stress testing workshop - cefpro · connecting portfolio risk return to valuation and ......

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

http://www.mayoclinic.org/about-this-site/welcome

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

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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

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BB

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Fift

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Cap

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Tier 1Common

Ratio


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)




Source: BHC-specific DFAST disclosures.

22


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

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BM

O

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Fift

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Hu

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Tier 1Common

Ratio


Capital Ratio in Severely Adverse Scenario2014/2015 BHC Comparison: Tier 1 Common Ratio, Ending

BHC 2014

BHC 2015







Source: BHC-specific DFAST disclosures.

23


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

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BB

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Fift

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Tier 1Common

Ratio


Capital Ratio in Severely Adverse Scenario2014/2015 FRB Comparison: Tier 1 Common Ratio, Actual Q3 2013/2014

Fed 2014

Fed 2015







Source: Fed DFAST disclosures.

24


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

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BB

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Tier 1Common

Ratio


Capital Ratio in Severely Adverse Scenario2014/2015 FRB Comparison: Tier 1 Common Ratio, Ending

Fed 2014

Fed 2015







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



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 (%)


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 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%


27

Other Bank Capital Ratios

Medians



BHC Model 13.95% 13.15% 11.95% 18.451% 14.35%

Fed Model 13.9% 12.40% 12.00% 17.8% 13.6%





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%


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

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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


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


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


33

All Loan Losses – BHC vs Historical comparison


34

First Lien Mortgages, Domestic


0.0%

1.0%

2.0%

3.0%

4.0%

5.0%

6.0%

7.0%

8.0%

9.0%

10.0%B

B&

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Severely Adverse Loan Loss (Percent) for Q4 2014 through Q4 20162014/2015 BHC Comparison: First Lien Mortgage, Domestic

BHC 2015

BHC 2014





Median (Other, BHC)

Median (Other, FED)


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


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


0.0%

5.0%

10.0%

15.0%

20.0%

25.0%

30.0%B

B&

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Severely Adverse Loan Loss (Percent) for Q4 2014 through Q4 20162014/2015 BHC Comparison: Junior Liens and HELOCs, Domestic

BHC 2015

BHC 2014





Median (Other, BHC)

Median (Other, FED)


BB&T BBVA


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


Wells

Fargo Ally

American


Goldman


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


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




Severely Adverse Loan Loss (Percent) for Q4 2014 through Q4 20162014/2015 BHC Comparison: Commercial and Industrial

BHC 2015

BHC 2014





Median (Other, BHC)

Median (Other, FED)


BB&T BBVA


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


Wells

Fargo Ally

American


Goldman


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


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




Severely Adverse Loan Loss (Percent) for Q4 2014 through Q4 20162014/2015 BHC Comparison: Commercial Real Estate, Domestic

BHC 2015

BHC 2014





Median (Other, BHC)

Median (Other, FED)


BB&T BBVA


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


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




Severely Adverse Loan Loss (Percent) for Q4 2014 through Q4 20162014/2015 BHC Comparison: Credit Cards

BHC 2015

BHC 2014





Median (Other, BHC)

Median (Other, FED)


BB&T BBVA


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


Wells

Fargo Ally

American


Goldman


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


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




Severely Adverse Loan Loss (Percent) for Q4 2014 through Q4 20162014/2015 BHC Comparison: Other Consumer

BHC 2015

BHC 2014





Median (Other, BHC)

Median (Other, FED)


BB&T BBVA


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


Wells

Fargo Ally

American


Goldman


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


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




Severely Adverse Loan Loss (Percent) for Q4 2014 through Q4 20162014/2015 BHC Comparison: Other Loans

BHC 2015

BHC 2014





Median (Other, BHC)

Median (Other, FED)


