Transcript Slide 1

Credit Risk Assessment of
Corporate Sector in Croatia
Saša Cerovac, Lana Ivičić
Croatian National Bank
Financial 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
Share of exposure of specific risk category
C
B
A90d
AX
TOTAL
Rating of debtor
50% or more
50% or more
50% or more
100%
100%
100%
C
B
A90d
Treshold of 50% maximizes AX rated liabilities to AX rated
companies and non-AX rated liabilities to the rest of firms.
Distribution of rated debtors from June 2006 to
December 2008
AX
A90d
B
C
6,0
7,6
2,1
84,3
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
where
•
Migration frequency:
•
Discrete multinomial estimator:
•
Migrations forecast:
•
Domestic corporate sector: no absorbing state (reversals are
possible); k=4
over horizon
Unconditional migration matrices
1-Year
AX
AX
A90d
B
C
A90d
95,0
43,0
10,1
1,7
PR
PD
B
2,0
22,0
1,8
0,1
C
2,7
32,3
81,9
1,3
0,3
2,6
6,1
96,9
Degree of
rating
stability
1-Quarter
AX
AX
A90d
B
C
A90d
97,5
40,6
6,0
1,5
1,5
43,6
0,9
0,2
B
C
0,9
14,9
90,8
0,8
0,1
0,8
2,3
97,5
Note: Initial rating in rows, terminal
rating in columns
Conditional matrices I
Hypothetical distributions of rating
upgrades/downgrades
Unconditional
distribution
Default area
Conditional
distribution 2
probability
Conditional
distribution 1
0
rating change
Quarterly conditional migration matrices II
a. Migration matrices conditional on economic activity
b. Migration matrices conditional on economic cycle
Industry
AX
AX
A90d
B
C
A90d
97,5
34,6
5,3
1,2
Acceleration phase
B
1,5
48,2
0,6
0,3
C
0,9
16,4
91,9
0,8
0,2
0,8
2,3
97,7
AX
AX
A90d
B
C
A90d
97,2
45,2
6,1
2,3
B
1,7
40,2
1,0
0,2
C
0,9
13,9
90,3
0,8
0,2
0,7
2,6
96,7
Construction
Retardation phase
AX
AX
A90d
B
C
A90d
97,5
46,5
8,7
1,7
B
1,5
40,8
1,5
0,0
C
0,9
12,1
87,1
1,3
0,1
0,6
2,8
97,0
AX
AX
A90d
B
C
A90d
97,8
36,1
5,9
0,7
1,3
47,1
0,9
0,2
B
C
0,8
16,0
91,2
0,9
0,1
0,9
1,9
98,2
Non-financial services
AX
AX
A90d
B
C
A90d
97,5
40,9
5,6
1,6
1,5
42,8
0,9
0,2
B
C
0,8
15,4
91,4
0,7
0,1
0,9
2,1
97,5
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
Probability of default (reversal)
in correlation with credit rating
120
Historical evolution of PDs
across sectors
Distribution of debtors according to their rating
Empirical probability of default (1-Y Matrix)
4,0
Empirical probability of default (1-Q Matrix)
100
3,0
Default rates, %
80
% 60
2,0
40
1,0
Industry
Construction
20
Non-financial services
q4/2008
q3/2008
q2/2008
q1/2008
q4/2007
q3/2007
C
q2/2007
B
q1/2007
A90d
q4/2006
AX
q3/2006
0,0
0
One-year forecasts
Annual forecast of migartion probabilities
Annual Forecast based on 1-Y Migration Matrix
AX
AX
A90d
B
C
A90d
91,4
53,6
18,7
3,4
B
2,4
6,3
2,1
0,2
C
5,4
34,8
68,0
2,4
0,8
5,2
11,0
94,0
Annual Forecast based on 1-Q Migration Matrix
AX
AX
A90d
B
C
A90d
93,1
69,6
21,8
6,2
B
2,5
5,4
1,6
0,4
C
3,9
22,0
68,9
2,9
Note: Initial rating in rows, terminal
rating in columns
0,5
2,8
7,8
90,5
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
0,20
0,18
2007
0,16
2008
0,14
0,12
0,10
0,08
0,06
0,04
0,02
0,00
Agriculture and
manufacturing
Construction and
real estate
Non-financial
service
Total
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
3 6 5 / a c co u n ts re c e iv a b le tu rn o ve r
20
15
10
5
(sa le s + d e p re cia tio n ) / to ta l a ss e ts
C a s h to to ta l a ss e ts
5
4
3
2
