgages,
Domestic Junior Liens and
HELOCs, Domestic Commercial
and Industrial Comercial
Real Estate Credit Cards
Other Consumer
Other Loans
BHC Model 4.3% 2.25% 7.7% 2.85% 3.25% 12.45% 3.95% 1.1%
Fed Model 6.1% 3.45% 9.45% 5.65% 8.3% 13.1% 5.15% 3.05%
Bank Holding Company
Loan Losses First Lien
Mortgages, Domestic
Junior Liens and HELOCs,
Domestic
Commercial and Industrial
Commercial Real Estate
Credit Cards Other
Consumer Other Loans
BHC Model
Fed Model
BHC Model
Fed Model
BHC Model
Fed Model
BHC Model
Fed Model
BHC Model
Fed Model
BHC Model
Fed Model
BHC Model
Fed Model
BHC Model
Fed Model
Bank of America 3.6% 4.9% 2.0% 3.1% 6.7% 9.2% 2.8% 3.9% 3.4% 8.3% 11.1% 11.4% 2.0% 2.8% 0.9% 2.1%
Citigroup 6.1% 7.2% 2.5% 4.8% 8.7% 11.5% 2.3% 4.6% 6.1% 9.1% 16.3% 15.0% 16.8% 11.9% 1.3% 2.7%
JP Morgan 5.0% 6.4% 4.6% 3.8% 11.7% 9.7% 3.3% 7.5% 3.1% 6.7% 11.8% 11.0% 2.8% 3.7% 1.1% 4.1%
Wells Fargo 3.2% 5.8% 1.7% 2.9% 6.5% 7.9% 2.9% 6.7% 2.3% 8.3% 13.1% 14.8% 5.1% 6.6% 1.1% 3.4%
Source: Fed DFAST disclosure and BHC-specific DFAST disclosures.
44
Other Bank Loan Losses – CCAR 2015
Medians Loan Losses First Lien Mortgages,
Domestic Junior Liens and
HELOCs, Domestic Commercial
and Industrial Commercial Real Estate
Credit Cards
Other Consumer
Other Loans
BHC Model 3.7% 1.7% 3.9% 5.1% 4.5% 13.85% 4.1% 1.8%
Fed Model 4.9% 3.8% 9.60% 7.6% 9.05% 13.7% 7.4% 3.2%
Bank Holding Company
Loan Losses First Lien
Mortgages, Domestic
Junior Liens and HELOCs,
Domestic
Commercial and Industrial
Commercial Real Estate
Credit Cards Other Consumer Other Loans
BHC Model
Fed Model
BHC Model
Fed Model
BHC Model
Fed Model
BHC Model
Fed Model
BHC Model
Fed Model
BHC Model
Fed Model
BHC Model
Fed Model
BHC Model
Fed Model
Ally NA 5.0% NA 5.4% NA 8.0% NA 4.5% NA 5.1% NA 0.0% NA 5.2% NA 12.7%
American Express 10.5% 9.2% 0.0% 0.0% 0.0% 0.0% 9.4% 9.0% 0.0% 0.0% 11.0% 9.3% 0.0% 14.3% 6.5% 0.0%
BNY Mellon 1.0% 2.3% 0.6% 2.9% 0.0% 9.8% 3.6% 3.3% 5.8% 10.3% 0.0% 0.0% 0.9% 10.6% 0.6% 1.4%
Capital One 8.3% 10.8% 0.4% 2.5% 4.9% 7.5% 4.6% 7.6% 2.3% 6.4% 16.3% 18.5% 5.8% 8.8% 1.7% 3.8%
Discover 10.1% 12.2% 0.0% 5.1% 0.0% 15.0% 0.0% 14.0% 0.0% 31.6% 11.4% 12.7% 5.1% 10.1% 0.0% 4.3%
Goldman Sachs 4.5% 3.2% 2.0% 5.1% 1.7% 9.3% 10.3% 9.8% 3.8% 6.1% 0.0% 0.0% 0.3% 2.7% 2.1% 2.0%
HSBC 7.3% 8.6% 8.6% 12.5% 25.6% 22.3% 5.5% 3.5% 6.5% 9.6% 18.5% 14.7% 4.1% 7.4% 2.6% 2.7%
Morgan Stanley 2.7% 4.0% 0.1% 1.6% 1.9% 9.3% 7.9% 8.0% 4.3% 19.7% NA 0.0% 0.2% 0.7% 2.1% 4.1%
Northern Trust 2.9% 4.9% 1.7% 3.5% 3.9% 13.0% 4.3% 4.0% 5.3% 8.5% 0.0% 0.0% 4.6% 13.1% 1.8% 3.7%
Deutsche Bank 1.3% 4.5% 2.5% 3.8% 0.0% 9.6% 2.5% 9.9% 0.0% 7.9% NA 0.0% 0.0% 2.3% 0.8% 1.4%
State Street 1.6% 3.3% 0.0% 0.0% 0.0% 0.0% 5.1% 4.8% 0.0% 29.4% NA 0.0% 0.0% 0.6% 1.3% 2.7%
Source: Fed DFAST disclosure and BHC-specific DFAST disclosures.
45
9 Quarter Historical Losses (Regional Banks)
9Q Period from Q3-2012 through Q3-2014
BHC Name CRE C&I HELOC &
Junior Lien Resi 1st
Lien Auto Cards TOTAL
CFG -0.02% 0.22% 1.10% 0.82% 0.30% 8.51% 1.15%
BB&T 1.46% 1.14% 1.48% 0.69% 4.11% 5.59% 1.37%
BBVA Compass 0.45% 0.50% 2.39% 0.78% 1.36% 11.11% 0.86%
BMO 0.51% 0.54% 3.23% 2.06% 0.11% 6.76% 1.09%
Comerica -0.03% 0.37% 1.03% 0.05% -4.49% N/A 0.29%
Fifth Third 1.83% 1.00% 2.02% 1.47% 0.40% 8.57% 1.37%
Huntington 1.11% 0.29% 1.91% 0.90% 0.48% 5.42% 0.88%
KeyCorp -0.08% 0.10% 1.26% 0.99% 0.53% 9.57% 0.71%
M&T 0.07% 0.59% 0.49% 0.40% 1.41% 5.27% 0.54%
PNC 0.41% 0.48% 2.31% 0.49% 0.76% 7.69% 0.98%
Regions 0.70% 1.23% 1.96% 2.03% 1.16% 8.11% 1.58%
Santander 0.45% 0.98% 0.84% 2.32% 11.97% 8.53% 3.23%
SunTrust 1.01% 0.51% 2.91% 1.42% 0.74% 6.27% 1.18%
MUFG -0.08% 0.11% 0.38% 0.09% -2.62% N/A 0.07%
U S BC -0.03% 0.66% 1.74% 0.97% 0.36% 8.54% 1.38%
ZIONS BC 0.04% 0.42% 0.54% 0.22% -2.13% 4.68% 0.29%
Source: FR Y-9C.
46
Ratio of FRB Forecast 9 Qtr Losses to last 9 Qtr Actuals –
CCAR 2015
-
10.0
20.0
30.0
40.0
50.0
60.0
70.0
80.0
Ratio of FRB 9 Qtr Forecast Losses to Historical Losses
C&I
HELOC & Junior Lien
Resi 1st Lien
Cards
TOTAL
Source: FR Y-9C and Fed DFAST disclosure.
47
PPNR Results
48
PPNR Results
Median for FED model is calculated from banks with both BHC data and FED data
Ratio: FED Model/BHC Model
BB&T BBVA
Compass BMO Comerica Fifth Third Huntington KeyCorp M&T PNC
Citizens
Financial
Group
Regions Santander SunTrust MUFG U.S. Bancorp
1.63 2.20 2.75 1.29 1.74 1.48 1.55 1.00 1.60 1.50 1.48 1.19 1.88 NA 1.46
Zions Bank of
America Citigroup JPMorgan Wells Fargo Ally
American
Express
BNY
Mellon Capital One Discover
Goldman
Sachs HSBC Morgan Stanley Northern Trust
Deutsche
Bank
1.03 1.15 0.97 0.61 0.86 NA 1.33 3.28 1.16 1.50 0.32 0.24 1.37 2.29 22.22
State Street
3.33
(10.00)
-
10.00
20.00
30.00
40.00
50.00
60.00
(5.00)
-
5.00
10.00
15.00
20.00
25.00
30.00
Billions of Dollars(National Banks)
Billionsof Dollars
Bank Holding CompaniesPre-Provision net revenue includes losses from operational-risk events, mortgage repurchase expenses, and othere real estate owned (OREO) costs
Capital Ratio in Severely Adverse ScenarioFED/BHC Comparison: Pre-Provision Net Revenue
Fed Model
BHC Model
Median (Regional, FED)
Median (Regional, BHC)
Median (Other, BHC)
Median (Other, FED)
Median (National, BHC)
Median (National, FED)
49
PPNR Results
Ratio: FED Adverse/FED Severely Adverse
BB&T BBVA
Compass BMO Comerica Fifth Third Huntington KeyCorp M&T PNC
Citizens Financial
Group Regions Santander SunTrust MUFG U.S. Bancorp
1.21 1.64 2.18 1.65 1.34 1.14 1.32 1.29 1.37 1.23 1.32 1.06 1.40 2.00 1.19
Zions Bank of America
Citigroup JPMorgan Wells Fargo Ally American Express
BNY Mellon
Capital One Discover Goldman
Sachs HSBC
Morgan Stanley
Northern Trust Deutsche
Bank
2.50 1.79 1.75 2.32 1.56 1.10 1.09 1.64 1.06 1.05 7.92 -6.43 4.05 1.50 2.80
State Street
1.56
(13.00)
(3.00)
7.00
17.00
27.00
37.00
47.00
57.00
67.00
77.00
(5.00)
-
5.00
10.00
15.00
20.00
25.00
30.00
Billions of Dollars(National Banks)
Billionsof Dollars
Bank Holding CompaniesPre-Provision net revenue includes losses from operational-risk events, mortgage repurchase expenses, and othere real estate owned (OREO) costs
Capital Ratio in Severely Adverse ScenarioFED Adverse & Severely Adverse Comparison: Pre-Provision Net Revenue
FED Adverse
FED Severely Adverse
Median (Regional, FED)
Median (Regional, BHC)
Median (Other, BHC)
Median (Other, FED)
Median (National, BHC)
Median (National, FED)
Source: Fed DFAST disclosure.
50
Application of Stress Test in
Banks
51
Comparison of EC and ST Uses
52
Purpose of ST Use
53
ST is Still Mostly a Regulatory Requirement
54
Stress Testing Has Not Made Inroads to Strategic
Decision Making
55
Industry is Just About Starting to Compare EC and ST
56
Use of the ST Results Drives the Granularity of the
Analysis
57
Production Cycle of ST Could be Quite Long
58
Reverse Stress Testing
59
Integrated Framework for Stress Testing
PPNR ALLL Capital PlanningBusiness strategy for
future business
Economic
conditions
Product life-
cycle
characteristics
Credit characte-
ristics
Strategic
efforts
- Forecast of revenue by
business segmentProvision levels based on: Capital ratio projections
- Forecast of interest
income by product based
Forecast of net charge-off
rates by product based on:
- Risk appetite
- RWAs
- Profitability
- Pricing StrategyExisting Balances Existing Balances Existing Balances Existing Balances
New Origination or
Purchased Balances
New Origination or
Purchased Balances
New Origination or
Purchased Balances
New Origination or
Purchased Balances
Loan Interest Rate
Weighted Average Life Capital levels
ExpensesCharge-off and recovery
balancesPost-stress buffer
Deliquency balances
Active
Po
rtfolio
Man
agem
en
t
Macroeconom
ic Scenarios
(Regulatory, Internal)
Changes in product attributes due to
seasoning and aging
Underw
riting standards, risk
indicators
Regional expansions, appetite for
growth, general m
arket dynamics of
product, pricing strategies
Stress Testing Conditioning FactorsForecasting Components
Capital Action Plan
Them
esM
etri
cs
Co
mp
ren
he
nsi
ve C
ove
rage
Risk-adjusted return
metrics
An Integrated ST Framework could serve multiple purposes in a Bank, regulatory compliance is just one of them
60
Scenario Selection and
development
61
Fed Releases Three Scenarios for the CCAR Exercise
In the order of
severity, Base,
Adverse and
Severely
Adverse
Severely
Adverse results
are used for
post-stress
capital buffers
House Price Index
62
How Did the CCAR Scenarios Evolved Over the Last Four
Years? Severely Adverse Scenario
For the Severely Adverse Scenario
both the level changes and the
trajectories are very comparable
The starting points are naturally
different reflecting the latest point
of actual observations
Real GDP Growth
Unemployment Rate
63
How Did the CCAR Scenarios Evolved Over the Last Four
Years? Severely Adverse Scenario
For the Severely Adverse Scenario both the level changes
and the trajectories are very comparable
The starting points are naturally different reflecting the
latest point of actual observations
BBB Corporate yield House Price Index
64
How Did the CCAR Scenarios Evolved Over the Last Two
Years? Adverse Scenario
65
Adverse scenario in 2014 had a sharp
rise in the longer term interest rates
aimed at financial system’s capability to
deal with future inflationary concerns
In 2015, both the short term and long
term rates are increasing in the Adverse
scenario.
3 Month Treasury Rate
10-Year Treasury Yield
3-month Treasury Rate under Adverse Scenario –
2015/2016 Comparison
66
10-year Treasury Rate under Adverse Scenario –
2015/2016 Comparison
67
10-year Treasury Rate under Sev. Adverse Scenario –
2015/2016 Comparison
68
Scenario Expansion
• Fed releases 14 or so US macroeconomic variables
• In many instances the Bank models require a larger set of variables
• Fed scenarios need to be translated and expanded using full-
fledged macroeconomic models
• However, full-fledged structural macroeconomic models are
difficult to develop and many Banks rely on vended tools for this
process.
• It is safe to validate vended models using simple benchmark
models built in-house
69
Regionalization of Scenarios – An Example
We developed a simple econometric model can be summarized as a panel regression model
which incorporates the fixed effect of each state or metropolitan area. The marginal effect
of national HPI turns out to be significantly positive, indicating regional house market co-
moved closely with national market.
