Credit Risk Assessment of Corporate Sector in Croatia

Saša Cerovac, Lana Ivičić

Croatian National BankFinancial Stability Department

Structure of the presentation

Intro – motivation and credit risk assessment framework

Data & definitions

Migration matrices

Logit model

Applications and further steps

Objective

Modeling credit risk of non-financial businesses entities: assessment and predicting of the rating

migration probabilities predicting the probability of being in the

default state

A contribution to the development of the CNB's technical infrastructure designed for the credit risk assessment (Figure 1)

Data sources

Two primary databases:

CNB’s database with prudential information on bank exposures and exposure ratings (quarterly frequency)

Financial Agency (FINA): micro data on corporate financial accounts (annual frequency)

Data preparation & cleaning (I)

Detailed CNB’s database available since June 2006 full coverage of the banks and detailed risk classification

Entries for non-residents, non-corporates, non-market based firms, group of activities and unidentified debtors (other debtors and portfolio of small loans) are removed from the population

All exposures towards each single debtor are summed according to their ID number and multiple entries are avoided by prioritizing them according to supervisory actions

Data preparation & cleaning (II)

Exposures towards small debtors – those not exceeding 100,000 kunas (13,500 euros) - are also removed reducing the volatility steaming from group of debtors that

have marginal share in total liabilities of the corporate sector

Negative values (“overpayments”) were treated as no exposure

Sample was stabilized by removal of enterprises entering and/or exiting the database during the period under observation (year, quarter)

Combining the CNB’s and FINA’s databases

Some further data reductions took place in the modeling phase due to errors and omissions in FINA’s database

Merging CNB’s database with annual financial statements of private non-financial companies obtained from FINA reduced sample dataset to 7,719 firms during 2007 and 2008 (covering more than 75% of bank’s exposures towards market-oriented corporates)

Final data set: non-balanced panel of 12,462 observations of binary dependent variable – default state.

Construction of credit rating (I)

The CNB's database provides only information on the risk classification of individual exposures (placements and off-balance sheet liabilities) - no risk classification of debtors

AX - standard A90d – standard, but over 90 days overdue B – substandard (over 90 days overdue) C – delinquent (over 365 days overdue)

Construction of credit rating (II)

The procedure for classifying debtors into distinct risk categories is based on solving a simple optimization problem derived from the risk classification of their total debt to the banking system as a whole

C 50% or more

B

A90d

AX

TOTAL 100% 100% 100%

Rating of debtor C B A90d

50% or more

50% or more

Treshold of 50% maximizes AX rated liabilities to AX rated companies and non-AX rated liabilities to the rest of firms.

Share of exposure of specific risk category

Distribution of rated debtors from June 2006 to December 2008

84,3

6,0

2,1

7,6

AX

A90d

B

C

Definition of default

Following the provisions of the Basel Committee on Banking Supervision (Basel II Accord) and applying general definition of default (Official Journal of the European Union, I.177 p. 113) :

Default state: ratings A90d, B or C

Rating migrations and the probability of default

Migration matrix

• Migration frequency:

• Discrete multinomial estimator:

• Migrations forecast:

• Domestic corporate sector: no absorbing state (reversals are possible); k=4

where

over horizon

Unconditional migration matrices

AX A90d B CAX 95,0 2,0 2,7 0,3A90d 43,0 22,0 32,3 2,6B 10,1 1,8 81,9 6,1C 1,7 0,1 1,3 96,9

AX A90d B CAX 97,5 1,5 0,9 0,1A90d 40,6 43,6 14,9 0,8B 6,0 0,9 90,8 2,3C 1,5 0,2 0,8 97,5

1-Year

1-Quarter

Note: Initial rating in rows, terminal rating in columns

PD

PRDegree of rating stability

Conditional matrices I

Hypothetical distributions of rating upgrades/downgrades

Unconditional distribution

Conditional distribution 1

Conditional distribution 2

Default area

rating change

prob

abili

ty

0

Quarterly conditional migration matrices II

AX A90d B CAX 97,5 1,5 0,9 0,2A90d 34,6 48,2 16,4 0,8B 5,3 0,6 91,9 2,3C 1,2 0,3 0,8 97,7

AX A90d B CAX 97,5 1,5 0,9 0,1A90d 46,5 40,8 12,1 0,6B 8,7 1,5 87,1 2,8C 1,7 0,0 1,3 97,0

AX A90d B CAX 97,5 1,5 0,8 0,1A90d 40,9 42,8 15,4 0,9B 5,6 0,9 91,4 2,1C 1,6 0,2 0,7 97,5

Construction

Non-financial services

a. Migration matrices conditional on economic activity

Industry

AX A90d B C

AX 97,2 1,7 0,9 0,2A90d 45,2 40,2 13,9 0,7B 6,1 1,0 90,3 2,6C 2,3 0,2 0,8 96,7

AX A90d B C

AX 97,8 1,3 0,8 0,1A90d 36,1 47,1 16,0 0,9B 5,9 0,9 91,2 1,9C 0,7 0,2 0,9 98,2

Retardation phase

b. Migration matrices conditional on economic cycle

Acceleration phase

Note: a. Initial rating in rows, terminal rating in columns b. Differences in migration frequencies that are statistically significant (5% level) in relation to the parameters of unconditional matrix are in italic[4].

