Impact of Economic Stress on Retail Credit Portfolios ...s Analytics W… · Impact of Economic Stress on Retail Credit Portfolios Across Europe 10 UK Mortgages Impact on the forecast

June 2020

Impact of Economic Stress on Retail Credit

Portfolios Across Europe

Europe Retail Credit Risk Webinar Series

Episode 1

Wednesday, 24 June

10:00 BST | 11:00 CEST

COVID-19:

360° View of the Dutch

Mortgage Loan Market

Impact of Economic

Stress on Retail Credit

Portfolios Across Europe

Episode 2

Tuesday, 30 June

14:00 BST | 15:00 CEST

Impact of Economic Stress on Retail Credit Portfolios Across Europe 3

Today’s Speakers

Brenda Solis Gonzalez

Risk Modeler

Pouyan Mashayekh, PhD

Senior Director

Juan Licari, PhD

Managing Director

Economics & Business Analytics


Retail loan-level econometric models for credit and impairment metrics

Portfolio Analyzer (PA) Suite of Models

Delinquency/Arrears (Flow rates or migration matrices)

Default Rates & PDs(Dynamic term-structures)

Prepayment Risk(Closed-good physical risk)

Exposure at Default (EADs)(Amortization curves or utilization

factors)

Expected Credit Losses (ECLs)

IFRS 9

Credit Impairments

M o r t g a g e s(With relevant sub-categories)

C r e d i t C a r d s(Bank cards & Retail cards)

P e r s o n a l L o a n s & L i n e s(Unsecured &r Secured)

Ve h i c l e / Au t o F i n a n c e(Loans & Leases)

O v e r d r a f t s(Across account types)

S t u d e n t L o a n s(Government & Private)

R e t a i l S M E s

(With relevant sub-categories)

Portfolio Loss Analytics(Dynamic multi-period Loss Distributions, VaRs, Tail-risk-contribution analysis,

Economic Capital, Risk Concentration)

Retai l Asset Classes


1. Modular Approach to Model Credit Risk for Retail Credit Portfolios

2. Case Studies for Mortgages, Consumer Loans, and Credit Cards

3. Auto Loans in Europe: Pan-European Perspective

4. Characterizing Non-Performing Loans

Agenda

1Modular Approach to Model

Credit Risk for Retail Credit

Portfolios


Shift of baseline scenarios

Unemployment Increases

0.0

5.0

10.0

15.0

20.0

25.0

Germany UnitedKingdom

France Greece Spain Italy Netherlands Portugal RussianFederation

Poland UnitedStates

Unemployment in 2019Q4 Max of 2020-22 Unemployment (Feb) Max of 2020-22 Unemployment (June)

CountryUnemployment

in 2019Q4

Max of 2020-22

Unemployment

(Feb)

Max of 2020-22

Unemployment

(June)

Value Date Value Date

Germany 5.00 5.36 2022Q4 6.36 2020Q4

United Kingdom 3.80 4.64 2022Q4 8.45 2021Q1

France 8.10 8.51 2020Q1 11.04 2020Q3

Greece 16.53 16.34 2020Q1 21.15 2020Q3

Spain 13.81 14.06 2020Q2 24.02 2020Q2

Italy 9.57 10.52 2022Q4 13.16 2020Q4

Netherlands 3.40 4.48 2021Q3 7.60 2021Q2

Portugal 6.62 6.29 2020Q1 9.86 2020Q3

Russian

Federation4.58 5.78 2021Q1 6.71 2020Q3

Poland 2.85 3.91 2022Q4 6.82 2021Q3

United States 3.53 4.37 2022Q2 14.04 2020Q2


Modular Structure for Consumer Loans AnalysisUsing Moody’s Portfolio Analyzer

FACTORS

Economic Data

Loan Level Data

Default/Prepayment

LGD

EAD

Risks simulations, cash-flow

calculations

Scenario projections for risk metrics such

as ECL, Loss Distribution, Credit

VaR

Account-level, forward-looking,

scenario-conditional cash-flows

MODELS OUTPUT

Staging, Impairment

Calculations & Forecasts

2Case Studies for

Mortgages, Consumer

Loans, and Credit Cards


UK MortgagesImpact on the forecast of key risk metrics

2

7

12

17

22

27

2020M4 2021M4 2022M4 2023M4 2024M4

Baseline (Feb) Upside (Feb) Downside (Feb)

