Credit Risk Assessment of Corporate Sector in Croatia

1
Credit Risk Assessment of Corporate Sector in
Croatia

• Saša Cerovac, Lana Ivicic
• Croatian National Bank
• Financial Stability Department

2
Structure of the presentation

• Intro motivation and credit risk assessment
  framework
• Data definitions
• Migration matrices
• Logit model
• Applications and further steps

3
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)

4
Data sources

• Two primary databases
• CNBs database with prudential information on
  bank exposures and exposure ratings (quarterly
  frequency)
• Financial Agency (FINA) micro data on corporate
  financial accounts (annual frequency)

5
Data preparation cleaning (I)

• Detailed CNBs 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

6
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)

7
Combining the CNBs and FINAs databases

• Some further data reductions took place in the
  modeling phase due to errors and omissions in
  FINAs database
• Merging CNBs 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 banks exposures towards
  market-oriented corporates)
• Final data set non-balanced panel of 12,462
  observations of binary dependent variable
  default state.

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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)

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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

10
Distribution of rated debtors from June 2006 to
December 2008
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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

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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) k4

where
over horizon
13
Unconditional migration matrices
PD
Degree of rating stability
PR
Note Initial rating in rows, terminal rating in
columns
14
Conditional matrices I
Hypothetical distributions of rating
upgrades/downgrades
15
Quarterly conditional migration matrices II
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 italic4. 4 The
t-statistics is derived from binominal standard
error.
16
Empirical regularities
Probability of default (reversal) in correlation
with credit rating
Historical evolution of PDs across sectors
17
One-year forecasts
Note Initial rating in rows, terminal rating in
columns
18
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

19
Share of firms in default across sectors
20
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

21
Selection of explanatory variables

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

22
Boxplots
23
Scatterplots
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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

25
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

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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

27
Best performing competing models
Indicator
Sector
Liquidity
Financial leverage
Activity
Profit
Size
28
Marginal effects at the means of independent
variables
29
Kernel density estimate of default probabilities
distribution for defaulted and non-defaulted
companies
30
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 AssessmentUpdate

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Model application I (debt)
Cumulative distribution of debt according to the
origin of a creditor
32
Model application II (debtors)
Cumulative distribution of debt according to the
origin of a creditor
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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

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Credit risk assessment in the Croatian National
Bank