100
120
140
160
180
200
220
240
260
HPI
2000
q1
2001
q1
2002
q1
2003
q1
2004
q1
2005
q1
2006
q1
2007
q1
2008
q1
2009
q1
2010
q1
2011
q1
2012
q1
2013
q1
2014
q1
2015
q1
2016
q1
2017
q1
2018
q1
National HPI
California HPI
Predicted California HPI
HPI : National vs California
100
120
140
160
180
200
HPI
2000
q1
2001
q1
2002
q1
2003
q1
2004
q1
2005
q1
2006
q1
2007
q1
2008
q1
2009
q1
2010
q1
2011
q1
2012
q1
2013
q1
2014
q1
2015
q1
2016
q1
2017
q1
2018
q1
National HPI
Massachusetts HPI
Predicted Massachusetts HPI
HPI : National vs Massachusetts
70
Scenario Design
• Tie to the risk drivers at the Bank and at the product level
• Indentify the risk drivers through Material Risk Identification process
• Connect risk taking, strategy and profitability
• Risk is in the baseline!
• Develop baseline forecasts that are realistic not inspirational, and
stress scenarios that are reasonable
• Identify the future seeds of instability
• Be active rather than passive in identifying future crisis. 2008 housing
meltdown will not repeat itself at least not in the same fashion!
• Use the scenarios in decision making
71
Scenario Severity How to compare the relative severity of various
scenarios
72
Combining: A Single Measure
• In order to get a sense of the overall severity of a scenario, the various individual severity indicators need to be combined. The following approaches were considered: – Mean severity of each of the indicators in each period.
– Choosing weights based on expert analysis.
– Using principal component analysis (PCA) to determine a single, time-varying measure.
• We currently propose using PCA as a reasonable way to combine the measures.
• The below graph plots the first PC of the five series (scale-adjusted).
73
• The first PC encapsulates about
43% of the variance of the 5 series
we have chosen to include.
• This percentage could be higher if the
number of series included were lower.
• The PC series to the right
demonstrates shows how this
combined severity index changes
over time, both historically and in
the three scenarios.
Combining: A Single Measure
• Alone, this PC series is a time-varying indicator of severity, but if we take the
cumulative difference from baseline (unchanged in Q4 2015), we can get a measure
of severity both at any point in the scenario and at the end.
• According to this measure, the BHC Stress scenario begins the most adverse, but
ends up less severe than both the FRB Severely Adverse and the Great Recession.
74
In Review
• Combining multiple macroeconomic series into a single severity measure is
difficult, even within a single period.
– This task becomes even more difficult when moving to changes across time.
• By normalizing a series and then taking its difference from an unchanged value,
there is the potential to compare series using a single measure.
• PCA is one method through which multiple series can be combined, reducing the
dimensionality of a complex system.
• Further research:
– Determine the appropriate number of series to include in the measure.
– Other time series unobserved component methods may be more appropriate, i.e Kalman Filter, VARMA
75
Retail Modeling Framework
76
Residential
Mortgage
New Originations
Prior Period Loan
Balance
New Non-
Performing Loans
Housing Price Index
Housing Price Index
Scheduled Principal
Payments
Unscheduled
Payoffs
Pull- Through Rate Unemployment
Unemployment
# of Applications
Market Growth
Product Mix Loan Officer Hiring
Portfolio Purchases
/ Sales
Loan Officer
Productivity
Credit Appetite
Existing
Driver
Macro Economic
Factor
Management View
Line Item
Risk Model Driver
Risk Model Driver /
Driver Relationship M1
M2
Strategy
Average New
Loan Size Strategy
Competition
Interest Rates
3 M
Strategy
Product Mix
A1
4
5
6
Pricing
Credit Appetite
4 M#
A#
#
Model
Analytic Process
Judgment
Risk Model M
2 1
A2
Age of Loan
Refinance
Housing Price Index
Interest Rates
4
Residential Mortgage Metrics to be Captured
77
Retail Loss Estimation
78
Segmentation in Retail Models
Segmentation by
- Discrete Variables
such as Product Type, Property Type, Lien Type, Term
- Continuous Variables
such as FICO, LTV, DTI
- Variable of interest, such as Default Behavior should be
analyzed for each one of these segments either using the
prior knowledge or taking it to data directly
79
Segmentation in Retail Models – LTV Example
0
500
00
100
00
01
50
00
02
00
00
02
50
00
0
L2sq
ua
red
dis
sim
ilari
ty m
ea
sure
27
39
54
62
73
89
99
114
130
149
Mortgage LTV Segmentation
0
.05
.1.1
5.2
Life
time
GC
O R
ate
0 50 100 150Orig LTV (%)
Group1 Group2
Group3
Mortgage LTV groups
- Analysis starts with a cluster
analysis
- Then differentiation in segments
are analyzed visually and
statistically
80
Segmentation in Retail Models – FICO Example
-Segmentation is the start of the
retail analysis -Time variation within these
segments and the interaction
between these segments need to be
brought into the model
0
500
00
01
00
00
00
150
00
00
200
00
00
250
00
00
L2sq
ua
red
dis
sim
ilari
ty m
ea
sure
574
597
621
636
656
683
711
726
748
760
777
803
823
838
Mortgage FICO Segmentation
0.2
.4.6
.8
Def
ault
Rat
e
500 600 700 800 900Orig FICO
Group1 Group2
Group3
Mortgage FICO groups
81
Metrics to Estimate
Performing Balances
Change in Balance
New to NPL
(Charge Off)
Change in
utilization
New Origination
Prepayment
=
+
• Future Underwriting Quality
• Macroeconomic Trends
• Strategic Decisions
Loan Term
Coupon Type
Interest Rates
Explanatory Variables
• Interest Rates
• Macroeconomic Trends
• Obligor Characteristics
• Vintage Characteristics
• Credit Quality
• Macroeconomic trends
• Loan Characteristic
• Macroeconomic variables
• Loan Characteristics
• Amortization Type
• Previous Delinquent Accounts
Identifiers
Scheduled
Amortization
FICO, LTV, Loan Term, Coupon Type
Property Type
Pricing –Spread, Competitive Actions
Home Equity Interest Rate over
Prime/Other Credit
Household Indebtedness
Change in Home Prices
•LTV,
•FICO,
•Property Type,
•Loan Term
Spread
Coupon Type
Remaining Time to Maturity
Interest Rate
82
Interdependence of Metrics Estimated
83
Taxonomy of Retail Models
1. Time Series Models
of Charge Offs
3a. Vintage-level
Models
2a. Naïve Roll Rate
Models
Model Output Pros/Cons
4a. Hazard Rate
Models
Sophistic
atio
n a
nd G
ranula
rity
Model Description
• Time series of bank level or
industry level C/O
explained by macro trends
• High-level C/O estimates
• Could be expanded to model
balances
• No product/bank specific
differentiation
• Indifferent to changes in portfolio
quality over time
• Not granular enough to drive credit
decisions
• Repeat the recent transition
probabilities for future
estimations
• Probabilities of being in
various delinquency buckets
including C/O
• Short forecast window
• Repeats the history, no
macroeconomic variables
• Not desirable for stress testing
2b. Macro-enhanced
Roll Rate Models
• Repeat the recent transition
probabilities for future
estimations adding the
benefit of changing
economic environment
• Short forecast window
• Repeats the history
• Macro variables provide a limited lift
3b. Advanced Vintage-
Level Models
4b. State/Transition
Models
• Estimate losses by
age/vintage • Loss rates at the vintage
level
• Portfolio losses would be
the sum of all vintages
• Use vintage origination information to
capture underwriting quality
• Still higher level than the level that is
desired for full risk-profile capture
• Terminal events such as
default or prepayment are
modeled jointly
• Loan level probabilities of
default and prepayment
• Not expandable to modeling the
interim delinquency states
• Logistic regression models
to estimate all possible
states of transitions
• Probabilities of being in
various delinquency buckets
including C/O
• Various macro variables for each
transition state
• Path-dependent models, might require
simulation which could add to
complexity
• Estimate losses by
age/vintage and macro
economic variables
84
Time Series Model of Charge Offs
Source: BHC Y9-C data from FRB Chicago
Same variables can capture the historical performance, but they don’t capture the credit quality
differences
Model for CFG NCO data
Description Coefficient P-value
Unemployment rate 0.00281 0
Constant -0.00875 0.037
Model for BB&T NCO data
Description Coefficient P-value
Unemployment rate 0.00221 0
Constant -0.01156 0
0.0%
0.5%
1.0%
1.5%
2.0%
2.5%
CFG - NCO (2nd Lien)
Actual Fitted
0.0%
0.2%
0.4%
0.6%
0.8%
1.0%
1.2%
BB&T - NCO (2nd Lien)
Actual Fitted
85
Account Level Data
Vintage Unemployment Rate Prime Rate Debt Service Burden CS HPI
Q1 2014 7% 3.25% 10.2 140.1
Combined Data used for model estimation
Sample data for illustration purposes
Vintage Aggregated Data
Vintage Macroeconomic Data
AccountOrigination
DateLien
Origination
Loan Amount Balance FICO LTV State
1 Mar-14 1 50,000$ 50,000$ 750 45 MA
2 Mar-14 1 100,000$ 100,000$ 720 70 PA
3 Mar-14 1 75,000$ 75,000$ 800 65 RI
4 Mar-14 1 45,000$ 45,000$ 825 80 CT
5 Mar-14 1 48,000$ 48,000$ 780 82 NJ
6 Mar-14 1 63,000$ 63,000$ 830 85 NJ
7 Mar-14 1 69,000$ 69,000$ 812 90 NY
8 Mar-14 1 45,000$ 45,000$ 630 81 NY
9 Mar-14 1 50,000$ 50,000$ 740 92 MI
10 Mar-14 1 200,000$ 200,000$ 780 80 MI
11 Mar-14 1 125,000$ 125,000$ 820 75 OH
12 Mar-14 1 110,000$ 110,000$ 690 72 OH
16 Mar-14 1 110,000$ 110,000$ 800 65 MA
13 Mar-14 2 81,000$ 81,000$ 700 91 CT
14 Mar-14 2 84,000$ 84,000$ 675 90 CT
15 Mar-14 2 95,000$ 95,000$ 790 80 RI
Vintage Lien # of AccountsTotal Loan
Amount
Total
BalanceWA FICO WA LTV Mid West
Q1 2014 1 13 1,090,000$ 1,090,000$ 771 75% 31%
Q1 2014 2 3 260,000$ 260,000$ 725 87% 0%
Introduction to Vintage Approach – cont.
86
Anatomy of the Vintage Models
•Charge Off = f( Age Effect,
Vintage Origination Characteristics,
Evolution of the Vintage based on underlying macro factors)
0%
4%
8%
12%
16%
20%
24%
28%
32%
3 9 15 21 27 33 39 45 51 57 63 69 75 81 87 93 99 105
% o
f O
rig
inal
Bala
nce
Months After Issuance
US First Lien Subprime RMBS 60+ Day Delinquencies
1999 2000 2001 2002 2003
2004 2005 2006 2007
•Age Effect: Every vintage follows a
general pattern of increasing defaults
and the default rate comes down as the
vintage matures. This effect is
independent of the origination
characteristics or macro economy
•Vintage Origination Characteristics:
Two factors could impact it. Bank’s
underwriting standards, and the overall
economic conditions
•Evolution of the Vintage: Once the
loans are booked evolution of the
macroeconomic factors would shape the
loss rates
Source: Moody’s Investor Service
87
Dynamics of Vintages Across Different Products
0%
4%
8%
12%
16%
20%
24%
28%
32%
3 9 15 21 27 33 39 45 51 57 63 69 75 81 87 93 99 105
% o
f O
rig
inal
Bala
nce
Months After Issuance
US First Lien Subprime RMBS 60+ Day Delinquencies
1999 2000 2001 2002 2003
2004 2005 2006 2007
Source: Moody’s Investor Service
0%
1%
2%
3%
4%
5%
6%
7%
8%
9%
3 9 15 21 27 33 39 45 51 57 63 69 75 81 87 93 99 105
% o
f O
rig
inal
Bala
nce
Months After Issuance
US Jumbo RMBS 60+ Day Delinquencies
2001 2002 2003 2004
2005 2006 2007
As of 9/13 As of 3/14 As of 3/25/14
Avg. Avg. Number Estimated Realized Bond
Cumulative Pool of Rated Principal Writedowns
Vintage Proj. Loss Factor Tranches $ (mil) % Orig.
2005 3.3% 18.5% 1,873 $554 0.6%
2006 7.3% 21.8% 1,869 $1,687 2.0%
2007 9.8% 26.2% 1,332 $1,658 2.9%
As of 9/13 As of 3/14 As of 3/25/14
Avg. Avg. Number Estimated Realized Bond
Cumulative Pool of Rated Principal Writedowns
Vintage Proj. Loss Factor Tranches $ (mil) % Orig.