[4] The t-statistics is derived from binominal standard error.

Empirical regularities

0

20

40

60

80

100

120

AX A90d B C

%

Distribution of debtors according to their rating

Empirical probability of default (1-Y Matrix)

Empirical probability of default (1-Q Matrix)

0,0

1,0

2,0

3,0

4,0

q3/2

006

q4/2

006

q1/2

007

q2/2

007

q3/2

007

q4/2

007

q1/2

008

q2/2

008

q3/2

008

q4/2

008

Def

au

lt r

ate

s, %

Industry

Construction

Non-financial services

Probability of default (reversal) in correlation with credit rating

Historical evolution of PDs across sectors

One-year forecasts

AX A90d B CAX 91,4 2,4 5,4 0,8A90d 53,6 6,3 34,8 5,2B 18,7 2,1 68,0 11,0C 3,4 0,2 2,4 94,0

AX A90d B CAX 93,1 2,5 3,9 0,5A90d 69,6 5,4 22,0 2,8B 21,8 1,6 68,9 7,8C 6,2 0,4 2,9 90,5

Annual forecast of migartion probabilities

Annual Forecast based on 1-Y Migration Matrix

Annual Forecast based on 1-Q Migration Matrix

Note: Initial rating in rows, terminal rating in columns

Modeling default state

Multivariate logit regression

Binary dependent variable yi,t explained by the set of factors X

The probability that a company defaults is

Using the logit function:

Share of firms in default across sectors

0,00

0,02

0,04

0,06

0,08

0,10

0,12

0,14

0,16

0,18

0,20

Agriculture andmanufacturing

Construction andreal estate

Non-financialservice

Total

2007

2008

Selection of explanatory variables

Initial set:

Financial ratios: liquidity (16), solvency (23), activity (12), efficiency (7), profitability (27) and investment indicators (1)

Size variables Sectoral dummies

Time lag: t-1

Correction of outliers: winsorization

Selection of explanatory variables

Univariate analysis

Mean equality test Graphical analysis: scatterplots Univariate logit models: ROC

Boxplots

-1

0

1

2

3

4

5

6

0 1

Cas

h to

tota

l ass

ets

Default

-0.2

0.0

0.2

0.4

0.6

0.8

1.0

1.2

0 1

Sh

are

ho

lde

rs' e

qu

ity to

tota

l ass

ets

Default

0

5

10

15

20

25

0 1

365

/ ac

coun

ts re

ceiv

able

turn

over

Default

0

1

2

3

4

5

6

0 1

(sa

les

+ d

ep

reci

atio

n)

/ to

tal a

sse

ts

Default

Scatterplots

Cash to total assets

00.10.20.30.40.50.60.7

-0.05 0 0.05 0.1 0.15

Percentile range average

Ave

rage

def

ault

rate

Shareholders' equity to total assets

0.00

0.05

0.10

0.15

0.20

0.25

0.30

0 0.1 0.2 0.3 0.4

Percentile range avergae

aver

age

defu

alt r

ate

365 / accounts receivable turnover

0.00

0.20

0.40

0.60

0.80

0 0.2 0.4 0.6

Percentile range average

Ave

rage

def

ault

rate

Sales + depreciation to total assets

0.00

0.10

0.20

0.30

0.40

0.50

0 2 4 6

Percentile range average

Ave

rage

def

ault

rate

ROC

The predictive power of a discrete-choice model is measured through its:

Sensibility (fraction of true positives): the probability of correctly classifying an individual whose observed situation is “default”

Specificity (fraction of true negatives): the probability of correctly classifying an individual whose observed situation is “no default”

ROC curves in univariate analysis

Profitability indicators seem to have highest univariate classification ability: AUCs ranging from 0.69 to 0.75

Among liquidity indicators, the best performing is the ratio of cash to total assets

Funding structure appears to be a good individual predictor of default too: ratios of equity capital to total assets and to total liabilities reach AUC values of above 0.70

Multivariate models

Intermediate choice: 28 financial ratios

Numerous models including different groups of variables were tested

Final multivariate model was chosen among best performing combinations of 3, 4, 5 and 6 explanatory variables + economic activity dummy

Best performing competing modelsModel 3_1 Model 4_1 Model 5_1 Model 6_1 Model 6_4

C 4.41 -0.41 -0.30 -0.17 -0.06(0.22) (0.17) (0.22) (0.22) (0.22)