Baseline (May) Upside (May) Downside (May)

0.05

0.15

0.25

0.35

0.45

0.55

0.65

0.75

0.85

0.95

2020M4 2021M4 2022M4 2023M4 2024M4



A n n u a l i z e d C o n d i t i o n a l P D , % A n n u a l i z e d L G D , %


UK MortgagesImpact on expected losses

0.015 0.0170.024

0.018

0.030

0.053

0.179

0.081

Upside Baseline Downside Prob. Weighted

Feb May

0.001 0.003 0.000.003 0.02 0.0230.02

0.100.12

0.010.04 0.05

0.03

0.11

0.160.16

0.48

0.76

0.00

0.10

0.20

0.30

0.40

0.50

0.60

0.70

0.80

Owner Occupied (OCC) Buy To Let (BTL) Secondary Home (SEC)

Upside (Feb) Baseline (Feb) Downside (Feb)Upside (May) Baseline (May) Downside (May)

E x p e c t e d l o s s e s b y O c c u p a n c y, % E x p e c t e d l o s s e s b y S t a g e s , %


UK Mortgages: Cross-country ComparisonIFRS 9 staging and expected losses

0.018

0.02

0.081

0.04

UK Dutch

Feb May

D i s t r i b u t i o n o f E x p o s u r e a c r o s s S t a g e s p e r S c e n a r i o s , %

E x p e c t e d C r e d i t L o s s e s o v e r E x p o s u r e s , %

99.06 99.00

0.789 0.853

0.150 0.150

Feb May

U K D u t c h

98.30

85.02

1.26

14.54

0.43 0.43

Feb May


UK Mortgages: Impact on One AccountChanging DPD status & scenario vintages, all else unchanged

E x p e c t e d L o s s e s , %

1.457

19.098

1.729

23.051

Current 60DPD

Feb May

L i f e t i m e P D , %

0.142

12.442

0.205

14.644

Current 60DPD

Feb May

1 2 m o n t h P D , %

0.006

0.754

0.008

0.910

Current 60DPD

Feb May


UK MortgagesSimulations –Distribution of Expected Losses

0

500

1,000

1,500

2,000

2,500

3,000

0.000 0.003 0.005 0.008 0.015 0.030 0.045 0.060 0.075 0.094 0.119

Feb May

February May

Loss Summary Baseline Feb 2020 Loss Summary Baseline May 2020

Expected Loss 0.002 Expected Loss 0.022

Loss Summary S3 Feb 2020 Loss Summary S3 May 2020




Aggregate Statistics Feb 2020 – BL Aggregate Statistics May 2020 – BL

Simulations 10,000 Simulations 10,000

Mean 0.0019 Mean 0.0222

SD 0.0016 SD 0.0118

IQR 0.0016 IQR 0.0141

Skewness 2.3716 Skewness 1.489

Kurtosis 8.7207 Kurtosis 3.6922

95th/50th Pct 3.7547 95th/50th Pct 2.2905

D i s t r i b u t i o n o f E L s A c r o s s S c e n a r i o V i n t a g e s , B a s e l i n e


0.3

0.35

0.4

0.45

0.5

0.55

2020M5 2020M11 2021M5 2021M11 2022M5



UK Credit CardsImpact on PD

0.4390.453

0.474

0.5070.523

0.549

Upside Baseline Downside Upside Baseline Downside

A n n u a l i z e d u n c o n d i t i o n a l P D , % 1 2 - m o n t h P D , %

Feb May


94.4593.97

5.55.97

0.05 0.05

Prob. Weighted Prob. Weighted

Stage 1 Stage 2 Stage 3

M a yF e b

Impact on stages and ECL

UK Credit Cards

0.72

0.74

0.77

0.74

0.78

0.80

0.84

0.80


Feb May

D i s t r i b u t i o n o f E x p o s u r e a c r o s s S t a g e s , % E x p e c t e d C r e d i t L o s s e s o v e r E x p o s u r e s , %