2005 18.4% 12.7% 5,590 $20,076 4.8%
2006 38.5% 22.3% 6,486 $66,049 15.3%
2007 48.3% 35.5% 3,049 $29,543 16.6%
88
Examples of Charge Off Models Using Vintage
Approach
(1) (2) (3) (4)
VARIABLES dlq_30_59 dlq_60_89 dlq_90p gco
Constant -8.625*** -9.581*** -11.38*** -12.33***
Age Factors a
Seasonal Dummies b
Unemployment Rate (t) 0.0919*** 0.0984*** 0.190*** 0.230***
Real GDP Growth Rate (t-3) -5.406*** -15.31*** -17.78*** -23.45***
% of the portfolio with a FICO 13.61** 7.12 12.84* 14.90**
Score between 0 and 659
% of the portfolio with a FICO 3.408*** 4.268*** 5.541*** 6.333***
Score between 660 and 759
Delinquency Rate 30_59 (t-1) 32.96***
Delinquency Rate 60_89 (t-1) 15.57*
Delinquency Rate 90 (t-1) -62.27
Annual Growth Rate of Manheim 0.00416
Index (t-3)
Observations 3,765 3,716 3,666 3,665
*** p<0.01, ** p<0.05, * p<0.1
89
Examples of Charge Off Models
In–Sample Fits
0
.005
.01
.015
.02
200
3m1
200
3m7
200
4m1
200
4m7
200
5m1
200
5m7
200
6m1
200
6m7
200
7m1
200
7m7
200
8m1
200
8m7
200
9m1
200
9m7
201
0m1
201
0m7
201
1m1
201
1m7
201
2m1
201
2m7
201
3m1
201
3m7
201
4m1
201
4m7
201
5m1
201
5m7
201
6m1
201
6m7
201
7m1
201
7m7
201
8m1
201
8m7
201
9m1
mtime
Observed Value Fitted Value BL Fitted Value S4
Portfolio Delinquency 30_59($)
0
.001
.002
.003
.004
.005
.006
200
3m1
200
3m7
200
4m1
200
4m7
200
5m1
200
5m7
200
6m1
200
6m7
200
7m1
200
7m7
200
8m1
200
8m7
200
9m1
200
9m7
201
0m1
201
0m7
201
1m1
201
1m7
201
2m1
201
2m7
201
3m1
201
3m7
201
4m1
201
4m7
201
5m1
201
5m7
201
6m1
201
6m7
201
7m1
201
7m7
201
8m1
201
8m7
201
9m1
mtime
Observed Value Fitted Value BL Fitted Value S4
Portfolio Delinquency 60_89($)
0
.0005
.001
.0015
.002
.0025
.003
2003
m1
2003
m7
2004
m1
2004
m7
2005
m1
2005
m7
2006
m1
2006
m7
2007
m1
2007
m7
2008
m1
2008
m7
2009
m1
2009
m7
2010
m1
2010
m7
2011
m1
2011
m7
2012
m1
2012
m7
2013
m1
2013
m7
2014
m1
2014
m7
2015
m1
2015
m7
2016
m1
2016
m7
2017
m1
2017
m7
2018
m1
2018
m7
2019
m1
mtime
Observed Value Fitted Value BL Fitted Value S4
Portfolio Delinquency 90p($)
0
.0005
.001
.0015
.002
2003
m1
2003
m7
2004
m1
2004
m7
2005
m1
2005
m7
2006
m1
2006
m7
2007
m1
2007
m7
2008
m1
2008
m7
2009
m1
2009
m7
2010
m1
2010
m7
2011
m1
2011
m7
2012
m1
2012
m7
2013
m1
2013
m7
2014
m1
2014
m7
2015
m1
2015
m7
2016
m1
2016
m7
2017
m1
2017
m7
2018
m1
2018
m7
2019
m1
mtime
Observed Value Fitted Value BL Fitted Value S4
Portfolio Charge Off Rate
90
Hazard Rate Model – Competing Risk 0
.001
.002
.003
.004
2007m1 2008m1 2009m1 2010m1 2011m1 2012m1 2013m1 2014m1month
fitted default rate actual default rate
In-sample estimated vs. actual default (by month)
0
.005
.01
.015
.02
2007m1 2008m1 2009m1 2010m1 2011m1 2012m1 2013m1 2014m1month
fitted prepay rate actual prepay rate
In-sample estimated vs. actual prepayment (by month)
failure_ml RRR
Std.
Err. z P>|z|
0 base outcome
1
FICO at origination 0.992229 0.000218 -35.49 0.000
LTV at origination 1.007364 0.000697 10.61 0.000
Region #2 indicator 1.351518 0.066409 6.13 0.000
Region #3 indicator 1.313174 0.070319 5.09 0.000
Region #4 indicator 1.449958 0.071644 7.52 0.000
# liens 0.832686 0.04231 -3.6 0.000
New FICO model indicator 0.246942 0.024326 -14.2 0.000
Product #2 indicator 2.203613 0.121558 14.32 0.000
Product #3 indicator 3.220573 0.360186 10.46 0.000
Product #4 indicator 2.784639 0.235112 12.13 0.000
Product #5 indicator 2.168074 0.248832 6.74 0.000
Product #6 indicator 4.751604 0.306457 24.16 0.000
Product #7 indicator 2.197428 0.163452 10.58 0.000
Age 0.999495 0.000521 -0.97 0.333
Unemployment rate 1.094949 0.008037 12.36 0.000
30Y Mortgage - Current coupon (difference) 1.206606 0.014 16.19 0.000
10Y Treasury - 3M T-bill (difference) 1.273308 0.025695 11.97 0.000
Existing home sales (level) 1.324652 0.049342 7.55 0.000
House price index (level) 0.995463 0.000826 -5.48 0.000
2
FICO at origination 1.007237 0.000128 56.78 0.000
LTV at origination 0.988492 0.000313 -36.61 0.000
Region #2 indicator 0.817845 0.017245 -9.54 0.000
Region #3 indicator 1.25287 0.022913 12.33 0.000
Region #4 indicator 0.66607 0.012792 -21.16 0.000
# liens 0.150615 0.005713 -49.91 0.000
New FICO model indicator 1.973376 0.049141 27.3 0.000
Product #2 indicator 1.018685 0.015408 1.22 0.221
Product #3 indicator 0.441167 0.029491 -12.24 0.000
Product #4 indicator 0.524645 0.020887 -16.2 0.000
Product #5 indicator 0.648758 0.029703 -9.45 0.000
Product #6 indicator 1.275247 0.035994 8.61 0.000
Product #7 indicator 0.398586 0.014149 -25.91 0.000
Age 1.014327 0.000271 53.17 0.000
Unemployment rate 1.038116 0.003441 11.29 0.000
30Y Mortgage - Current coupon (difference) 1.231057 0.007126 35.91 0.000
10Y Treasury - 3M T-bill (difference) 1.167407 0.010841 16.67 0.000
Existing home sales (level) 1.063011 0.019011 3.42 0.001
House price index (level) 1.000861 0.000311 2.77 0.006
91
Hazard Rate Model – Competing Risk (con’t) 0
.001
.002
.003
.004
2007m1 2008m1 2009m1 2010m1 2011m1 2012m1 2013m1 2014m1month
fitted default rate actual default rate
In-sample estimated vs. actual default (by month)
0
.005
.01
.015
.02
2007m1 2008m1 2009m1 2010m1 2011m1 2012m1 2013m1 2014m1month
fitted prepay rate actual prepay rate
In-sample estimated vs. actual prepayment (by month)
failure_ml RRR
Std.
Err. z P>|z|
0 (base outcome)
1
FICO at origination 0.99221 0.000719 -10.790 0.000
LTV at origination 1.007155 0.006159 1.170 0.244
Region #2 indicator 1.351658 0.06642 6.130 0.000
Region #3 indicator 1.313261 0.07035 5.090 0.000
Region #4 indicator 1.449862 0.071643 7.520 0.000
# liens 0.833008 0.041499 -3.670 0.000
New FICO model indicator 0.24706 0.02432 -14.200 0.000
Product #2 indicator 2.202742 0.120412 14.450 0.000
Product #3 indicator 3.219313 0.360569 10.440 0.000
Product #4 indicator 2.784095 0.234846 12.140 0.000
Product #5 indicator 2.167501 0.248531 6.750 0.000
Product #6 indicator 4.750969 0.305299 24.250 0.000
Product #7 indicator 2.197102 0.163201 10.600 0.000
Age 0.999492 0.000521 -0.970 0.330
Unemployment rate 1.094962 0.008034 12.360 0.00030Y Mortgage - Current coupon
(difference) 1.206677 0.014235 15.930 0.000
10Y Treasury - 3M T-bill (difference) 1.273285 0.025651 11.990 0.000
Existing home sales (level) 1.324665 0.04932 7.550 0.000
House price index (level) 0.995464 0.000825 -5.490 0.000
LTV x FICO score (at origination) 1.000000 8.8E-06 0.030 0.976
2
FICO at origination 1.00232 0.000361 6.430 0.000
LTV at origination 0.936667 0.003498 -17.520 0.000
Region #2 indicator 0.807076 0.017051 -10.150 0.000
Region #3 indicator 1.24535 0.022638 12.070 0.000
Region #4 indicator 0.659981 0.012675 -21.640 0.000
# liens 0.138548 0.005527 -49.550 0.000
New FICO model indicator 2.007633 0.049885 28.050 0.000
Product #2 indicator 1.026581 0.015426 1.750 0.081
Product #3 indicator 0.442515 0.03015 -11.970 0.000
Product #4 indicator 0.519086 0.020679 -16.460 0.000
Product #5 indicator 0.641221 0.029333 -9.710 0.000
Product #6 indicator 1.272717 0.036271 8.460 0.000
Product #7 indicator 0.396391 0.014047 -26.110 0.000
Age 1.014394 0.000272 53.36 0.000
Unemployment rate 1.037842 0.003432 11.23 0.00030Y Mortgage - Current coupon
(difference) 1.226582 0.007153 35.02 0.000
10Y Treasury - 3M T-bill (difference) 1.167192 0.010829 16.660 0.000
Existing home sales (level) 1.064262 0.019012 3.490 0.000
House price index (level) 1.000728 0.00031 2.350 0.019
LTV x FICO score (at origination) 1.000072 4.92E-06 14.560 0.000
92
Loan Level models Example of loan-level delinquency migration modeling framework with
Monte Carlo simulation
93
Delinquency and
Default
Transition Rate
Models
Prepayment
Transition Rate
Models
New
Originations
Volume
Monte Carlo
Simulation
GCO $ Amount
Calculation
Raw loan-level
and
macroeconomic
data
Cleaned loan-
level and
macroeconomic
data
Recovery Rate
Model
NCO Amount
Calculation
Recovery
Amount
Calculation
Modeling Transition Rates
To (k): 0 dpd 30 dpd 60 dpd 90 dpd
120
dpd
150
dpd Prepay Default
From
(j):
0 dpd 0 0 0 0 0
30 dpd 0 0 0 0
60 dpd 0 0 0 0
90 dpd 0 0 0
120 dpd 0 0 0 0 0
150 dpd 0 0 0 0 0
Prepay 0 0 0 0 0 0 1 0
Default 0 0 0 0 0 0 0 1
94
Modeling Transition Rates, cont’d.
95
Mortgage Transition Matrix Example
To (k): 0 dpd 30 dpd 60 dpd 90 dpd 120 dpd 150 dpd Prepay Default
From
(j):
0 dpd 95.7% 1.8% 0.0% 0.0% 0.0% 0.0% 2.6% 0.0%
30 dpd 41.6% 41.3% 16.8% 0.0% 0.0% 0.0% 0.4% 0.0%
60 dpd 27.7% 17.0% 29.2% 26.1% 0.0% 0.0% 0.0% 0.0%
90 dpd 11.4% 4.9% 17.1% 19.1% 47.5% 0.0% 0.0% 0.0%
120 dpd 0.0% 0.0% 0.0% 5.3% 26.3% 68.3% 0.0% 0.0%
150 dpd 0.0% 0.0% 0.0% 0.0% 2.6% 20.5% 0.0% 76.9%
96
Various approaches to model delinquency transitions
1. Model all relevant cells separately – example separate models for current
to 30 dpd, 30 dpd to 60 dpd, 60 to 90 dpd etc. and leaving the diagonal out
of estimation so each row adds up to 100 percent
2. Model all relevant cells in a row – based on the current state model all
likely states (can be done using something like multinomial logit)
3. Model all cells in a column – based on last month’s state model all likely
states a loan can be this month (can be done by interacting each state with
macroeconomic and policy variables as the drivers could be different)
In the following slide we will see examples of option 1 (modeling all relevant
cells separately)
97
Current to 30 dpd Transition Rate Model
98
30 to 60 dpd Transition Rate Model
99
60 to 90 dpd Transition Rate Model
100
90 to 120 dpd Transition Rate Model
101
120 to 150 dpd Transition Rate Model
102
150 to 180 dpd (default) Transition Rate Model
103
Current to Prepay Transition Rate Model
104
30 dpd to Current Transition Rate Model
105
60 dpd to Current Transition Rate Model
106
Simulation Engine
• The simulation engine combines the predicted transition probabilities generated by the delinquency, default,
and prepayment models to produce states of the mortgage portfolio under the baseline and stressed
macroeconomic scenarios.
• The Model simulates the transition path that each loan in the portfolio takes during the forecast period.
• Depending on the loan-month transition probabilities generated by the delinquency, default, and prepayment
models, a loan can continue to be in good standing, default, or prepay in any period.
• For those loans that defaulted during each simulation, they are flagged, and a monthly GCO is calculated for
each month after it defaults. For each simulation, the GCOs that occurred in each forecast period are
aggregated to generate a portfolio-level GCO for each month.
• For those loans that prepaid during each simulation run, they are flagged, no additional payments are made
during the subsequent forecast periods, and the remaining net book balance (as of the time of prepayment) of
the loan is removed from the simulation.
• The simulation is then repeated several times to generate a distribution of monthly defaults, prepayments,
and GCOs for the portfolio.