Construction and real -0.45 -0.26 -0.24 -0.28 -0.30estate dummy (0.06) (0.07) (0.07) (0.07) (0.07)Cash to short-term -0.29liabilities (0.01)Cash to total assets -0.67 -0.67 -0.63 -0.65

(0.04) (0.04) (0.04) (0.04)Shareholders' equity to -1.87 -1.96 -2.17total assets (0.19) (0.19) (0.20)Shareholders' equity to -0.23 -0.27total liabilities (0.01) (0.01)After tax profit + -0.04depreciation to debt/365 (0.00)365 / accounts 0.10 0.11 0.09 0.09receivable turnover (0.01) (0.01) (0.01) (0.01)EBIT to total liabilities -0.17 -0.14

(0.01) (0.01)Sales + depreciation to -0.75 -0.51 -0.37 -0.41total assets (0.04) (0.05) (0.05) (0.05)Sales -0.01 -0.01 -0.01

(0.00) (0.00) (0.00)R2

0.18 0.19 0.19 0.20 0.20AUC 0.79 0.79 0.79 0.80 0.80% of correct 0 71.57 72.37 71.29 74.89 75.90% of correct 1 73.21 71.20 72.99 71.20 69.50% of total correct 71.80 72.22 71.51 74.41 75.05

Sector

Liquidity

Financial leverage

Profit

Activity

Size

Indicator

Marginal effects at the means of independent variables

Variable Coefficient Marginal effect (M) 1 Std.dev.*M

Constant -0,17

Construction and real estate dummy -0,28 -0,02 -0,009

Cash to total assets -0,63 -0,048 -0,020

Equity to assets -1,96 -0,149 -0,032

365/accounts receivable turnover 0,09 0,007 0,003

EBIT to liabilities -0,14 -0,011 -0,011

Sales + depreciation to total assets -0,37 -0,028 -0,029

Sales -0,01 -0,001 -0,0004

Kernel density estimate of default probabilities distribution for defaulted and non-defaulted

companies

0

100

200

300

400

500

600

700

800

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9

Nodefault=1

Nodefault=0

0.01

0.1

1

10

100

1000

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9

Nodefault=1

Nodefault=0

Cross-border lending effects on credit risk distribution

"In the presence of the effective credit limits, foreign banks help arrange direct cross-border borrowing for their clients, typically for the most creditworthy large corporates, leaving the Croatian banks mostly with customers with no other sources of financing.”

IMF (2008): Republic of Croatia: Financial System Stability Assessment—Update

Model application I (debt)Cumulative distribution of debt according to the origin of a creditor

a. Cumulative distribution of debt, 2007 b. Cumulative distribution of debt, 2002

0,0

10,0

20,0

30,0

40,0

50,0

60,0

70,0

80,0

90,0

100,0

1 6 11 16 21 26 31 36 41 46 51 56 61 66 71 76 81 86 91 96

Percentiles

Domestic creditors only

Dominantly domestic creditors

Dominantly foreign creditors

0,0

10,0

20,0

30,0

40,0

50,0

60,0

70,0

80,0

90,0

100,0

1 6 11 16 21 26 31 36 41 46 51 56 61 66 71 76 81 86 91 96

Percentiles

Domestic creditors only

Dominantly domestic creditors

Dominantly foreign creditors

Model application II (debtors)Cumulative distribution of debt according to the origin of a creditor

c. Cumulative distribution of debtors, 2007 d. Cumulative distribution of debtors, 2002

0,0

10,0

20,0

30,0

40,0

50,0

60,0

70,0

80,0

90,0

100,0

1 6 11 16 21 26 31 36 41 46 51 56 61 66 71 76 81 86 91 96

Percentiles

Domestic creditors only

Dominantly domestic creditors

Dominantly foreign creditors

0,0

10,0

20,0

30,0

40,0

50,0

60,0

70,0

80,0

90,0

100,0

1 6 11 16 21 26 31 36 41 46 51 56 61 66 71 76 81 86 91 96

Percentiles

Domestic creditors only

Dominantly domestic creditors

Dominantly foreign creditors

Further steps Refinements of the approach:

Searching for alternative definitions of default Applying alternative estimators and modeling conditionality

of ratings dynamics Examining alternatives for the selection of explanatory

variables Correcting for selection bias using multinomial logit Modeling the event of default (PD) Modeling the event of reversal (PR) Improving explanatory power using macroeconomic

variables (contingent on longer data series)

Model applications: Forecasts of EAD Stress-testing of the corporate sector

Credit risk assessment in the Croatian National Bank

Macro approach

Sectoral approach

EWS

Macroeconomic risk model

Corporate credit risk models

Households credit risk

Bank failiure model

CAMELS downgarde model

Linear probability model (LOGIT)

Migration matrices

Sensitivity of NPLR's

Credit deafult

Sensitivity of financial margin

Capital adequacy