Assessment of default probability in conditions of cyclicality

Totmyanina Ksenia

Moscow, 2014

Actuality• Corporate sector represents a significant part of banking

business worldwide.

• Loans to corporates are a significant part of Russian banking portfolio: to the end of 2013 loans to corporates reached 56% of total credit portfolio and 39% of total assets of Russian banks.

• Number of researches devoted to corporate credit risk estimation is strongly limited, especially for emerging market economies.

• Level of non-performing loans in corporate portfolio is increasing - this fact can lead to instability of Russian financial and banking system

• Construction companies are the most widespread among Russian borrowers and at the same time very exposed to systematic risksObject of research – Russian contracting companiesItem of research – Assessment of default probability

Purpose of researchThe purpose of our research is to develop an empirical model for estimation default probability of potential corporate clients of Russian banks.

The key steps to achieve this purpose are:

• research the different approaches to default definitions• represent the classification of existing models to default modeling,

review the advantages and disadvantages of these models• analyze the nature and sources of the procyclicality effect, represent

the review of available instruments to mitigation of the procyclicality effect

• collect the sample of financial indicators for defaulted and non-defaulted companies and macro factors for the specified period

• execute a statistical analysis to determine the sensetive financial indicators and macro factors

• execute a multivariable analysis to build sets of logit models• analyze the quality and predictive power of final model and represent

the economic interpretation of the observed relationship

Default definitions

There are a lot of approaches:

• Default as non-fulfillment the conditions of the

loan agreement due to inability or unwillingness

of the borrower

• Default as the bankruptcy

• Default based on BIS criteria:

overdue more than 90 days and / or

bank considers that the debtor is unable to repay the

loan

Review of default models

1.2 Market-based models 1.1 Fundamental-based models

1.1.3 Rating-based models

Cohort approach

1.1. 2 Macroeconomics models

Binary choice models

Univariate discrimination

Multiple discrimination

Reduced forms

Structural models 1.1.1 Models based on financial statements

Scoring models

Exogenous factors

Endogenous factors Duration approach

1.3 Advanced models Linear discrimination models

Neuron networks

Fuzzy sets models

Probability of default models

Procyclicality issueProcyclical effect - increased business cycle fluctuations

Sources can be different:

1) Prudential control: for example, capital adequacy requirements increase during periods of recession and reduce during the period of growth

2 ) The behavior of economic agents: for example, lending activity increase in periods of growth and decrease in periods of recession

3) Expectations of economic agents: for example, the risk is underestimated in the periods of growth, and overestimated during recessions

4) The corporate governance system: for example, the KPI systems and bonuses for managers

Procyclicality mitigation instruments

via inputs data via outputs data

EAD conversion

TTC LGD Other parameters

Time horizon

TTC PD

Quantile

Macro factors

Scalar factor

Conter-cyclicality index

Capital buffers

Dynamic provisions

Stress-testing

Mitigations of procyclicality

 

Financial parameters that can be statistically significant

Group of financial factors potentially affecting the level of credit risk: •Size•Profitability•Turnover•Financial stability

We formed a long list of financial indicators from each class above - finally total list consists of 31 indicators

Sample for modeling

• All defaulted companies in constructing industries during 2005-2013 – 159 companies

• Default = bankruptcy • For each defaulted companies we had 3 analogical (same size

and industry) non-defaulted companies – 477 non-defaulted companies

2005 2006 2007 2008 2009 2010 2011 2012 20130

5

10

15

20

25

30

35

40

Defaults dynamics

Univariate analysis: selection of the risk dominant financial indicators

Instruments:1)Analysis and normalization of data (Chebyshev’s

inequality)2)Statistical tests to identify the most descriptive

variables (Student's test, Welch tests, ANOVA test)

More risk-dominant factors:Balance value Return on sales Working capital Share of stocks in current assets Return on assets Profitability of expensesCoefficient of autonomy

Univariate analysis: selection of the risk sensitive macro indicators

Instruments:1)Analysis and normalization of data (Chebyshev’s

inequality)2)Regression models between macro factors and

average default rate (based on S&P data)

More sensitive macro factors:Oil priceExport of goods and services Imports of goods and services Current account Unemployment rateLoans to individuals

Multivariate analysis: a binary choice model

Binary logit-models:

where

set of financial and macro factors

if the company is defaultotherwise

Multivariate analysis

On the basis of selected financial indicators and macro variables, all possible multivariate models were built

The resulting combination was selected based on following criteria:• No significant correlation• Significance of indicators (t-statistic and

F-test)• The highest value Mc Fadden R2

Best model (Mc Fadden R2=32%):

•Stocks in current assets, profitability of expenses, coefficient of autonomy and import are most risk sensitive

•Share of stocks in current assets was included in the quadratic form that led to increase of R2 by 2%

Multivariate analysis: results

Quality of model – classification table

Classification table

Model results

Non default Default

Obs

erve

d Non default 84% (TN) 53% (FP)

Default 16% (FN) 47% (TP)

•Model better predicts no default cases•With the exclusion of macroeconomic

indicators the quality of the model decreases

Thank you for your attention!