107
Examples of Recovery and Repayment Models
$-
$1,000,000
$2,000,000
$3,000,000
$4,000,000
$5,000,000
$6,000,000
20
02
M4
20
02
M1
2
20
03
M8
20
04
M4
20
04
M1
2
20
05
M8
20
06
M4
20
06
M1
2
20
07
M8
20
08
M4
20
08
M1
2
20
09
M8
20
10
M4
20
10
M1
2
20
11
M8
20
12
M4
20
12
M1
2
20
13
M8
20
14
M4
20
14
M1
2
20
15
M8
20
16
M4
20
16
M1
2
20
17
M8
20
18
M4
20
18
M1
2
Recovery
Fitted+Baseline Actual S4
$-
$100,000,000
$200,000,000
$300,000,000
$400,000,000
$500,000,000
$600,000,000
20
02
M4
20
02
M1
2
20
03
M8
20
04
M4
20
04
M1
2
20
05
M8
20
06
M4
20
06
M1
2
20
07
M8
20
08
M4
20
08
M1
2
20
09
M8
20
10
M4
20
10
M1
2
20
11
M8
20
12
M4
20
12
M1
2
20
13
M8
20
14
M4
20
14
M1
2
20
15
M8
20
16
M4
20
16
M1
2
20
17
M8
20
18
M4
20
18
M1
2
Repayment
Fitted+Baseline Actual S4
Prepayment
Variable Description Coefficient
Maturation Spline 1 for prepayment -0.013
Maturation Spline 2 for prepayment -0.041
Prime Rate -0.067
Term (Weighted Average At Origination) -0.006
Vehicle Sales: Cars and Light Truck -0.037
(Base) 1.Monthly Time Seasonality 0.000
Monthly Time Seasonality_i -
Manheim Used Vehicle Value Index, YoY Growth 0.004
Constant -5.007
Repayment
Variable Description Coefficient
Maturation Spline 1 for Repayment 0.032
Maturation Spline 2 for Repayment 0.105
Maturation Spline 3 for Repayment -0.195
Term (Weighted Average At Origination) -0.015
NPL Balance -8.802
Constant -2.915
Recovery
Variable Description Coefficient
Maturation Spline 1 for recovery 0.868
Maturation Spline 2 for recovery -29.483
Maturation Spline 3 for recovery 77.446
Maturation Spline 4 for recovery -49.283
Manheim Used Vehicle Value Index 2.205
Original LTV (Weighted Average) -0.053
Constant -13.516
108
Putting It Altogether
-
200,000,000
400,000,000
600,000,000
800,000,000
1,000,000,000
1,200,000,000
-
5,000,000
10,000,000
15,000,000
20,000,000
25,000,000
30,000,000
35,000,000
20
12
m1
20
12
m3
20
12
m5
20
12
m7
20
12
m9
20
12
m1
1
20
13
m1
20
13
m3
20
13
m5
20
13
m7
20
13
m9
20
13
m1
1
20
14
m1
20
14
m3
20
14
m5
20
14
m7
20
14
m9
20
14
m1
1
20
15
m1
20
15
m3
20
15
m5
20
15
m7
20
15
m9
20
15
m1
1
20
16
m1
20
16
m3
20
16
m5
20
16
m7
20
16
m9
20
16
m1
1
20
17
m1
20
17
m3
20
17
m5
20
17
m7
20
17
m9
20
17
m1
1
20
18
m1
20
18
m3
20
18
m5
20
18
m7
20
18
m9
Baseline Scenario: CFs projections for a vintage of amortizing loans
Gross Charge-off CF Baseline Prepayment CF Baseline
Scheduled Payment of Surving loans Baseline Outstanding Balance Baseline
109
Altogether – cont.
-
200,000,000
400,000,000
600,000,000
800,000,000
1,000,000,000
1,200,000,000
-
5,000,000
10,000,000
15,000,000
20,000,000
25,000,000
30,000,000
35,000,000
20
12
m1
20
12
m3
20
12
m5
20
12
m7
20
12
m9
20
12
m1
1
20
13
m1
20
13
m3
20
13
m5
20
13
m7
20
13
m9
20
13
m1
1
20
14
m1
20
14
m3
20
14
m5
20
14
m7
20
14
m9
20
14
m1
1
20
15
m1
20
15
m3
20
15
m5
20
15
m7
20
15
m9
20
15
m1
1
20
16
m1
20
16
m3
20
16
m5
20
16
m7
20
16
m9
20
16
m1
1
20
17
m1
20
17
m3
20
17
m5
20
17
m7
20
17
m9
20
17
m1
1
20
18
m1
20
18
m3
20
18
m5
20
18
m7
20
18
m9
Baseline Scenario: CFs projections for a vintage of amortizing loans
Gross Charge-off CF Baseline Prepayment CF Baseline
Scheduled Payment of Surving loans Baselibe Outstanding Balance Baseline
-
200,000,000
400,000,000
600,000,000
800,000,000
1,000,000,000
1,200,000,000
-
5,000,000
10,000,000
15,000,000
20,000,000
25,000,000
30,000,000
35,000,000
20
12
m1
20
12
m3
20
12
m5
20
12
m7
20
12
m9
20
12
m1
1
20
13
m1
20
13
m3
20
13
m5
20
13
m7
20
13
m9
20
13
m1
1
20
14
m1
20
14
m3
20
14
m5
20
14
m7
20
14
m9
20
14
m1
1
20
15
m1
20
15
m3
20
15
m5
20
15
m7
20
15
m9
20
15
m1
1
20
16
m1
20
16
m3
20
16
m5
20
16
m7
20
16
m9
20
16
m1
1
20
17
m1
20
17
m3
20
17
m5
20
17
m7
20
17
m9
20
17
m1
1
20
18
m1
20
18
m3
20
18
m5
20
18
m7
20
18
m9
Recession Scenario: CFs projections for a vintage of amortizing loans
Gross Charge-off CF Recession Prepayment CF Recession
Scheduled Payment of Surving loans Outstanding Balance Recession
-
5,000,000
10,000,000
15,000,000
20,000,000
25,000,000
30,000,000
35,000,000
20
12
m4
20
12
m6
20
12
m8
20
12
m1
0
20
12
m1
2
20
13
m2
20
13
m4
20
13
m6
20
13
m8
20
13
m1
0
20
13
m1
2
20
14
m2
20
14
m4
20
14
m6
20
14
m8
20
14
m1
0
20
14
m1
2
20
15
m2
20
15
m4
20
15
m6
20
15
m8
20
15
m1
0
20
15
m1
2
20
16
m2
20
16
m4
20
16
m6
20
16
m8
20
16
m1
0
20
16
m1
2
20
17
m2
20
17
m4
20
17
m6
20
17
m8
20
17
m1
0
20
17
m1
2
20
18
m2
20
18
m4
20
18
m6
20
18
m8
20
18
m1
0
20
18
m1
2
Actual Repayment Baseline Actual Repayment Recession
-
500,000
1,000,000
1,500,000
2,000,000
2,500,000
20
12
m4
20
12
m6
20
12
m8
20
12
m1
0
20
12
m1
2
20
13
m2
20
13
m4
20
13
m6
20
13
m8
20
13
m1
0
20
13
m1
2
20
14
m2
20
14
m4
20
14
m6
20
14
m8
20
14
m1
0
20
14
m1
2
20
15
m2
20
15
m4
20
15
m6
20
15
m8
20
15
m1
0
20
15
m1
2
20
16
m2
20
16
m4
20
16
m6
20
16
m8
20
16
m1
0
20
16
m1
2
20
17
m2
20
17
m4
20
17
m6
20
17
m8
20
17
m1
0
20
17
m1
2
20
18
m2
20
18
m4
20
18
m6
20
18
m8
20
18
m1
0
20
18
m1
2
Prepayment CF Baseline Prepayment CF Recession
110
Altogether – cont.
-
50,000
100,000
150,000
200,000
250,000
300,000
350,000 2
01
2m
4
20
12
m6
20
12
m8
20
12
m1
0
20
12
m1
2
20
13
m2
20
13
m4
20
13
m6
20
13
m8
20
13
m1
0
20
13
m1
2
20
14
m2
20
14
m4
20
14
m6
20
14
m8
20
14
m1
0
20
14
m1
2
20
15
m2
20
15
m4
20
15
m6
20
15
m8
20
15
m1
0
20
15
m1
2
20
16
m2
20
16
m4
20
16
m6
20
16
m8
20
16
m1
0
20
16
m1
2
20
17
m2
20
17
m4
20
17
m6
20
17
m8
20
17
m1
0
20
17
m1
2
20
18
m2
20
18
m4
20
18
m6
20
18
m8
20
18
m1
0
20
18
m1
2
Gross Charge-off CF Baseline Gross Charge-off CF Recession
111
Recap of Retail Modeling Methodology
• Leave the Basel-like models (time-invariant metrics, through the cycle estimates)
behind
• Think of a model that could generate cash flows
• Try to use time series in addition to cross sectional component (same FICO implies
different default probability at different points in time)
• Lifetime loss models are preferable (move away from 12 month horizon)
• Interconnectedness of balance metrics are important, model them jointly
112
Commercial Loss Estimation
113
Loss Estimation for C&I Loans
Outline
• Review of industry practices
– Evaluation of Transition Matrix (TM) approach vs. other
alternatives
– Single Factor Representation of TM
– RBS-Citizens Approach to Dynamic Matrix estimation
• Dynamics of TMs
– How to apply them in Baseline estimation
• Diagonal matrix
• Tilt of a matrix
– How they respond in the stress conditions – what to expect
114
Review of Industry Practices
115
Why TMs? What are the advantages?
• Transition Matrices (TM) are composed of probabilities of moving from
one credit state to another.
• TMs provide intuitive and sound measures of credit risk.
• TMs are input to many other credit risk analysis, ex: portfolio risk
measurement, pricing of bonds and derivatives, regulatory capital
assessment etc.
Sample TM Source: Moody’s
116
Shortcomings of TMs
• TMs change over time and in order to model TMs we need good
amount of data:
- Time span of observations should cover a few economic cycles.
- Each element of the TM requires sufficient amount of firms at each time
point to have a stable TM
• Firms with same risk ratings can behave differently under stress
conditions based on its sector or line of business
• TMs cannot capture the firms entering the cohort versus the existing
firms
• TMs based on Bank’s internal ratings could suffer from
- Change of rating systems or qualitative components
- TTC rating systems that don’t let to build TMs
- PIT rating systems with annual updates that do not allow quarterly
transitions
117
Modeling Transition Matrices
In this presentation we will compare two TM modeling
approaches:
1. Single Factor Approach: Explains dynamics of transitions
with shifting standard normal curves. Commonly used in
the industry*.
2. Citizens Approach: Explicitly models the most important
cells of the TM.
* A One-Parameter Representation of Credit Risk and Transition Matrices
118
Single Factor Approach
1. Map the historical average TM to percentiles of Std Normal Dist.
2. Extract a single factor as a function of time, z(t), that can explain the
deviation of the TM(t) from the average TM.
3. Model z(t) as a function of macro and use z predictions to predict TMs.
Average TM (15x16)
z(t)
119
Single Factor Approach
• Time series of z(t) is obtained by minimizing the
difference between
• the shifted average TM and the TM(t):
2)(
)()(min ttz
zTMtTM
120
Drawbacks of the Single Factor Approach
1. Are transitions aligned with shifts in Normal Dist?
• If so the cut-off points should move in a parallel fashion !!!
2. Aims to model all the
matrix elements with
a single factor.
3. The important part of
the matrix, default
state, is just treated
as any other element
of the TM.
121
Citizens Approach to TM Modeling
• We model the quarterly transitions matrices generated
using external database of PDs with the cohort
approach. PDs are bucketed into 16 Asset Quality (AQ)
Bands, the last band being the default state.
• Model Default, Staying Same, and 1-notch Upgrade &
Downgrade probabilities. On average these represent
90% of the transitions.
Sample
Quarterly TM
(15x16)
122
Time Series of Default Rates by Agency Rating
123
TMs and the Economy
124
Model Specification
• We utilized “Fractional Logit”, i.e., GLM with logit
link (g(.)), and binomial family.
• For the 4 modeled transitions, each row of the TM
will have a different intercept and sensitivity to
Macro Variables.
𝑔 𝐸(𝑦) = 𝛼 + 𝛽𝑖
𝐷
15
𝑖=2
𝐼(𝑟𝑜𝑤 =𝑖) + 𝛽𝑘𝑋𝑘
2
𝑘=1
+ 𝛽𝑖 ,𝑘𝑋 𝐼(𝑟𝑜𝑤 =𝑖)𝑋𝑘 , 𝑦 ~ 𝐵𝑖𝑛𝑜𝑚𝑖𝑎𝑙.
2
𝑘=1
15
𝑖=2
Here I(row=i) is the TM row dummy, and X stands for the macro-economic
variables.
125
Example Fits: Transitions from AQ14
0
.2
.4
.6
.8
1
2000q1 2004q1 2008q1 2012q1 2000q1 2004q1 2008q1 2012q1 2000q1 2004q1 2008q1 2012q1
13 14 15
Graphs by tgt
blue: predicted series
Predicted transitions from AQ14
Upgrade Stay the Same Downgrade
126
Example Fits: Default Probability AQ14
.035
.04
.045
.05
2000q1 2002q1 2004q1 2006q1 2008q1 2010q1 2012q1quarter
Predicted Actual
Default Probabilities for AQ14
127
How to model the rest of the matrix
• Historically the un-modeled elements
account for ~ 10% of the TM most of the
time.
• Use the average the proportion of the
total un-modeled probability for each of
the un-modeled matrix element, ex: if
the remaining probability is 10% for the
1st row, TM[1,3] = 5.8%
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
1 NA NA 58% 18% 7% 7% 3% 4% 2% 1% 1% 0% 0% 0% 0%
2 NA NA NA 47% 15% 14% 6% 9% 3% 2% 1% 1% 1% 0% 0%
3 30% NA NA NA 22% 19% 7% 10% 4% 3% 2% 2% 1% 0% 0%
4 6% 16% NA NA NA 33% 12% 16% 7% 4% 3% 2% 1% 1% 0%
5 2% 5% 37% NA NA NA 15% 21% 8% 5% 3% 3% 2% 1% 0%
: : : : : : : : : : : : : : : :
How un-modeled cells are distributed for each row:
128
TM(model) – TM(actual): Comparison with Single-Factor Model
CFG
As of 2008Q3
Single Factor
Note that this is an in-sample fit; we simply used the actual z(t), i.e., not even modeled
it:
129
Dynamics of TM
Baseline and Stress applications
130
Background on the rest of the Models & Framework
Run-offs
Stress Test
Engine
LGD
New
Origination
Usage
Loss
Emergence
Models
Business
Assumptions
Scenarios
Business Users may
override one or more of
the models output
Balance
Time series
NCOs
Time Series
New NPL
Time series
GCO rate
Default Rate
Time series
Outputs
PD
131
Background on the rest of the Models & Framework
• Major Components:
1. Transition Matrix: Major
components are modeled
explicitly as a function of
macroeconomic variables.
2. LGD and New originations
also change with macro
economy.
3. Usage is modeled as a
function of risk ratings.
4. Runoff and Loss
Emergence are based on
actual data.
