CREDIT RISK MEASUREMENT AND
MANAGEMENT OF THE
LOAN PORTFOLIO
LEARNING OBJECTIVES
By the end of this chapter you should be able to:
1.
2.
3.
4.
5.
6.
7.
8.
9.
describe the benefits of credit risk management
explain and use Altman’s score
explain how stock prices can be used to explain credit risk
explain how actuarial approach can be used by examining CreditRisk+
explain how a macroprudential approach can be used by looking at CreditPortfolio view
suggest how risk-adjusted return on capital can be used for portfolio purposes
use the Sharpe Index for lending purposes
calculate the risk of a loan portfolio using CreditMetrics™
understand the elements of loan pricing.
z
KEY TERMS
z
Altman score
credit default swap
credit migration
default point
option modelling
put option
Sharpe Index
CreditRisk+
call option
credit derivatives
credit options
duration
pass through
risk-adjusted return on capital
CreditPortfolio
CreditPortfolio View
375
concentration risk
credit event
CreditMetrics™
expected default frequency
pay through
securitisation
zone of ignorance
376
INTRODUCTION
As mentioned in the credit scoring chapter, mathematical modelling is increasingly being used
for the measurement of credit risk (see Chapter 3). Originally, lending institutions did not
have departments that took an institutional perspective of the lending portfolio. Loans were
granted and were promptly forgotten as long as borrowers made their scheduled repayments.
These practices, however, led to loan portfolios that were not close to the efficient frontier. The
efficient frontier is a finance concept. In brief, it maps the return that an investor should receive
for a given level of risk. In terms of lending, it means receiving an appropriate return for a given
level of lending or credit risk. The aim of credit risk management is to balance between risk and
return to achieve optimum profitability and efficiency. Bank managements realised that they
were not doing this.
Exacerbating this situation was the rise of relationship banking. This form of banking was based
on a manager developing close links with an entity or individual. Often, lenders would lend too
much to one entity relative to the rest of the lending portfolio. This is known as concentration
risk. (We address concentration risk in Chapter 16 - Quantitative Finance.) The underlying
risks may be sound, but there is a danger when the economy undergoes turmoil. Any bank that
was overly exposed to the airline industry in September 2001 might have had problems due to
the downturn in the airline and ancillary industries. This is the same for the mining industry
leading up to 2017. Not all would agree that concentration risk is a bad thing. Some have pointed
out that small financial institutions that focus their business choose to wear concentration risk
because they have particular expertise. Many regional building societies, for example, have
concentration risk in terms of their credit type and geography.
Lenders recognised that lending needed to be conducted on a more scientific basis, rather than
relying on the lender-borrower relationship. This change would remove the subjective nature of
lending. Further, rather than managing loans on an individual basis, lenders needed to manage
loans on a portfolio basis, just like any other investment portfolio, such as bonds or equity.
Finally, banks questioned what happened when there were identified loans that were not
appropriate to the statement of financial position. This question has given rise to the new credit
management techniques of securitisation and credit derivatives. Credit risk management helps
a bank to achieve the following objectives: (a) achieve an appropriate balance between risk and
return, (b) avoid concentration risk, (c) manage loans on a portfolio basis and (d) take a group of
loans off the statement of financial position.
Before credit risk can be managed, it is important to know the extent to which the financial
institution is exposed to it. This knowledge is achieved by measuring credit risk. As you read
this chapter, keep in mind that modelling is being developed at a rapid rate; the models we are
looking at today are the most popular and have regulatory approval. In the next section, we
discuss the various techniques of the measurement of credit risk:.
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CREDIT RISK MEASUREMENT
Measurement of credit risk refers to the quantification of credit risk exposure of a bank. It has a
number of useful benefits, such as:
■
removing the subjectivity from credit assessment
■
adding a scientific basis to the credit assessment process
■
rating loans that have no credit ratings or providing a system of grading
■
providing a mechanism for monitoring loans to ensure they are not potential problem
loans.
Many credit risk measurement models are available today. Many have been internally developed
by lending institutions, while some are available as 'off-the-shelf solutions to lending institutions.
Lending institutions often use an 'off-the-shelf solution as a check against an internal model.
