•
Liquidity risk. •
Rate risk.
• Exchange risk. •
Credit risk.
The
credit risk
A- Definition
The credit risk is the oldest form of risks on funds markets.
We distinguish it from the two other major types of risks which
financial institutions are submitted, the market risk and the
operational risk. The market risk is the risk that the value
of an asset (a debt) held by a financial institution varies
because of the evolution of the prices on the financial markets.
This risk has different forms: the exchange risk (that modifies
the value assets in currencies of the institution), the rate
risk (that affects the value of rate tools) or the market risk
strictly speaking (which affects the prices of shares, in particular).
For its part, the operational risk is, according to the basel
Committee (2001), « the risk of direct or indirect losses
resulting from an inadequacy or a failure attributable to some
procedures, agents, inner systems or outside events».
It therefore refers to some inefficiencies of the institution
organisation and management.
The credit risk is the risk that the borrower does not pay back
his debt in due time. If it first was a worry only for banking
organisms, it though concerns every firms (in particular through
the claims they grant to their customers, that are some types
of short term loan), and many have to integrate them in their
management in order to minimize it.
This risk has indeed serious consequences for any firm: each
unpaid debt is economically a dead loss that the creditor bears.
Accountably speaking, claims and loans granted to third parties
constitute a specific station in the sheet-balance of the firm
and any negative evolution burden with debt the survival of
the firm at medium or long term. Very early, firms tried to
immunize themselves against this credit risk, and this thanks
to two ways. Upstream, the risk can be the subject of an evaluation
thanks to different criteria and techniques mixing calculation
and intuition. Following this evaluation, firms dispose of means
of protection to minimize or even cancel this economic risk.
In other words, the credit risk is the risk of payment shortage
from the borrower. It also takes various forms and names: counterparty
risk (in the transactions on financial markets and inner banking),
bankruptcy risk or credit risk in the literal sense (in the
transactions on credit markets). On financial markets where
credit tools are the subject of regular listings, the risk of
payment shortage is evaluated thanks to spreads that translate
into monetary terms the plausibility of the execution of the
payment shortage risk. The random evolution of those spreads
constitutes a form of credit risk insofar as it acts on the
market value of those stocks.
It is then not necessary for the shortage to happen to make
the credit risk affects in a negative way the value of an asset
or a portfolio. The rise of the event plausibility is enough,
it can happen, for example, after the degradation of a rating.
This risk can be measured from an individual level, as far as
it affects financial tools or borrowers considered separately.
It can also be measured from the credit portfolio level of an
institution, which leads to take into account correlations between
the risk factors affecting the different components of this
portfolio.
Planning, from the point of view of the financial institution,
the credit risk essentially comes from the uncertainty of losses.
That is why the final subject of credit risk models is to imitate
the distribution of future losses to a given horizon.
B- The credit risk evaluation
The credit risk evaluation first returns to the solvability
question of the considered firm. This solvability depends on
the pure inner elements to the firm, and also on the outside
contextual elements like the geographical localisation, the
global economic situation and the prospects of sectoral evolution.
1) Exogenous data
1.1) Geographical localisation
The consideration of the firm environment is a necessary preliminary
and fundamental in terms of the risk evolution for a firm is
not a self-sufficient entity: it interacts, in a geographical
context more or less large, with a set of outside elements (other
firms, individuals, banks, insurances, States…).
The settlement of the firm (registered office and possible subsidiary
companies) in such country puts the firm under the influence
of the political situation (stale or not), of the local tax
system, the legislation (labour law, business law, regulation
on security and environment…). Despite the situation of
the firm itself, the quality of its settlement and the prospects
of evolution in its country of origin can seriously handicap
it according to the cases, in this way affecting its future.
To improve an estimate of the geographical context of a firm,
the main financial rating agencies, major investment banks and
insurances publish « ratings » where every country
is attributed a mark (number or letter) that sums up the data
considered as pertinent ones. This mark generally comes with
a comment that explains the evaluation that has been done and
it indicates the main favourable and/ or unfavourable factors.
1.2) The sectorial prospects
The good economic health of the firm also figures under the
impact of the sector where it exercises its activity. If a sector
is growing, it certainly augurs an increase of the firm activity
in the following years, whereas a sector in crisis arouses bigger
risks for the firm that works in it; this is rather true in
some particular sectors sensitive to the international context
(raw materials, transport, light industries…). A deep
analysis of such sector gives in the end a good idea of the
prospects for the upcoming years and enables the evaluation
of every firm which work there.