Starting Balance (Step 1)
New Orig. Balance (Step 2)
Ending Balance (Step 7)
AQ1
AQ1
AQ2
AQ2
AQ3
AQ3
AQ4
AQ4
Runoff Balance (Step 3)
Migrating Balance (Step 4)
Transition Matrix
(Step 5)
Migrated Balance (Step 6)
AQ1
AQ1
AQ1
AQ2
AQ2
AQ2
AQ3
AQ3
AQ3
AQ4
AQ4
AQ4
New NPL (Step 8)
Expected Loss (Step 9)
Loss Emergence Curve
NCO (Step 10)
Usage Adjustments
132
» LGD model:
LGD: History & Forecast (CCAR 2013)
growth GDP *fLGD
» Data Sources:
CFG loan loss data (2005 to
2012)
2,300 observations
133
» Loss Emergence curves not very sensitive to credit cycle Static curves
for NCOs and defaulted loan pay-offs are based on 2005 2012 data
Loss Emergence: Evolution of NCO
Quarter NCO
1 57.60%
2 16.33%
3 11.18%
4 6.78%
5 3.82%
6 2.06%
7 1.08%
8 0.56%
9 0.29%
10 0.15%
11 0.08%
12 0.08%
Total: 100%
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14
Quarter
NCOs (as % of Total NCOs) Fit
134
New Origination: History & Forecast (CCAR 2013)
» Modeled as a function of unemployment rate and GDP growth.
» Very good ratings (AQ1): Expect to see an increase of
origination in this area (“flight to quality”)
» Medium ratings (AQ2-AQ10): We expect to see a decrease in
new origination in stress.
» Bad ratings (AQ11-AQ13): Very little origination at all times
» Distressed ratings (AQ14-AQ16): No new origination
r
q 1
r
qr
qBalance Total
Balance Originated NewRate NO
q: quarter
r: rating
0%
2%
4%
6%
8%
10%
12%
14%
New Originations AQ2-10
CCAR 13 Actual Fit
135
Model Worst Period (08Q4-10Q4)
9 Qtr Accumulated NCO Rates
Historical 2.61%
CFG Model 2.59%
Z-Factor 3.03%
Overall C&I NCO Backtests
-0.10%
0.00%
0.10%
0.20%
0.30%
0.40%
0.50%
CFG Model C&I NCO Backtests
Historical (C&I Modeled) Backtest2007Q3
Backtest2009Q3 Backtest2011Q1
136
Case study – applying transition matrix approach in the
Baseline estimates
137
How to apply TMs in Baseline Estimation
• For the baseline it is all about the default column
• We converted every TM for very quarter in the model by
keeping the default column and assigning the rest of the
probability mass to the diagonal elements(default or stay the
same)
• For example the first TM now looks like:
AQ Bands 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 Default
1 99.95% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0.05%
2 0% 99.94% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0.06%
3 0% 0% 99.91% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0.09%
4 0% 0% 0% 99.86% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0.14%
5 0% 0% 0% 0% 99.81% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0.19%
6 0% 0% 0% 0% 0% 99.74% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0.26%
7 0% 0% 0% 0% 0% 0% 99.65% 0% 0% 0% 0% 0% 0% 0% 0% 0.35%
8 0% 0% 0% 0% 0% 0% 0% 99.52% 0% 0% 0% 0% 0% 0% 0% 0.48%
9 0% 0% 0% 0% 0% 0% 0% 0% 99.28% 0% 0% 0% 0% 0% 0% 0.72%
10 0% 0% 0% 0% 0% 0% 0% 0% 0% 99.02% 0% 0% 0% 0% 0% 0.98%
11 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 98.64% 0% 0% 0% 0% 1.36%
12 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 98.09% 0% 0% 0% 1.91%
13 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 97.29% 0% 0% 2.71%
14 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 96.20% 0% 3.80%
15 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 95.08% 4.92%
138
Comparison of CCAR 2014 Baseline Results of Regular and
Diagonal TM
• Losses are similar with either the Full TM or Diagonal-only TM for the first year
• Downgrades or upgrades have minimal impact in the short term
• EL is what matters in the short term for baseline scenarios!
… and we let the model run for 9 quarters
139
The Direction of the Baseline TM
How can we measure the impact of the scenarios on the TMs in the Baseline scenarios?
140
The Direction of the Baseline TM
How can we measure the impact of the scenarios on the TMs in the Baseline
scenarios? We took the very first TM (2013Q3) generated in the base line scenario and applied it to a portfolio
balance with $100 in each AQ band. In order to account for the bias that occurs as worst ratings
disappear faster due to their high default probabilities, we have added back the defaulted balance for
each rating after each quarter. The balance weighted average PDs for 10 quarters indicates an
improving portfolio.
AQ Band MidPoint PD Q0 Q 1 Q 2 Q 3 Q 4 Q 5 Q 6 Q 7 Q 8 Q 9 Q 10
1 0.03% 100 102 102 101 100 99 99 98 97 97 97
2 0.23% 100 88 81 77 74 73 73 73 74 75 76
3 0.32% 100 114 125 134 143 150 156 162 168 174 179
4 0.45% 100 113 123 130 137 144 149 155 160 165 170
5 0.64% 100 92 89 90 91 93 96 98 100 103 105
6 0.91% 100 117 124 128 132 135 137 140 143 146 149
7 1.28% 100 83 76 75 75 77 78 80 81 83 84
8 1.81% 100 121 134 144 151 156 161 164 166 168 169
9 2.56% 100 101 103 105 107 108 108 108 108 107 106
10 3.62% 100 104 104 102 100 98 97 95 93 91 90
11 5.12% 100 92 86 83 80 78 76 74 72 70 69
12 7.24% 100 96 93 91 89 88 86 84 81 78 76
13 10.24% 100 102 106 108 108 105 101 96 91 85 80
14 14.48% 100 120 119 110 98 86 76 67 59 53 47
15 28.96% 100 57 34 22 15 11 8 7 6 5 4
PD: 5.2% 4.6% 4.1% 3.8% 3.5% 3.3% 3.1% 3.0% 2.8% 2.7% 2.6%
141
How TMs Respond in Stress Conditions
CCAR 2014 Base and Severely Adverse Scenarios
142
How TMs Respond in Stress Conditions
CCAR 2014 Base and Severely Adverse Scenarios
Sample Transitions under Stress: Net Charge-off Rates
Note that in 2015 Unemployment levels off and GDP growth turns positive
143
Benchmark Models
144
Benchmark Model Summary
145
The C&I benchmark model adopts a top-down stress testing approach in addition to its sophisticated
primary loss forecasting model for CCAR purposes
Bank Inputs to
regulators
FR Y- 14Q
FR Y -14 M/Q
FR Y-14 A
Public Data – FR Y-
9C from regulators
• Basic Financial Data
from BHC
• Consolidated
Balance Sheet,
Income Statement,
supporting schedules
and off-balance
sheet items
Peer Bank FR Y-9C Data
Macro-economic Data
Model Data
Gross Charge-off
Recovery
Balance
Model Data contains the
following for all peer banks
Fed published series
Data aggregation for the top-down approach
Benchmark Model Summary – cont.
146
The C&I benchmark model is simple and yet powerful as it can prevent the submission of very high /
low champion model loss estimates
The parameters of the equation are estimated using seemingly unrelated regression (SUR) method, utilizing GCO rate
information on peer BHCs and their specific macroeconomic drivers in a system of equations. The advantages of the method are:
Makes use of a large pool of information from peer BHCs, in addition to CFG, allowing a more efficient estimation
Allows each BHC to have its individual specification, allowing greater flexibility
Recognizes the interconnectedness (correlations) between peer BHCs in the estimation
It is easy to interpret and allows comparisons across peer BHCs
Gross Charge-
off Rate Recovery Rate + Net Charge-off
Rate
Scenario Specific
Forecasts
Balance Scenario Specific Forecasts
from PPNR
X
$ Loss Amount
Model Component (in-scope for EP) Leveraged from PPNR
Public Data
Historical Gross-
Charge off
+
Macro-economic
Variables
Schematics for loss forecasting
0%
1%
2%
3%
4%
5%
6%
7%
8%
2008 2009 2010 2011 2012 2013 2014
Citizens has lower historical C&I losses than peers and similar asset
quality; we would expect fairly similar to slightly lower loss rates
Similar historical losses
C&I 9-Qtr NCO rates, year end1
1. FRED database from FRB of St. Louis. Peers included in the analysis are: BB&T, Comerica, Fifth Third, Key, M&T, PNC, Regions, Sun Trust, and US Bank
2. OCC, “Large Bank Commercial Credit Trends 1Q15”
3. Leveraged lending and ABL are15% of commercial exposures. Denominator is total leveraged lending or total ABL. NPLs not available for total commercial exposure
CFG
Regional bank peer
3
3 CFG 1.4% 2.6% 3.2% 1.7% 0.8% 0.5% 0.2%
Median 1.4% 2.7% 3.4% 2.7% 1.5% 0.9% 0.8%
CFG NCOs are an average of 28% lower than the median
147
Model Fit – GCO C&I
148
Following charts depicts the in-sample model fit of the Predicted Gross Charge-Off Rates for CFG
0.5
11
.52
2.5
An
n. G
CO
ra
te, %
2002q1 2004q1 2006q1 2008q1 2010q1 2012q1 2014q1quarter
Historical GCO rate CFG Fit GCO rate CFG (Static)
C&I GCO rate Model CFG
GCO Ratet = f(GDP YoY Growth Rate, Lagged GCO Rates)
Estimated as one of the relationships in a system of seemingly unrelated regressions
Explanatory Factor Trends - GCO C&I
149
Following charts depict the comparative trend of probable explanatory macroeconomic factors with
CFG Gross Charge-Off Rates
12
34
56
BB
B S
pre
ad
0.5
11
.52
CF
G A
nn. G
CO
rate
, %
2002q1 2004q1 2006q1 2008q1 2010q1 2012q1 2014q1
CFG Ann. GCO rate BBB Spread
BBB Spread, %
-4-2
02
4
GD
P Y
oY
Gro
wth
0.5
11
.52
CF
G A
nn. G
CO
rate
, %
2002q1 2004q1 2006q1 2008q1 2010q1 2012q1 2014q1
CFG Ann. GCO rate GDP YoY Growth
GDP YoY Growth, %
-10
12
34
YoY
Change in U
nem
plo
ym
ent ra
te, L2
0.5
11
.52
CF
G A
nn. G
CO
rate
, %
2002q1 2004q1 2006q1 2008q1 2010q1 2012q1 2014q1
CFG Ann. GCO rate YoY Change in Unemployment rate, L2
YoY Change in Unemployment rate, %
Model Fit – GCO C&I – Peer Banks
150
Following are the Gross Charge-Off Rates in-sample model fits for CFG and its peer banks
0.5
11
.52
2.5
2002q1 2004q1 2006q1 2008q1 2010q1 2012q1 2014q1
Historical GCO rate CFG Fit GCO rate CFG (Static)
C&I GCO rate Model CFG
0.5
11
.52
2.5
2002q1 2004q1 2006q1 2008q1 2010q1 2012q1 2014q1
Historical GCO rate MnT Fit GCO rate MnT (Static)
C&I GCO rate Model MnT
01
23
45
2002q1 2004q1 2006q1 2008q1 2010q1 2012q1 2014q1
Historical GCO rate Key Fit GCO rate Key (Static)
C&I GCO rate Model Key
0.5
11
.52
2002q1 2004q1 2006q1 2008q1 2010q1 2012q1 2014q1
Historical GCO rate BBT Fit GCO rate BBT (Static)
C&I GCO rate Model BBT0
12
34
2002q1 2004q1 2006q1 2008q1 2010q1 2012q1 2014q1
Historical GCO rate Com Fit GCO rate Com (Static)
C&I GCO rate Model Com
02
46
2002q1 2004q1 2006q1 2008q1 2010q1 2012q1 2014q1
Historical GCO rate Fif Fit GCO rate Fif (Static)
C&I GCO rate Model Fif
01
23
2002q1 2004q1 2006q1 2008q1 2010q1 2012q1 2014q1
Historical GCO rate PNC Fit GCO rate PNC (Static)
C&I GCO rate Model PNC
01
23
4
2002q1 2004q1 2006q1 2008q1 2010q1 2012q1 2014q1
Historical GCO rate Sun Fit GCO rate Sun (Static)
C&I GCO rate Model Sun
0.5
11
.52
2002q1 2004q1 2006q1 2008q1 2010q1 2012q1 2014q1
Historical GCO rate USB Fit GCO rate USB (Static)
C&I GCO rate Model USB
Model Forecasts – CFG C&I Net Charge-Off
151
Following depicts the GCO Rate and NCO rate and their corresponding forecasts under the CCAR 2015
specific baseline, adverse and severely adverse Fed scenarios
0.5
11
.52
2.5
An
n. N
CO
/GC
O r
ate
, %
2002q1 2006q1 2010q1 2014q1 2018q1quarter
Historical NCO rate CFG Historical GCO rate CFG
NCO rate ccar15_Base GCO rate ccar15_Base CFG
NCO/GCO rate CFG ccar15_Adverse NCO/GCO rate CFG ccar15_SevAdv
C&I GCO rate vs NCO rate CCAR 2015 CFG
Projected First 9-Qtrs
Rate, %
CCAR 2015 Sev. Adverse
NCO/GCO 2.97
Historical Worst 9-Qtrs
NCO Rate, %
3.2 2008Q4-
2010Q4
Contributing Factor Forecasts – CFG C&I
152
The following are the forecast trends for the GCO rate and the key macroeconomic variable
0.5
11
.52
2.5
Ann. G
CO
rate
, %
2002q1 2006q1 2010q1 2014q1 2018q1quarter
Historical GCO rate CFG GCO rate ccar15_Base CFG
GCO rate ccar15_Adverse CFG GCO rate ccar15_SevAdv CFG
C&I Ann. GCO CCAR 2015 CFG (SUR)
-4-2
02
4
2002q1 2006q1 2010q1 2014q1 2018q1quarter
Historical ccar15 Base
ccar15 Adverse ccar15 SevAdv
GDP YoY Growth and CCAR Predictions
C&I – Historical & Forecasts GCO
0.00%
1.00%
2.00%
3.00%
4.00%
5.00%
6.00%
7.00%
2002q1 2003q1 2004q1 2005q1 2006q1 2007q1 2008q1 2009q1 2010q1 2011q1 2012q1 2013q1 2014q1 2015q1 2016q1 2017q1 2018q1 2019q1
CnI Historical & Forecasts GCO Rate - FRB 2016 Sev. Adverse Scenario
The numbers reported in the plot are the worst 9-quarter GCO rate. The vertical indicates the start of recession.
