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Credit Risk Assessment of Corporate Sector in CroatiaPowerPoint Presentation

Credit Risk Assessment of Corporate Sector in Croatia

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### 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 assessmentframework
- 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

Distribution of rateddebtors from June 2006 to December 2008

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

PD

Degree of rating stability

PR

Note: Initial rating in rows, terminal rating in columns

Conditional matrices I

Hypothetical distributions of rating upgrades/downgrades

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 italic[4].

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

Empirical regularities

Probability of default (reversal) in correlation with credit rating

Historical evolution of PDs across sectors

One-year forecasts

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:

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

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

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

Cross-border lending effects on credit distribution for defaulted and non-defaulted companiesrisk distribution

"In the presence of the effective credit limits, foreign bankshelp 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) distribution for defaulted and non-defaulted companies

Cumulative distribution of debt according to the origin of a creditor

Model application I distribution for defaulted and non-defaulted companiesI (debtors)

Cumulative distribution of debt according to the origin of a creditor

Further steps distribution for defaulted and non-defaulted companies

- Refinements of theapproach:
- 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 distribution for defaulted and non-defaulted companies

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