0.169.72

97.08

0.179.87

97.22



0.5

0.6

0.7

0.8

0.9

1

1.1

1.2

1.3

May 2020 Nov 2020 May 2021 Nov 2021 May 2022



German Personal LoansImpact on PD

0.799 0.805 0.811

0.8900.955

1.020

Upside Baseline Downside Upside Baseline Downside

A n n u a l i z e d u n c o n d i t i o n a l P D , % 1 2 - m o n t h s P D , % ,

Feb May


63.1554.43

36.6545.37

0.20 0.20

Prob. Weighted Prob. Weighted


M a yF e b

Impact on stages and ECL

German Personal Loans

1.341.44

1.611.46

1.5481.717

1.9211.727


Feb May

D i s t r i b u t i o n o f E x p o s u r e a c r o s s S t a g e s , % E x p e c t e d C r e d i t L o s s e s o v e r E x p o s u r e s , %

0.47 2.76

77.86

0.51 3.20

96.74


3 Auto Loans in Europe:

Pan-European Perspective


Explanatory Variables of PP, PD & LGD models

LTV

New/Used

Car age at origination

Loan age

Employment status

Borrower income

Income verification

Unemployment rate

Home price changes

LTV

New/Used

Car age at origination

Borrower income

PD & Prepayment Loss Given Default


Spain: PD ForecastFor a representative portfolio

A n n u a l i z e d C o n d i t i o n a l P D , % 1 2 - m o n t h P D , %

F e b M a y

0

0.005

0.01

0.015

0.02

0.025

0.03

0.035

0.04

2020M07 2021M08 2022M09 2023M10 2024M11

Baseline (Feb) Downside (Feb) Severe downside (Feb)

Baseline (May) Downside (May) Severe downside (May)

0.11

0.16

0.17 0.16

0.20

0.22

0

0.05

0.1

0.15

0.2

0.25

Baseline Downside Severedownside



February May








Number of Simulations 100,000 Number of Simulations 100,000

Mean (Portfolio EL) 0.8966% Mean (Portfolio EL) 1.2786%

Standard Deviation 0.2019 Standard Deviation 0.1875

Interquartile Range 0.2183 Interquartile Range 0.2108



95th / 50th Percentile 1.4642 95th / 50th Percentile 1.2835

Value-at-Risk Value-at-Risk

99.5 1.77 99.5 2.03

99.0 1.59 99.0 1.89

95.0 1.26 95.0 1.61

90.0 1.14 90.0 1.51

Spain: Simulations – Distribution of Expected LossesExpected Loss - VaR Approach


0

0.02

0.04

0.06

0.08

0.1

0.12

0.0

5

0.2

0.3

5

0.5

0.6

5

0.8

0.9

5

1.1

1.2

5

1.4

1.5

5

1.7

1.8

5 2

2.1

5

2.3

2.4

5

2.6

2.7

5

2.9

3.0

5

3.2

3.3

5

3.5

3.6

5

3.8

3.9

5

4.1

4.2

5

4.4

Fre

qu

en

cy

EL [%]

Feb 2020 - BL May 2020 - BL


Portugal: PD ForecastFor a representative portfolio

A n n u a l i z e d C o n d i t i o n a l P D , % 1 2 - m o n t h P D , %

0

0.01

0.02

0.03

0.04

0.05

0.06

0.07

0.08

2020m07 2021m06 2022m05 2023m04 2024m03 2025m02

Baseline (Feb) Downside (Feb) Severe downside (Feb)

Baseline (May) Downside (May) Severe downside (May)

0.25

0.32

0.35

0.32

0.40

0.43

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

0.45

Baseline Downside SevereDownside


F e b M a y


Feb May








Number of Simulations100,000

Number of Simulations100,000

Mean (Portfolio EL) 1.6269% Mean (Portfolio EL) 2.3703%

Standard Deviation 0.2087 Standard Deviation 0.2707

Interquartile Range0.2737

Interquartile Range0.3538



95th / 50th Percentile1.2342

95th / 50th Percentile1.2127

Value-at-Risk Value-at-Risk

99.5 2.27 99.5 3.23

99.0 2.19 99.0 3.12

95.0 2.00 95.0 2.85

90.0 1.91 90.0 2.73

Portugal: Simulations – Distribution of Expected LossesExpected Loss - VaR Approach