3.39%
7.41%
6.65%
3.93%
2.71%
2.17%
153
PPNR Modeling
154
PPNR Results from 2015 CCAR
155
2015 CCAR PPNR Results
Median for FED model is calculated from banks with both BHC data and FED data
(10.00)
-
10.00
20.00
30.00
40.00
50.00
60.00
(5.00)
-
5.00
10.00
15.00
20.00
25.00
30.00
Billions of Dollars(National Banks)
Billionsof Dollars
Bank Holding CompaniesPre-Provision net revenue includes losses from operational-risk events, mortgage repurchase expenses, and othere real estate owned (OREO) costs
Capital Ratio in Severely Adverse ScenarioFED/BHC Comparison: Pre-Provision Net Revenue
Fed Model
BHC Model
Median (Regional, FED)
Median (Regional, BHC)
Median (Other, BHC)
Median (Other, FED)
Median (National, BHC)
Median (National, FED)
FRB PPNR estimates are higher than BHC’s
156
PPNR Results
(13.00)
(3.00)
7.00
17.00
27.00
37.00
47.00
57.00
67.00
77.00
(5.00)
-
5.00
10.00
15.00
20.00
25.00
30.00
Billions of Dollars(National Banks)
Billionsof Dollars
Bank Holding CompaniesPre-Provision net revenue includes losses from operational-risk events, mortgage repurchase expenses, and othere real estate owned (OREO) costs
Capital Ratio in Severely Adverse ScenarioFED Adverse & Severely Adverse Comparison: Pre-Provision Net Revenue
FED Adverse
FED Severely Adverse
Median (Regional, FED)
Median (Regional, BHC)
Median (Other, BHC)
Median (Other, FED)
Median (National, BHC)
Median (National, FED)
Source: Fed DFAST disclosure.
FRB PPNR estimates are sensitive to scenarios
157
Concepts and terminology
158
Role of PPNR in Stress Testing Application
Scenarios
Net Revenue (+) Net Interest Income + Non-Interest Income – Non-Interest Expense
Credit,
Counterparty/Trading,
Operational Risk and others
Losses (-)
Capital Actions
Capital Position
Dividends to stockholders,
Share buy-backs and other capital actions
Post-stress Capital position against the regulatory cushion
159
Supervisory ST Results For all CCAR Banks
160
Waterfall of Capital Consumption
2015 Citi Example
161
I. Complexities in PPNR
Modeling
162
Credit vs. PPNR Modeling
Credit Loss Models
PPNR Models
Losses are largely impacted by your own
portfolio/borrower behavior and economy
- Voluntary prepayment and default
Various Drivers both internal and external
-- Customer behavior (prepay, line utilization, NPL)
-- Economy (HPI, GDP)
-- Market Rates (LIBOR, prime rate, spreads)
-- Competitors’ behavior (market share, new products)
PBE or DSGE models are not the solution!
163
Some Questions PPNR Framework Should Address
Assets Liabilities
Deposits
Traded Assets
Assets For Sale
Asset Volumes
- Loan and trading asset mix
- Mix of new and exiting business
- Expansion/contraction of the business
Loans
Goodwill
Equity
Short Term
Debt
Long Term Debt
Funding
- Funding mix (Debt vs. Equity)
- Liquidity considerations
- Stickiness of the indeterminate maturity deposits
Income and Expense
- Yields on loans
- Fee and trading income on the non-lending assets
- Interest expense (both Deposit and Debt funding)
164
How to Classify the Drivers of PPNR
Metrics to be Modeled Decision Point
Resources/Employees
Operating Expenses
One-time Expenses
New Business
Volume and
Rates
Mix of new and existing assets/loans
Type of loans/trades
Term of the loans
Interest rate type
Credit quality
Balances
Yields
Fee Income
Trading Income
Debt/Equity
ST/LT
Debt/Deposits
Non-interest
Expenses
Funding
1
3
2
Mix of Debt/Equity, Retail/Wholesale funding
Availability of each option
Interest expense of each option
Capital considerations
Employee allocation over new
origination/workout
Retention of key employees
Expansion/contraction of business lines/products
Business/asset sales
Determination of core business/assets
165
Components of PPNR Models
PPNR components
Related to Income
Net interest
income
Drivers
Non-
interest
revenue
Non-
interest
expenses
• New loan origination volume
• Taxonomy of fee generating activity
• Customers with fee-based products
Number of fee
generating events
Average fee per event • Historical fee trends and strategic
decisions to raise or lower fees
1
2
3
• New loan origination volume
• Run-off rates (amortization, prepayment)
• Non-performing balances Asset balances 1a
Sub-drivers Further Details
• Separate models for each component
• New origination volumes depends on market
size and bank’s share
• Existing balances depend on contractual
maturity and pre-payment rate
• Deposit balances
• Wholesale funding
Deposit / funding
balances 1c • Separate models for retail and wholesale
liabilities and type of deposit (term vs. non-
maturity)
• Dependence on wholesale funding
• Interest expense of deposits
• Interest cost of wholesale liabilities Deposit / funding costs 1d
• Estimate based on contractual rates for term
deposits and assumptions of spreads to risk-
free rate (T-bill) based on the scenario
• Contractual interest rates
• Fixed vs. variable rates Earning asset yields 1b
• Existing portfolio: ALM systems are equipped
to track contractual rates
• New originations: Estimate based on the
scenario and strategy
• Separate models/assumptions for each fee or
revenue type (e.g., late fees, transaction fee
income, trading fees, deal volumes)
• Compensation
• Non-comp expenses
• Property
• IT
• Internal /External
services
• Compensation based on FTE plus
incentives
• Top-down expense estimates for fixed line
items (e.g., occupancy, external services)
• Other expenses based on estimated levels
of activities (e.g., numbers of transactions,
customers)
• Often rules-based, following centrally-defined
principles (e.g., greater reduction of
discretionary expenses, compensation
guidelines depending on pro forma year-end
performance)
• Ability to reduce expenses in stress periods
• Need to increase certain activities in
downturns (collection, risk management)
2a
2b
166
Granularity of the Analysis Example of Balance Models
High Level Models of
Balance
Total Balance Models
More Granular Models
of Balances
Separate Components
-- Easier to gather data, substitute industry data for internal
-- Easier to fit, follows the industry/macro trends
-- May not fit for every bank/product
-- Breakdown by components of Balances (prepayment,
amortization, utilization, charge off, new origination)
-- Further breakdown by product
-- Ability to model the spread, yield, volume at the product
level
-- More suitable for business use
-- Important to separate new origination from existing/allows
strategy analysis
-- Some components might be hard to fit at a granular level
(i.e. change in line utilization)
-- All the components must be estimated jointly
167
II. Prioritization of which PPNR
metric to model first
168
Balances – Comparison Across Banks
0
20
40
60
80
100
120
140
160
180
20
01
Q1
20
01
Q4
20
02
Q3
20
03
Q2
20
04
Q1
20
04
Q4
20
05
Q3
20
06
Q2
20
07
Q1
20
07
Q4
20
08
Q3
20
09
Q2
20
10
Q1
20
10
Q4
20
11
Q3
20
12
Q2
20
13
Q1
20
13
Q4
20
14
Q3
20
15
Q2
20
16
Q1
20
16
Q4
Bill
ion
s o
f D
olla
rs
CFG Total Assets & Total Loan Balances
recession
Total Assets
FED Total Assets Adverse
FED Total Assets Severely Adverse
Total Loan Balances
FED Total Loan Adverse
FED Total Loan Severely Adverse
BHC Total Assets Severely Adverse
BHC Total Loan Severely Adverse
-26.86%
2004 Charter One acq.
0
50
100
150
200
250
20
01
Q1
20
01
Q4
20
02
Q3
20
03
Q2
20
04
Q1
20
04
Q4
20
05
Q3
20
06
Q2
20
07
Q1
20
07
Q4
20
08
Q3
20
09
Q2
20
10
Q1
20
10
Q4
20
11
Q3
20
12
Q2
20
13
Q1
20
13
Q4
20
14
Q3
20
15
Q2
20
16
Q1
20
16
Q4
Bill
ion
s o
f D
olla
rs
BB&T Total Assets & Total Loan Balances
recession
Total Assets
FED Total Assets Adverse
FED Total Assets Severely Adverse
Total Loan Balances
FED Total Loan Adverse
FED Total Loan Severely Adverse
BHC Total Assets Severely Adverse
BHC Total Loan Severely Adverse
2009 Colonial Bank of Montgomery acq.
0
20
40
60
80
100
120
140
20
01
Q1
20
01
Q4
20
02
Q3
20
03
Q2
20
04
Q1
20
04
Q4
20
05
Q3
20
06
Q2
20
07
Q1
20
07
Q4
20
08
Q3
20
09
Q2
20
10
Q1
20
10
Q4
20
11
Q3
20
12
Q2
20
13
Q1
20
13
Q4
20
14
Q3
20
15
Q2
20
16
Q1
20
16
Q4
Bill
ion
s o
f D
olla
rs
BMO Total Assets & Total Loan Balances
recession
Total Assets
FED Total Assets Adverse
FED Total Assets Severely Adverse
Total Loan Balances
FED Total Loan Adverse
FED Total Loan Severely Adverse
BHC Total Assets Severely Adverse
BHC Total Loan Severely Adverse-34.58%
2010 Marshall & Ilsely acq.
0
10
20
30
40
50
60
70
80
20
01
Q1
20
01
Q3
20
02
Q1
20
02
Q3
20
03
Q1
20
03
Q3
20
04
Q1
20
04
Q3
20
05
Q1
20
05
Q3
20
06
Q1
20
06
Q3
20
07
Q1
20
07
Q3
20
08
Q1
20
08
Q3
20
09
Q1
20
09
Q3
20
10
Q1
20
10
Q3
20
11
Q1
20
11
Q3
20
12
Q1
20
12
Q3
20
13
Q1
20
13
Q3
20
14
Q1
20
14
Q3
20
15
Q1
20
15
Q3
20
16
Q1
20
16
Q3
Bil
lio
ns
of
Do
lla
rs
Comerica Total Assets & Total Loan Balances
recession
Total Assets
FED Total Assets Adverse
FED Total Assets Severely Adverse
Total Loan Balances
FED Total Loan Adverse
FED Total Loan Severely Adverse
BHC Total Assets Severely Adverse
BHC Total Loan Severely Adverse
-25.96%
2011 Sterling Bank acq.
169
Industry vs. Individual BHC
0
50
100
150
200
250
300
350
20
01
Q1
20
01
Q3
20
02
Q1
20
02
Q3
20
03
Q1
20
03
Q3
20
04
Q1
20
04
Q3
20
05
Q1
20
05
Q3
20
06
Q1
20
06
Q3
20
07
Q1
20
07
Q3
20
08
Q1
20
08
Q3
20
09
Q1
20
09
Q3
20
10
Q1
20
10
Q3
20
11
Q1
20
11
Q3
20
12
Q1
20
12
Q3
20
13
Q1
20
13
Q3
20
14
Q1
20
14
Q3
CFG vs. Industry: Total Assets Index & Total Loan Balances Index
recession
Total Assets
Total Loan Balances
Industry Total Assets
Industry Total Loan Balances
0
50
100
150
200
250
20
01
Q1
20
01
Q3
20
02
Q1
20
02
Q3
20
03
Q1
20
03
Q3
20
04
Q1
20
04
Q3
20
05
Q1
20
05
Q3
20
06
Q1
20
06
Q3
20
07
Q1
20
07
Q3
20
08
Q1
20
08
Q3
20
09
Q1
20
09
Q3
20
10
Q1
20
10
Q3
20
11
Q1
20
11
Q3
20
12
Q1
20
12
Q3
20
13
Q1
20
13
Q3
20
14
Q1
20
14
Q3
BB&T vs. Industry: Total Assets Index & Total Loan Balances Index
recession
Total Assets
Total Loan Balances
Industry Total Assets
Industry Total Loan Balances
0
50
100
150
200
250
300
20
01
Q1
20
01
Q3
20
02
Q1
20
02
Q3
20
03
Q1
20
03
Q3
20
04
Q1
20
04
Q3
20
05
Q1
20
05
Q3
20
06
Q1
20
06
Q3
20
07
Q1
20
07
Q3
20
08
Q1
20
08
Q3
20
09
Q1
20
09
Q3
20
10
Q1
20
10
Q3
20
11
Q1
20
11
Q3
20
12
Q1
20
12
Q3
20
13
Q1
20
13
Q3
20
14
Q1
20
14
Q3
BMO vs. Industry: Total Assets Index & Total Loan Balances Index
recession
Total Assets
Total Loan Balances
Industry Total Assets
Industry Total Loan Balances
0
50
100
150
200
250
20
01
Q1
20
01
Q3
20
02
Q1
20
02
Q3
20
03
Q1
20
03
Q3
20
04
Q1
20
04
Q3
20
05
Q1
20
05
Q3
20
06
Q1
20
06
Q3
20
07
Q1
20
07
Q3
20
08
Q1
20
08
Q3
20
09
Q1
20
09
Q3
20
10
Q1
20
10
Q3
20
11
Q1
20
11
Q3
20
12
Q1
20
12
Q3
20
13
Q1
20
13
Q3
20
14
Q1
20
14
Q3
Comerica vs. Industry: Total Assets Index & Total Loan Balances Index
recession
Total Assets
Total Loan Balances
Industry Total Assets
Industry Total Loan Balances
170
The most important component of the PPNR is the
balances
171
The best approach to building balance model is to work hierarchically
1. Industry level supply and demand for loans
2. Product level demand, i.e. loan for new autos
3. Bank level determinants – loan pricing, credit quality, strategy
Bigger Picture- Demand for Loans and Leases Drop During Recessions
172
Published by Dvorkin and Shell, “Bank Lending During
Recessions”, 2016, Federal Reserve Bank of St. Louis.