While there is a plethora of models, most are based on either accounting ratios or information
contained in share prices. In the recent past, given the limitations of some these models, others
have been proposed. These include using actuarial approaches and incorporating endogenous
variables. Keep in mind that the development of all these models indicates that none of them
are prefect.
z
In this chapter, we will examine four models that use these approaches. The Altman score
was one of the earliest models used by banks in an attempt to remove the subjectivity from the
lending decision. It examines the accounting ratios of two populations—failed and non-failed
companies—to assess credit risk. Despite questions about its “look back” methodology, it is
relatively robust and is still used used.
Another methodology has been the use of information implied in share prices and option pricing
theory to predict default. This approach has been popularised by KMV Corporation.
More recent developments have been the use of actuarial methodologies as proposed by Credit
Suisse's CreditRisk+ model and the incorporation of economic variables as developed by
McKinsey's Credit Portfolio View model.
ALTMAN'S z SCORE
Altman's (1993) work was predicated by work by Beaver (1967), who found that firm bankruptcy
could be predicted by the use of financial ratios for up to five years before bankruptcy. Beaver
used univariate models that distinguished between failed and non-failed firms for periods of up
to five years in advance. This mathematical method was derived from the biological sciences,
which define populations with different characteristics.
Table 11.1 below shows a strong relationship between credit ratings and key financial ratios.
The differences between the ratios of AAA-rated organisations and B-rated organisations are
quite marked.
378
To some extent, the use of accounting ratios in such analysis is a logical extension of original
credit analysis where a person would be the mechanism of analysis, generating and using the
ratios. Altman and others extended the work of Beaver to improve the predicative power of such
models to predict default and/or bankruptcy. The analysis was improved by extending single
univariate models to multivariate models that used a number of financial ratios.
A survey of the research over the past thirty years has sought to identify which accounting ratios
provide the best predicative power, including:
■
activity
■
liquidity
■
solvency
■
profitability
■
earnings variability
■
size.
Table 11.1 Relationship between ratings and financial ratios
Adjusted key industrial financial ratios - 1
US industrial long-term debt
Three-year medians (1998-2000)
EBIT interest coverage (x)
EBITDA interest coverage (x)
Free operating cashflow/
total debt (%)
Funds from operations/total debt (%)
Return on capital (%)
Operating income/sales (%)
Long-term debt/capital (%)
Total debt/capitalisation
(incl. short-term debt) (%)
Companies (no.)
Credit rating
AAA
AA
A
BBB
BB
B
ccc
21.4
26.5
10.1
12.9
6.1
9.1
3.7
5.8
2.1
3.4
0.8
1.8
0.1
1.3
84.2
25.2
15.0
8.5
2.6
(3.2)
(12.9)
28.8
34.9
27.0
13.3
55.4
21.7
22.1
28.2
43.2
19.4
18.6
33.9
30.8
13.6
15.4
42.5
18.8
11.6
15.9
57.2
7.8
6.6
11.9
69.7
1.6
1.0
11.9
8.8
22.9
37.7
42.5
48.2
62.6
74.6
7.7
8
29
136
218
273
281
22
a times number (that is, a number to multiply by)
EBIT = earnings before interest and tax;
EBITDA = earnings before interest, tax, depreciation and amortisation.
X=
Source: Standard
Poor's, www.standardandpoors.com.
Depending on the industry generally, each of the above ratios has strong predicative power. As
a general rule, however, liquidity, profitability and earnings variability have strong predicative
power. This is easy to explain. Cash repays loans, so those ratios with a strong cash component
are the ones that indicate the potential for debt defaults.
379
In the remaining part of this section, we will concentrate on the development of the Altman
score as a basis for considering later developments.
The Altman
z
z score is given as:
z = 1.2X, + 1.4X2 + 3.3X3 + O.6X4 + 0.999X5
where
X, = the working capital divided by total assets
X2= the retained earnings divided by total assets
X2 = the earnings before interest and taxes divided by total assets
X4 = the market value of equity divided by the book value of total liabilities
X5= sales divided by total assets.
The first four variables are expressed as decimals while the fifth is expressed as a times number
(for example, 5 x) as opposed to a percentage or decimals. We will explain the interpretation of
the result of this score later, because first it is instructive to understand why these particular
variables were chosen.
