Page 87 - DCOM505_WORKING_CAPITAL_MANAGEMENT

Working Capital Management

Notes

Notes The risk-rating model should capture various types of risks such as Industry/
Business Risk, Financial Risk and Management Risk, associated with credit.
Industry/Business risk consists of both systematic and unsystematic risks which are market
driven. The systematic risk emanates from General political environment, changes in economic
policies, fiscal policies of the government, infrastructural changes etc. The unsystematic risk
arises out of internal factors such as machinery breakdown, labour strike, new competitors who
are quite specific to the activities in which the borrower is engaged.

Assessment of financial risks involves appraisal of the financial strength of a unit based on its
performance and financial indicators like liquidity, profitability, gearing, leverage, coverage,
turnover, etc. It is necessary to study the movement of these indicators over a period of time as
also its comparison with industry averages wherever possible. A study carried out in the western
corporate world reveals that 45% of the projects failed to take off simply because the personnel
entrusted with the test were found to be highly wanting in qualitatively managing the project.
The key ingredient of credit risk is the risk of default that is measured by the probability that
default occurs during a given period. Probabilities are estimates of future happenings that are
uncertain. We can narrow the margin of uncertainty of a forecast if we have a fair understanding
of the nature and level of uncertainty regarding the variable in question and availability of
quality information at the time of assessment.

The expected loss/unexpected loss methodology forces banks to adopt new Internal Ratings
Based approach to credit risk management as proposed in the Capital Accord II. Some of the risk
rating methodologies used widely is briefed below:

1. Altman’s Z score Model involves forecasting the probability of a company entering
bankruptcy. It separates defaulting borrower from non-defaulting borrower on the basis
of certain financial ratios converted into simple index.
2. Credit Metrics focuses on estimating the volatility of asset values caused by variation in
the quality of assets. The model tracks rating migration which is the probability that a
borrower migrates from one risk rating to another risk rating.
3. Credit Risk +, a statistical method based on the insurance industry, is for measuring credit
risk. The model is based on actuarial rates and unexpected losses from defaults. It is based
on insurance industry model of event risk.
4. KMV, through its Expected Default Frequency (EDF) methodology derives the actual
probability of default for each obligor based on functions of capital structure, the volatility
of asset returns and the current asset value. It calculates the asset value of a firm from the
market value of its equity using an option pricing based approach that recognizes equity
as a call option on the underlying asset of the firm. It tries to estimate the asset value path
of the firm over a time horizon.
The default risk is the probability of the estimated asset value falling below a pre-specified
default point.
5. McKinsey’s credit portfolio view is a multi factor model which is used to stimulate the
distribution of default probabilities, as well as migration probabilities conditioned on
the value of macro economic factors like the unemployment rate, GDP growth, forex
rates, etc.

In today’s parlance, default arises when a scheduled payment obligation is not met within 180
days from the due date and this cut-off period may undergo downward change. Exposure risk is

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