Loan growth became
negative and remained
so in the great recession.
The drop was more sever
compared to the
previous two recessions
Loan Supply
173
And Demand conditions both contribute to the overall
drop
174
Product level view
175
Forecasts are provided by Moody’s
Economy.com
What the bank controls
Example of an Input Template for Future Originations –
Measure Variable Q2 2015 Q3 2015 Q4 2015 Q1 2016 Q2 2016 Q3 2016 Q4 2016
Credit/Volume % of loans with FICO < 6XX 3% 3% 3% 3% 3% 3% 3%
Credit/Volume % of loans with LTV > 1XX 36% 36% 36% 36% 36% 36% 36%
Credit/Volume Weighted Average LTV 85 85 85 85 85 85 85
Credit/ Yields Weighted Average Loan Term 72.6 72.6 72.6 72.6 72.6 72.6 72.6
Credit/ Yields/Prepayment % of loans with term > 72 months 20% 20% 20% 20% 20% 20% 20%
Credit/Volume Weighted Average FICO 760 760 760 760 760 760 760
Yields/Prepayment/Volume Weighted Average Coupon/ Interest rate 4.49% 4.72% 4.95% 5.18% 5.39% 5.58% 5.77%
**Hypothetical Data
176
As we can see there is a strong positive
correlation between Vehicle Sales and
Auto volume (0.60) and negative one
between Unemployment rate and Auto
volume (-0.57). These variable can be
modeled based on historical relationship
and a scenario is provided for doing
forecast.
There is a negative correlation
between our origination FICO and Auto
volume (-0.55). This variable can be
modeled and based on the policy we
can generate a forecast
Approval
Rate
Dealer Count/
Productivity
Pricing/
Profitability
Strategy as an Overlay
T
O
T
A
L
V
O
L
U
M
E
Strategic decisions change over time and easier to bring as an overlay to the
model output
4.00
6.00
8.00
10.00
12.00
14.00
16.00
18.00
20.00
$-
$500
$1,000
$1,500
$2,000
$2,500
$3,000 2
00
2Q
1
20
02
Q3
20
03
Q1
20
03
Q3
20
04
Q1
20
04
Q3
20
05
Q1
20
05
Q3
20
06
Q2
20
06
Q4
20
07
Q2
20
07
Q4
20
08
Q2
20
08
Q4
20
09
Q2
20
09
Q4
20
10
Q2
20
10
Q4
20
11
Q2
20
11
Q4
20
12
Q2
20
12
Q4
20
13
Q2
20
13
Q4
Mill
ion
s
Auto Historical Origination Volume and Macro
Origination Balance Vehicle Sales (Millions) Unemployment Rate
700
710
720
730
740
750
760
770
780
790
$-
$500
$1,000
$1,500
$2,000
$2,500
$3,000
20
02
Q1
20
02
Q3
20
03
Q1
20
03
Q3
20
04
Q1
20
04
Q3
20
05
Q1
20
05
Q3
20
06
Q2
20
06
Q4
20
07
Q2
20
07
Q4
20
08
Q2
20
08
Q4
20
09
Q2
20
09
Q4
20
10
Q2
20
10
Q4
20
11
Q2
20
11
Q4
20
12
Q2
20
12
Q4
20
13
Q2
20
13
Q4
Mill
ion
s
Auto Historical Origination Volume and Credit Policy
Origination Balance Original FICO
M
A
C
R
O
C
R
E
D
I
T
Origination Volume Model Framework
S
T
R
A
T
E
G
Y
177
III. Setting up an integrated
framework to model cash flows
178
Integrated Framework for Stress Testing
PPNR ALLL Capital PlanningBusiness strategy for
future business
Economic
conditions
Product life-
cycle
characteristics
Credit characte-
ristics
Strategic
efforts
- Forecast of revenue by
business segmentProvision levels based on: Capital ratio projections
- Forecast of interest
income by product based
Forecast of net charge-off
rates by product based on:
- Risk appetite
- RWAs
- Profitability
- Pricing StrategyExisting Balances Existing Balances Existing Balances Existing Balances
New Origination or
Purchased Balances
New Origination or
Purchased Balances
New Origination or
Purchased Balances
New Origination or
Purchased Balances
Loan Interest Rate
Weighted Average Life Capital levels
ExpensesCharge-off and recovery
balancesPost-stress buffer
Deliquency balances
Active
Po
rtfolio
Man
agem
en
t
Macroeconom
ic Scenarios
(Regulatory, Internal)
Changes in product attributes due to
seasoning and aging
Underw
riting standards, risk
indicators
Regional expansions, appetite for
growth, general m
arket dynamics of
product, pricing strategies
Stress Testing Conditioning FactorsForecasting Components
Capital Action Plan
Them
esM
etri
cs
Co
mp
ren
he
nsi
ve C
ove
rage
Risk-adjusted return
metrics
An Integrated ST Framework could serve multiple purposes in a Bank, regulatory compliance is just one of them
Establishing the linkage between the various aspects of Credit and PPNR
is key to generating consistent cash flow estimates
179
Residential
Mortgage
New Originations
Prior Period Loan
Balance
New Non-
Performing Loans
Housing Price Index
Housing Price Index
Scheduled Principal
Payments
Unscheduled
Payoffs
Pull- Through Rate Unemployment
Unemployment
# of Applications
Market Growth
Product Mix Loan Officer Hiring
Portfolio Purchases
/ Sales
Loan Officer
Productivity
Credit Appetite
Existing
Driver
Macro Economic
Factor
Management View
Line Item
Risk Model Driver
Risk Model Driver /
Driver Relationship M1
M2
Strategy
Average New
Loan Size Strategy
Competition
Interest Rates
3 M
Strategy
Product Mix
A1
4
5
6
Pricing
Credit Appetite
4 M#
A#
#
Model
Analytic Process
Judgment
Risk Model M
2 1
A2
Age of Loan
Refinance
Housing Price Index
Interest Rates
4
PPNR FRAMEWORK Residential Mortgage Example
180
Components of Balances in Retail Models
Performing Balances
Change in Balance
New to NPL
(Charge Off)
Change in
utilization
New Origination
Prepayment
=
+
• Future Underwriting Quality
• Macroeconomic Trends
• Strategic Decisions
Loan Term
Coupon Type
Interest Rates
Explanatory Variables
• Interest Rates
• Macroeconomic Trends
• Obligor Characteristics
• Vintage Characteristics
• Credit Quality
• Macroeconomic trends
• Loan Characteristic
• Macroeconomic variables
• Loan Characteristics
• Amortization Type
• Previous Delinquent Accounts
Identifiers
Scheduled
Amortization
FICO, LTV, Loan Term, Coupon Type
Property Type
Pricing –Spread, Competitive Actions
Home Equity Interest Rate over
Prime/Other Credit
Household Indebtedness
Change in Home Prices
•LTV,
•FICO,
•Property Type,
•Loan Term
Spread
Coupon Type
Remaining Time to Maturity
Interest Rate
181
Connecting PPNR with Credit
Classification of Risk Drivers for the Cash Flow Modeling
Macro economy
Volume
Rates
GDP
Unemployment Rate
HPI
Corporate Spreads
Funding
Credit Quality
Underwriting Policy
Product Strategy
Pricing
FICO
LTV
Term
Product Type
Distribution Channels
Incentives
Footprint
Pricing/spread
Funding Strategy
Interest Expense on
Deposits and Wholesale funding
ST vs LT wholesale funding
182
Examples of Origination Loan Volume Models
Three main factors:
Credit Policy,
Macro economy and
Strategy
-
5,000
10,000
15,000
20,000
25,000
30,000
35,000
40,000
# of Accounts at Origination
Actual Fitted + Base S4
$-
$20,000
$40,000
$60,000
$80,000
$100,000
$120,000
$140,000
$160,000
Avg Credit Limit at Origination
Actual Fitted + Base S4
Origination # of Accounts
Description Coefficient
Prime Rate -0.0450
Original LTV (Weighted Average) 0.0391
Debt Service Burden 0.2254
Consumer Confidence Index 0.0019
Retail Sales 0.0366
(Base) 1.Vintage Seasonality 0.0000
2.Vintage Seasonality 0.1913
3.Vintage Seasonality 0.0901
4.Vintage Seasonality 0.1270
Constant 4.1302
Origination Avg Credit Limit
Description Coefficient
Original LTV (Weighted Average) 0.0665
Disposable Personal 0.000001
Case-Shiller HPI 0.0094
Unemployment Rate -0.0539
Constant 8.0600
183
iv. Modelling considerations
184
Modeling Considerations
- Combination of many factors make the modeling more challenging -Revenue may stay the same because of two opposite forces, lower asset level but
higher volume in times of stress
- Deciding what piece of PPNR to model and what part to leave to expert
judgment is key
- It is hard to find long time series of stable and granular data. Some short time
series might exhibit challenges for stationarity
- Pricing/loan origination regime is changing frequently -Not only the borrower behavior may be different but the Bank’s origination strategy,
channels might be different
185
Appendix
186
Framework for the Cash Flow in the WHL Models
Starting Balance (Step 1)
New Orig. Balance (Step 2)
Ending Balance (Step 7)
AQ1
AQ1
AQ2
AQ2
AQ3
AQ3
AQ4
AQ4
Runoff Balance (Step 3)
Migrating Balance (Step 4)
Transition Matrix
(Step 5)
Migrated Balance (Step 6)
AQ1
AQ1
AQ1
AQ2
AQ2
AQ2
AQ3
AQ3
AQ3
AQ4
AQ4
AQ4
New NPL (Step 8)
Expected Loss (Step 9)
Loss Emergence Curve
NCO (Step 10)
Revolver Usage Predictions
Some simplifying assumptions
are needed to bring in the
balance components in to
commercial modeling
framework that is based on
conditional transition matrices
187
Balances – Comparison Across Banks
0
20
40
60
80
100
120
140
160
20
01
Q1
20
01
Q4
20
02
Q3
20
03
Q2
20
04
Q1
20
04
Q4
20
05
Q3
20
06
Q2
20
07
Q1
20
07
Q4
20
08
Q3
20
09
Q2
20
10
Q1
20
10
Q4
20
11
Q3
20
12
Q2
20
13
Q1
20
13
Q4
20
14
Q3
20
15
Q2
20
16
Q1
20
16
Q4
Bill
ion
s o
f D
olla
rs
Fifth Third Total Assets & Total Loan Balances
recession
Total Assets
FED Total Assets Adverse
FED Total Assets Severely Adverse
Total Loan Balances
FED Total Loan Adverse
FED Total Loan Severely Adverse
BHC Total Assets Severely Adverse
BHC Total Loan Severely Adverse2007 R-G Crown Bank acq.
-13.59%
0
10
20
30
40
50
60
70
80
20
01
Q1
20
01
Q4
20
02
Q3
20
03
Q2
20
04
Q1
20
04
Q4
20
05
Q3
20
06
Q2
20
07
Q1
20
07
Q4
20
08
Q3
20
09
Q2
20
10
Q1
20
10
Q4
20
11
Q3
20
12
Q2
20
13
Q1
20
13
Q4
20
14
Q3
20
15
Q2
20
16
Q1
20
16
Q4
Bill
ion
s o
f D
olla
rs
Huntington Total Assets & Total Loan Balances
recession
Total Assets
FED Total Assets Adverse
FED Total Assets Severely Adverse
Total Loan Balances
FED Total Loan Adverse
FED Total Loan Severely Adverse
BHC Total Loan Severely Adverse
-12.77%
2007 Sky Financial acq.
0
20
40
60
80
100
120
20
01
Q1
20
01
Q4
20
02
Q3
20
03
Q2
20
04
Q1
20
04
Q4
20
05
Q3
20
06
Q2
20
07
Q1
20
07
Q4
20
08
Q3
20
09
Q2
20
10
Q1
20
10
Q4
20
11
Q3
20
12
Q2
20
13
Q1
20
13
Q4
20
14
Q3
20
15
Q2
20
16
Q1
20
16
Q4
Bill
ion
s o
f D
olla
rs
KeyCorp Total Assets & Total Loan Balances
recession
Total Assets
FED Total Assets Adverse
FED Total Assets Severely Adverse
Total Loan Balances
FED Total Loan Adverse
FED Total Loan Severely Adverse
BHC Total Assets Severely Adverse
BHC Total Loan Severely Adverse
-29.79%
0
20
40
60
80
100
120
140
20
01
Q1
20
01
Q4
20
02
Q3
20
03
Q2
20
04
Q1
20
04
Q4
20
05
Q3
20
06
Q2
20
07
Q1
20
07
Q4
20
08
Q3
20
09
Q2
20
10
Q1
20
10
Q4
20
11
Q3
20
12
Q2
20
13
Q1
20
13
Q4
20
14
Q3
20
15
Q2
20
16
Q1
20
16
Q4
Bill
ion
s o
f D
olla
rs
M&T Total Assets & Total Loan Balances
recession
Total Assets
FED Total Assets Adverse
FED Total Assets Severely Adverse
Total Loan Balances
FED Total Loan Adverse
FED Total Loan Severely Adverse
BHC Total Assets Severely Adverse
BHC Total Loan Severely Adverse2003 Allfirst Bank of Baltimore acq.2011 Wilmington Trust acq.