0

0.02

0.04

0.06

0.08

0.1

0.12

0.0

5

0.2

0.3

5

0.5

0.6

5

0.8

0.9

5

1.1

1.2

5

1.4

1.5

5

1.7

1.8

5 2

2.1

5

2.3

2.4

5

2.6

2.7

5

2.9

3.0

5

3.2

3.3

5

3.5

3.6

5

3.8

3.9

5

4.1

4.2

5

4.4

Fre

qu

en

cy

EL [%]

Feb 2020 - BL May 2020 - BL

4 Characterizing

Non-performing Loans


Household NPL exposure 2018, € Billion

NPLs By Country

Source: Moody’s Analytics, ECB

56.1

1

48.6

2

41.1

2

34.6

7

34.5

6

16.2

7

14.7

0

9.0

1

5.7

8

5.2

0

4.8

6

4.8

0

4.6

1

4.2

2

4.0

9

2.9

2

1.9

3

0.5

0

0.2

6

0.2

5

0.1

7

0.1

5

0.1

5

0.1

5

0.1

1

0.0

5

0.0

3

0

10

20

30

40

50

60

70

€, B

illio

n


Models for Predicting NPL Cash Flow

N P L

N e v e r c u r e d

L i q u i d a t i o n

C u r e d

R e d e f a u l t

1 C u r e m o d e l 2 E xp e c t ed t i m e t o

l i q u i d a t i on i n c a s e

c u r e n e ve r h a p p ens

3 L o s s g i ve n d e f au l t i n

c a s e o f l i q u i da t i o n

4 R e d e f au l t m o d e l f o r

c u r e d l o a n s

M o d e l s N e e d e d

Case 1 Auto Loans in Europe


Cured = at least 3 months of being in current after 90DPD

Probability of Getting Cured Thru Time

0%

5%

10%

15%

20%

25%

30%

35%

40%

45%

50%

2014M01 2014M07 2015M01 2015M07 2016M01 2016M07 2017M01 2017M07 2018M01 2018M07

Pro

babili

ty o

f gett

ing c

ure

d

Default date


Redefault Rate is Relatively High for Cured Loans

0.0%

0.5%

1.0%

1.5%

2.0%

2.5%

3.0%

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28

Re

De

fau

lt r

ate

Number of months from cure date


LGD in Case of Not Getting CuredOn average it takes 6 months to liquidation after default

0

10

20

30

40

50

60

70

80

0 50 100 150 200 250

LG

D

Original LTV

0

5,000

10,000

15,000

20,000

25,000

30,000

0

500

1,000

1,500

2,000

2,500

1 5 9

13

17

21

25

29

33

37

41

45

49

53

57

61

65

69

73

77

81

85

89

93

97

Num

ber

of

loans w

ith L

GD

>0 a

nd L

GD

<100

Num

ber

of

loans w

ith L

GD

>0 a

nd L

GD

<100

LGD

Cure Model

for Mortgages in ItalyCase 2


Explanatory Variables of the Cure Model

CLTV (-)

Time since default (-)

Loan Characteristics

GDP annual growth at origination (-)

Unemployment annual growth

at origination (+)

Spread of interest rate

at origination (-)

Macroeconomic Factors

Seasonality

NUTS 1 regions flags

Fixed Effects

Owner occupied flag (-)

Mortgage purpose:

construction flag (-)

Additional


In-sample Fit of the Cure Model

T i m e s i n c e D e f a u l tC LT V


Q&A

Email us at [email protected] or

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qualify for certain types of treatment under U.S. laws. MJKK and MSFJ are credit rating agencies registered with the Japan Financial Services Agency and

their registration numbers are FSA Commissioner (Ratings) No. 2 and 3 respectively.

MJKK or MSFJ (as applicable) hereby disclose that most issuers of debt securities (including corporate and municipal bonds, debentures, notes and

commercial paper) and preferred stock rated by MJKK or MSFJ (as applicable) have, prior to assignment of any rating, agreed to pay to MJKK or MSFJ

(as applicable) for ratings opinions and services rendered by it fees ranging from JPY125,000 to approximately JPY250,000,000.

MJKK and MSFJ also maintain policies and procedures to address Japanese regulatory requirements.

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