1
0
S h a re h o ld e rs ' e q u ity to to ta l a ss e ts
Boxplots
6
1.2
-1
0
25
0
0
1
Default
1.0
0.8
0.6
0.4
0.2
0.0
1
-0.2
0
Default
1
Default
6
5
4
3
2
1
0
0
1
Default
Scatterplots
Shareholders' equity to total assets
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
-0.05
average defualt rate
Average default rate
Cash to total assets
0.30
0.25
0.20
0.15
0.10
0.05
0.00
0
0.05
0.1
0.15
0
0.2
0.3
0.4
Percentile range avergae
Percentile range average
365 / accounts receivable turnover
Sales + depreciation to total assets
0.80
Average default rate
Average default rate
0.1
0.60
0.40
0.20
0.00
0.50
0.40
0.30
0.20
0.10
0.00
0
0.2
0.4
Percentile range average
0.6
0
2
4
Percentile range average
6
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 models
Indicator
C
Sector
Liquidity
Financial
leverage
Activity
Profit
Size
Construction and real
estate dummy
Cash to short-term
liabilities
Cash to total assets
Shareholders' equity to
total assets
Shareholders' equity to
total liabilities
After tax profit +
depreciation to debt/365
365 / accounts
receivable turnover
EBIT to total liabilities
Model 3_1
4.41
(0.22)
-0.45
(0.06)
-0.29
(0.01)
-0.23
(0.01)
Model 4_1
-0.41
(0.17)
-0.26
(0.07)
Model 5_1
-0.30
(0.22)
-0.24
(0.07)
Model 6_1
-0.17
(0.22)
-0.28
(0.07)
Model 6_4
-0.06
(0.22)
-0.30
(0.07)
-0.67
(0.04)
-0.67
(0.04)
-1.87
(0.19)
-0.63
(0.04)
-1.96
(0.19)
-0.65
(0.04)
-2.17
(0.20)
-0.27
(0.01)
0.10
(0.01)
Sales + depreciation to
total assets
Sales
-0.75
(0.04)
R2
AUC
% of correct 0
% of correct 1
% of total correct
0.18
0.79
71.57
73.21
71.80
0.11
(0.01)
-0.17
(0.01)
-0.51
(0.05)
0.19
0.79
72.37
71.20
72.22
-0.01
(0.00)
0.19
0.79
71.29
72.99
71.51
0.09
(0.01)
-0.14
(0.01)
-0.37
(0.05)
-0.01
(0.00)
0.20
0.80
74.89
71.20
74.41
-0.04
(0.00)
0.09
(0.01)
-0.41
(0.05)
-0.01
(0.00)
0.20
0.80
75.90
69.50
75.05
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
1000
800
700
Nodefault=1
Nodefault=1
Nodefault=0
600
500
100
Nodefault=0
10
400
1
300
200
0.1
100
0
0.01
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
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
100,0
100,0
90,0
90,0
80,0
80,0
70,0
70,0
60,0
60,0
50,0
50,0
40,0
40,0
Domestic creditors only
30,0
Domestic creditors only
30,0
Dominantly domestic creditors
Dominantly domestic creditors
Dominantly foreign creditors
20,0
Dominantly foreign creditors
20,0
10,0
10,0
0,0
0,0
1
6 11 16 21 26 31 36 41 46 51 56 61 66 71 76 81 86 91 96
Percentiles
1
6 11 16 21 26 31 36 41 46 51 56 61 66 71 76 81 86 91 96
Percentiles
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
100,0
100,0
90,0
90,0
80,0
80,0
70,0
70,0
60,0
60,0
50,0
50,0
40,0
40,0
Domestic creditors only
30,0
Dominantly domestic creditors
Dominantly foreign creditors
20,0
Domestic creditors only
30,0
Dominantly domestic creditors
Dominantly foreign creditors
20,0
10,0
10,0
0,0
0,0
1
6 11 16 21 26 31 36 41 46 51 56 61 66 71 76 81 86 91 96
Percentiles
1
6 11 16 21 26 31 36 41 46 51 56 61 66 71 76 81 86 91 96
Percentiles
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
Sensitivity of
NPLR's
Macroeconomi
c risk model
Corporate credit
risk models
Sectoral approach
Households
credit risk
Bank failiure
model
EWS
CAMELS
downgarde model
Linear
probability
model (LOGIT)
Migration
matrices
Credit deafult
Sensitivity of
financial margin
Capital
adequacy