188
Balances – Comparison Across Banks
0
50
100
150
200
250
300
350
400
20
01
Q1
20
01
Q4
20
02
Q3
20
03
Q2
20
04
Q1
20
04
Q4
20
05
Q3
20
06
Q2
20
07
Q1
20
07
Q4
20
08
Q3
20
09
Q2
20
10
Q1
20
10
Q4
20
11
Q3
20
12
Q2
20
13
Q1
20
13
Q4
20
14
Q3
20
15
Q2
20
16
Q1
20
16
Q4
Bill
ion
s o
f D
olla
rs
PNC Total Assets & Total Loan Balances
recession
Total Assets
FED Total Assets Adverse
FED Total Assets Severely Adverse
Total Loan Balances
FED Total Loan Adverse
FED Total Loan Severely Adverse
BHC Total Assets Severely Adverse
BHC Total Loan Severely Adverse
15.67%
2008 National City Corp acq.
0
20
40
60
80
100
120
140
160
20
04
Q3
20
05
Q1
20
05
Q3
20
06
Q1
20
06
Q3
20
07
Q1
20
07
Q3
20
08
Q1
20
08
Q3
20
09
Q1
20
09
Q3
20
10
Q1
20
10
Q3
20
11
Q1
20
11
Q3
20
12
Q1
20
12
Q3
20
13
Q1
20
13
Q3
20
14
Q1
20
14
Q3
20
15
Q1
20
15
Q3
20
16
Q1
20
16
Q3
Bil
lio
ns o
f D
oll
ars
Regions Total Assets & Total Loan Balances
recession
Total Assets
FED Total Assets Adverse
FED Total Assets Severely Adverse
Total Loan Balances
FED Total Loan Adverse
FED Total Loan Severely Adverse
BHC Total Assets Severely Adverse
BHC Total Loan Severely Adverse
-25.88%
2006 merged with AmSouth Bancorporation.
0
50
100
150
200
250
20
01
Q1
20
01
Q4
20
02
Q3
20
03
Q2
20
04
Q1
20
04
Q4
20
05
Q3
20
06
Q2
20
07
Q1
20
07
Q4
20
08
Q3
20
09
Q2
20
10
Q1
20
10
Q4
20
11
Q3
20
12
Q2
20
13
Q1
20
13
Q4
20
14
Q3
20
15
Q2
20
16
Q1
20
16
Q4
Bill
ion
s o
f D
olla
rs
SunTrust Total Assets & Total Loan Balances
recession
Total Assets
FED Total Assets Adverse
FED Total Assets Severely Adverse
Total Loan Balances
FED Total Loan Adverse
FED Total Loan Severely Adverse
BHC Total Assets Severely Adverse
BHC Total Loan Severely Adverse
-12.03%
2004 National Commerce Financial acq.
0
10
20
30
40
50
60
70
80
90
20
01
Q1
20
01
Q3
20
02
Q1
20
02
Q3
20
03
Q1
20
03
Q3
20
04
Q1
20
04
Q3
20
05
Q1
20
05
Q3
20
06
Q1
20
06
Q3
20
07
Q1
20
07
Q3
20
08
Q1
20
08
Q3
20
09
Q1
20
09
Q3
20
10
Q1
20
10
Q3
20
11
Q1
20
11
Q3
20
12
Q1
20
12
Q3
20
13
Q1
20
13
Q3
20
14
Q1
20
14
Q3
20
15
Q1
20
15
Q3
20
16
Q1
20
16
Q3
Bill
ion
s o
f D
olla
rs
BBVA Total Assets & Total Loan Balances
recession
Total Assets
FED Total Assets Adverse
FED Total Assets Severely Adverse
Total Loan Balances
FED Total Loan Adverse
FED Total Loan Severely Adverse
BHC Total Assets Severely Adverse
BHC Total Loan Severely Adverse
-10.5%
2007 BBVA acquired Compass Bancshares.
189
Balances – Comparison Across Banks
0
20
40
60
80
100
120
140
20
01
Q1
20
01
Q4
20
02
Q3
20
03
Q2
20
04
Q1
20
04
Q4
20
05
Q3
20
06
Q2
20
07
Q1
20
07
Q4
20
08
Q3
20
09
Q2
20
10
Q1
20
10
Q4
20
11
Q3
20
12
Q2
20
13
Q1
20
13
Q4
20
14
Q3
20
15
Q2
20
16
Q1
20
16
Q4
Bill
ion
s o
f D
olla
rs
MUFG Total Assets & Total Loan Balances
recession
Total Assets
FED Total Assets Adverse
FED Total Assets Severely Adverse
Total Loan Balances
FED Total Loan Adverse
FED Total Loan Severely Adverse
-7.2%
2008 BTMU acquired UnionBanCal.
0
50
100
150
200
250
300
350
400
450
20
01
Q1
20
01
Q4
20
02
Q3
20
03
Q2
20
04
Q1
20
04
Q4
20
05
Q3
20
06
Q2
20
07
Q1
20
07
Q4
20
08
Q3
20
09
Q2
20
10
Q1
20
10
Q4
20
11
Q3
20
12
Q2
20
13
Q1
20
13
Q4
20
14
Q3
20
15
Q2
20
16
Q1
20
16
Q4
Bill
ion
s o
f D
olla
rs
U.S. Bancrop Total Assets & Total Loan Balances
recession
Total Assets
FED Total Assets Adverse
FED Total Assets Severely Adverse
Total Loan Balances
FED Total Loan Adverse
FED Total Loan Severely Adverse
BHC Total Assets Severely Adverse
BHC Total Loan Severely Adverse2009 FBOP Corporation's nine subsidiary banks acq.
0
10
20
30
40
50
60
70
20
01
Q1
20
01
Q4
20
02
Q3
20
03
Q2
20
04
Q1
20
04
Q4
20
05
Q3
20
06
Q2
20
07
Q1
20
07
Q4
20
08
Q3
20
09
Q2
20
10
Q1
20
10
Q4
20
11
Q3
20
12
Q2
20
13
Q1
20
13
Q4
20
14
Q3
20
15
Q2
20
16
Q1
20
16
Q4
Bill
ion
s o
f D
olla
rs
Zions Total Assets & Total Loan Balances
recession
Total Assets
FED Total Assets Adverse
FED Total Assets Severely Adverse
Total Loan Balances
FED Total Loan Adverse
FED Total Loan Severely Adverse
BHC Total Loan Severely Adverse
-14.35%
2005 Amegy Bancorporation acq.
190
Industry vs. Individual BHC
0
50
100
150
200
250
20
01
Q1
20
01
Q3
20
02
Q1
20
02
Q3
20
03
Q1
20
03
Q3
20
04
Q1
20
04
Q3
20
05
Q1
20
05
Q3
20
06
Q1
20
06
Q3
20
07
Q1
20
07
Q3
20
08
Q1
20
08
Q3
20
09
Q1
20
09
Q3
20
10
Q1
20
10
Q3
20
11
Q1
20
11
Q3
20
12
Q1
20
12
Q3
20
13
Q1
20
13
Q3
20
14
Q1
20
14
Q3
Fifth Third vs. Industry: Total Assets Index & Total Loan Balances Index
recession
Total Assets
Total Loan Balances
Industry Total Assets
Industry Total Loan Balances
0
50
100
150
200
250
20
01
Q1
20
01
Q3
20
02
Q1
20
02
Q3
20
03
Q1
20
03
Q3
20
04
Q1
20
04
Q3
20
05
Q1
20
05
Q3
20
06
Q1
20
06
Q3
20
07
Q1
20
07
Q3
20
08
Q1
20
08
Q3
20
09
Q1
20
09
Q3
20
10
Q1
20
10
Q3
20
11
Q1
20
11
Q3
20
12
Q1
20
12
Q3
20
13
Q1
20
13
Q3
20
14
Q1
20
14
Q3
Huntington vs. Industry: Total Assets Index & Total Loan Balances Index
recession
Total Assets
Total Loan Balances
Industry Total Assets
Industry Total Loan Balances
0
50
100
150
200
250
20
01
Q1
20
01
Q3
20
02
Q1
20
02
Q3
20
03
Q1
20
03
Q3
20
04
Q1
20
04
Q3
20
05
Q1
20
05
Q3
20
06
Q1
20
06
Q3
20
07
Q1
20
07
Q3
20
08
Q1
20
08
Q3
20
09
Q1
20
09
Q3
20
10
Q1
20
10
Q3
20
11
Q1
20
11
Q3
20
12
Q1
20
12
Q3
20
13
Q1
20
13
Q3
20
14
Q1
20
14
Q3
KeyCorp vs. Industry: Total Assets Index & Total Loan Balances Index
recession
Total Assets
Total Loan Balances
Industry Total Assets
Industry Total Loan Balances
0
50
100
150
200
250
20
01
Q1
20
01
Q3
20
02
Q1
20
02
Q3
20
03
Q1
20
03
Q3
20
04
Q1
20
04
Q3
20
05
Q1
20
05
Q3
20
06
Q1
20
06
Q3
20
07
Q1
20
07
Q3
20
08
Q1
20
08
Q3
20
09
Q1
20
09
Q3
20
10
Q1
20
10
Q3
20
11
Q1
20
11
Q3
20
12
Q1
20
12
Q3
20
13
Q1
20
13
Q3
20
14
Q1
20
14
Q3
M&T vs. Industry: Total Assets Index & Total Loan Balances Index
recession
Total Assets
Total Loan Balances
Industry Total Assets
Industry Total Loan Balances
191
Industry vs. Individual BHC
0
50
100
150
200
250
300
350
20
01
Q1
20
01
Q3
20
02
Q1
20
02
Q3
20
03
Q1
20
03
Q3
20
04
Q1
20
04
Q3
20
05
Q1
20
05
Q3
20
06
Q1
20
06
Q3
20
07
Q1
20
07
Q3
20
08
Q1
20
08
Q3
20
09
Q1
20
09
Q3
20
10
Q1
20
10
Q3
20
11
Q1
20
11
Q3
20
12
Q1
20
12
Q3
20
13
Q1
20
13
Q3
20
14
Q1
20
14
Q3
PNC vs. Industry: Total Assets Index & Total Loan Balances Index
recession
Total Assets
Total Loan Balances
Industry Total Assets
Industry Total Loan Balances
0
20
40
60
80
100
120
140
160
180
200
Regions vs. Industry: Total Assets Index & Total Loan Balances Index
recession
Total Assets
Total Loan Balances
Industry Total Assets
Industry Total Loan Balances
-30
20
70
120
170
220
270
20
01
Q1
20
01
Q3
20
02
Q1
20
02
Q3
20
03
Q1
20
03
Q3
20
04
Q1
20
04
Q3
20
05
Q1
20
05
Q3
20
06
Q1
20
06
Q3
20
07
Q1
20
07
Q3
20
08
Q1
20
08
Q3
20
09
Q1
20
09
Q3
20
10
Q1
20
10
Q3
20
11
Q1
20
11
Q3
20
12
Q1
20
12
Q3
20
13
Q1
20
13
Q3
20
14
Q1
20
14
Q3
BBVA vs. Industry: Total Assets Index & Total Loan Balances Index
recession
Total Assets
Total Loan Balances
Industry Total Assets
Industry Total Loan Balances
0
50
100
150
200
250
20
01
Q1
20
01
Q3
20
02
Q1
20
02
Q3
20
03
Q1
20
03
Q3
20
04
Q1
20
04
Q3
20
05
Q1
20
05
Q3
20
06
Q1
20
06
Q3
20
07
Q1
20
07
Q3
20
08
Q1
20
08
Q3
20
09
Q1
20
09
Q3
20
10
Q1
20
10
Q3
20
11
Q1
20
11
Q3
20
12
Q1
20
12
Q3
20
13
Q1
20
13
Q3
20
14
Q1
20
14
Q3
Sun Trust vs. Industry: Total Assets Index & Total Loan Balances Index
recession
Total Assets
Total Loan Balances
Industry Total Assets
Industry Total Loan Balances
192
Industry vs. Individual BHC
0
50
100
150
200
250
20
01
Q1
20
01
Q3
20
02
Q1
20
02
Q3
20
03
Q1
20
03
Q3
20
04
Q1
20
04
Q3
20
05
Q1
20
05
Q3
20
06
Q1
20
06
Q3
20
07
Q1
20
07
Q3
20
08
Q1
20
08
Q3
20
09
Q1
20
09
Q3
20
10
Q1
20
10
Q3
20
11
Q1
20
11
Q3
20
12
Q1
20
12
Q3
20
13
Q1
20
13
Q3
20
14
Q1
20
14
Q3
MUFG vs. Industry: Total Assets Index & Total Loan Balances Index
recession
Total Assets
Total Loan Balances
Industry Total Assets
Industry Total Loan Balances
0
50
100
150
200
250
20
01
Q1
20
01
Q3
20
02
Q1
20
02
Q3
20
03
Q1
20
03
Q3
20
04
Q1
20
04
Q3
20
05
Q1
20
05
Q3
20
06
Q1
20
06
Q3
20
07
Q1
20
07
Q3
20
08
Q1
20
08
Q3
20
09
Q1
20
09
Q3
20
10
Q1
20
10
Q3
20
11
Q1
20
11
Q3
20
12
Q1
20
12
Q3
20
13
Q1
20
13
Q3
20
14
Q1
20
14
Q3
U.S. Bancrop vs. Industry: Total Assets Index & Total Loan Balances Index
recession
Total Assets
Total Loan Balances
Industry Total Assets
Industry Total Loan Balances
0
50
100
150
200
250
20
01
Q1
20
01
Q3
20
02
Q1
20
02
Q3
20
03
Q1
20
03
Q3
20
04
Q1
20
04
Q3
20
05
Q1
20
05
Q3
20
06
Q1
20
06
Q3
20
07
Q1
20
07
Q3
20
08
Q1
20
08
Q3
20
09
Q1
20
09
Q3
20
10
Q1
20
10
Q3
20
11
Q1
20
11
Q3
20
12
Q1
20
12
Q3
20
13
Q1
20
13
Q3
20
14
Q1
20
14
Q3
Zions vs. Industry: Total Assets Index & Total Loan Balances Index
recession
Total Assets
Total Loan Balances
Industry Total Assets
Industry Total Loan Balances
193
Application for portfolio
monitoring and management
194
Application – Auto Portfolio
Origination credit quality
Origination loan terms and
interest rates
Macroeconomic background
Evolution of the portfolio over
time:
GCO
Prepay and
Loan terms
195
Application – Mortgage Portfolio
Origination credit quality
Origination loan terms and
interest rates
Macroeconomic background
Evolution of the portfolio over
time:
GCO
Prepay and
Loan terms
196