BB&T BBVA


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


Wells

Fargo Ally

American


Goldman


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%


Loan Losses First Lien

Mortgages, Domestic

Junior Liens and HELOCs,

Domestic


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%


43

National Bank Loan Losses – CCAR 2015




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%



Mortgages, Domestic


Domestic



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%


44

Other Bank Loan Losses – CCAR 2015




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%



Mortgages, Domestic


Domestic



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%


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


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


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 (Other, BHC)

Median (Other, 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


Billionsof Dollars


Capital Ratio in Severely Adverse ScenarioFED Adverse & Severely Adverse Comparison: Pre-Provision Net Revenue

FED Adverse

FED Severely Adverse



Median (Other, BHC)

Median (Other, FED)




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 –


67

10-year Treasury Rate under Sev. Adverse Scenario –


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


• 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


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


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


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


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


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



# 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






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



# liens 0.150615 0.005713 -49.91 0.000

New FICO model indicator 1.973376 0.049141 27.3 0.000


Product #3 indicator 0.441167 0.029491 -12.24 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



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




# liens 0.833008 0.041499 -3.670 0.000

New FICO model indicator 0.24706 0.02432 -14.200 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



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




# liens 0.138548 0.005527 -49.550 0.000

New FICO model indicator 2.007633 0.049885 28.050 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



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


100


101


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


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


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


(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


Billionsof Dollars


Capital Ratio in Severely Adverse ScenarioFED/BHC Comparison: Pre-Provision Net Revenue

Fed Model

BHC Model



Median (Other, BHC)

Median (Other, 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


Billionsof Dollars


Capital Ratio in Severely Adverse ScenarioFED Adverse & Severely Adverse Comparison: Pre-Provision Net Revenue

FED Adverse

FED Severely Adverse



Median (Other, BHC)

Median (Other, FED)




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



Total Loan Balances





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



Total Loan Balances




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



Total Loan Balances





-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



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



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



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


• Strategic Decisions

Loan Term

Coupon Type

Interest Rates

Explanatory Variables

• Interest Rates


• 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



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


0

20

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60

80

100

120

140

160

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20

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Q3

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15

Q2

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16

Q1

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16

Q4

Bill

ion

s o

f D

olla

rs

Fifth Third Total Assets & Total Loan Balances

recession

Total Assets



Total Loan Balances




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

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20

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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



Total Loan Balances




-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

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13

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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



Total Loan Balances





-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

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Q3

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06

Q2

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Q1

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07

Q4

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Q3

20

09

Q2

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10

Q1

20

10

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11

Q3

20

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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



Total Loan Balances




BHC Total Loan Severely Adverse2003 Allfirst Bank of Baltimore acq.2011 Wilmington Trust acq.

188


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



Total Loan Balances





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

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Q1

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08

Q3

20

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Q1

20

09

Q3

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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



Total Loan Balances





-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

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Q3

20

15

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20

16

Q1

20

16

Q4

Bill

ion

s o

f D

olla

rs

SunTrust Total Assets & Total Loan Balances

recession

Total Assets



Total Loan Balances





-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

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olla

rs

BBVA Total Assets & Total Loan Balances

recession

Total Assets



Total Loan Balances





-10.5%

2007 BBVA acquired Compass Bancshares.

189


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

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rs

MUFG Total Assets & Total Loan Balances

recession

Total Assets



Total Loan Balances



-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

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13

Q4

20

14

Q3

20

15

Q2

20

16

Q1

20

16

Q4

Bill

ion

s o

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rs

U.S. Bancrop Total Assets & Total Loan Balances

recession

Total Assets



Total Loan Balances




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

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rs

Zions Total Assets & Total Loan Balances

recession

Total Assets



Total Loan Balances




-14.35%

2005 Amegy Bancorporation acq.

190


0

50

100

150

200

250

20

01

Q1

20

01

Q3

20

02

Q1

20

02

Q3

20

03

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20

03

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20

04

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20

04

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20

05

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20

05

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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



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

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20

10

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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



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



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



191


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



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



-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



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



192


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



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



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



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

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