1.3) The macroeconomic situation
It is a matter of globalization and internationalization. This
point mainly concerns the firms and companies that have a strong
international activity. Depending on many markets, making transactions
on several different currencies, they are particularly sensitive
to the hazards of the worldwide or continental economy and to
the often sudden variations of exchange rates between currencies.
2) Endogenous data
It is mainly purely financial criteria that are taking into
account, and simple calculations can offer an almost precise
idea of the ability of a customer/borrower to repay its debt
in due time. Here is a none exhaustive list of “speaking”
data:
• Annual turnover
• Current level of debt (short and long term)
• Operating profit or loss
• Cash-flow generated
• Availabilities (accounts of the firm)
• Net financial expense
The calculation of some basic ratios, from its data, enables
a first evaluation of the solvency of the firm. Thus, if the
connection between indebtedness and the annual turnover is too
important, it would be too risky to accept a new credit. The
weakness of the trading profit compared with the turnover can
also indicates difficulties in the refund of the loans.
C- The credit risk measure
The « expert » systems in force in rating agencies
or banks rely on methods which are essentially qualitative.
On the contrary, the « scoring » models are based
on quantitative methods. Both types of models use accounting
and financial information or qualitative information.
The first are more frequently used to measure the risk of major
customers while the seconds are adapted to the shortage risk
of the retail banking customers and to the ones of small and
medium firms.
« Expert » systems and « scoring » models
are the main tools used in banks to decide the granting of credits,
but also to mark borrowers. Both types of tools aim at the same
targets, but their approaches are very different.
1) The « expert » systems
In « expert » systems, the approach is of a qualitative
nature. It wants to reproduce in a consistent way the rules
of decision-making of the experts in terms of credit or their
risk evaluation system.
We determine those rules in a totally empiric way, by questioning
the experts – the people in charge of credits –
on their practices, by confronting their opinions and by asking
them to confirm the decision-making rules collectively resulting
from those discussions and confrontations.
This set of rules matched with balances will be used to describe
the risk categories of the borrower and to give him a mark.
These systems are in force in banks but also in rating agencies.
Those identical principles preside over the construction of
grading systems of rating agencies and of « expert »
systems used by the credit analysts of banks. The tools are
the same.
Moreover, like banks, rating agencies have a long term perspective.
In the same way that banks appreciate the situation of borrowers
by adopting the depositors perspectives, rating agencies grade
by adopting the ones on the holders of duties and other long-term
creditors or the ones of the insurers.
2) Score models
Score models are more and more used in financial institutions,
notably in retail banks. They have become a common tool of credit
granting to consumption, but they tend to develop to the credit
risk measure of housing, credits for professional and credits
for small and medium firms.
Score models are risk measure tools that use historical data
and statistics techniques. Their aim is to determine the effects
of various borrower characteristics on the shortage risk chance.
They produce « scores » that are marks measuring
the shortage risk of potential or real borrowers. Financial
institutions can use this marks to put in order the borrowers
according to risk categories.
To make a score model, we generally use the history of the borrowers’
old performances, or the ones of the loans they have been granted,
in order to determine which are the characteristics of borrowers
that enable to foresee why a loan will have good performances
in the future.
This information is obtained from the customers’ credit
files or outside sources. A good score model is a model that
affects high scores (poor shortage risk) to borrowers without
any problems whose loans are going well and poor scores to those
whose loans have bad performances.
To reach this aim, score models have to be able to find the
major risk factors; it means those that determine the most the
probability of shortage from a borrower, and to measure the
relative contribution of each factor of shortage risk.
The interest of score models in retail banks is based on several
advantages nowadays. First of all, they enable a mass treatment
of numerous populations of borrowers and their use reduces in
a significant way the duration of the treatment of credit files
(15 days, for most of standard credits). This time-saving is
one of the first factors of cost economy that « scoring
» brings.
Then, « scoring » tools are little costly. A score
costs a few euros, at the maximum. The adoption of the score
also enables credit analysts to concentrate their attention
on other aspects of the customer’s relation and the risk.
Finally, « scoring » tools provide objective measures
of the risk that insure that all borrowers are treated equally
by the people in charge of the customers