Altman’s initial work showed that many ratios were reasonable indicators of the potential
for default. They were further categorised into the categories of activity, liquidity, solvency,
profitability, earnings variability and size. Altman chose, however, to concentrate on those
ratios that were well accepted in the academic literature. He reduced the number of variables to
twenty-two and used the following method:
1.
2.
3.
4.
Altman observed each statistical significance of various alternative functions, including
determining the relevant contributions of each independent variable.
He evaluated the intercorrelation among the relevant variables.
He then observed of the predicative accuracy of the various profiles.
He used his own judgement when finalising the function.
It needs to be recognised that the resulting function, as stated above, provides a good discriminant
between failed and non-failed companies but is not necessarily optimal. The categorisation of
these ratios then becomes as shown in Table 11.2.
The following issues should be considered in the use of this technique:
1.
2.
3.
4.
It is heavily biased towards US data, so an examination of Australian data may create a
different score.
The function implies a heavy bias towards financial ratios that indicate the firm’s ability to
create cashflow. This is obviously satisfying given that cashflow repays debt.
There are obvious problems with the use of financial ratios:
a. They are open to manipulation by firms.
b. They are also open to interpretation by analysts.
The function is independent of loan amount, which alerts us to the need for supplementary
analysis of the statistical method.
z
380
Table 11.2 Ratio categorisation
Variable
x,=
ie
x3 =
Category
Liquidity
Profitability
Profitability/
productivity
Leverage/solvency
Activity
It is also instructive to picture graphically (Figure 11.1) the results of the discriminant function,
with the objective of having the groups as different as possible.
Figure 11.1 Multidiscriminant distributions using z scores
The bounds of the two distributions are 1.81 and 2.99. A firm that scores 2.99 or above, therefore,
would be considered to be to be creditworthy, while one below 1.81 would be considered to be
non-creditworthy. The difficulty for lending officers is with scores that fall between 1.81 and
2.99. This is known as the zone of ignorance and represents sampling errors between the two
populations. In this zone, good loans could be classified as bad, and bad loans could be classified
as good.
Statistically, the former is known as a type 2 error while the latter is known as a type 1 error.
Each situation is an opportunity cost to the lender, although the latter has a more serious explicit
381
cost. Ways of dealing with the zone of ignorance differ among lending organisations, but the
following would be considered:
■
the risk profile of the lending organisation
■
the lending organisation’s relationship with the potential borrower
■
the judgement of the analyst.
The following example should assist in your understanding of this technique. Imagine you are a
lending officer with XYZ Bank. You are given the following information about Company ABC:
Table 11.3 Company ABC
Assets
Current assets
Noncurrent assets
Shareholders’ funds
Liabilities
Current liabilities
Noncurrent liabilities
$10
$20
$6
$9
$15
You also know that earnings before interest and tax are $2 and sales are $35. Would you lend to
the company if you followed the Altman method?
Table 11.4 Company ABC and the Altman method
Result
Variable
(10-9)
X,
30
6
-r30
*3
=0.037
= 0.2
2
—5— =0.067
30
6
x5
35
...
30
=1.67 times
Before providing the solution to this example, we should point out that such a calculation may
involve making assumptions. This is not unusual, but care should be exercised in arriving at the
382
assumptions. In this example, we have assumed (given lack of information, which also is not
unusual) that:
■
the equity on the statement of financial position is all retained earnings, and
■
the equity on the statement of financial position is also the market value of equity.
We can now complete the exercise. Using the formula for Altman’s
z score, we have:
z = 1.2X, + 1.4X2 + 3.3X, + 0.6X4 + 0.999X5
= 1.2(0.037) + 1.4(0.2) + 3.3(0.067) + 0.6(0.25) + 0.999(1.67)
= 2.36
The score falls into the zone of ignorance (between 1.81 and 2.99). Depending on XYZ Bank's
tolerance in the zone of ignorance, there is a strong likelihood that you would lend to this
company. If, however, you made different assumptions regarding equity (retained earnings and
the market value of equity), then the score would most likely fall below the lower bound of 1.81.
z
z score was developed in 1968, there have been developments in its form:
A private firm z score has been developed, which takes into account that the market value
Since the
•
■
■
of equity is not available. The major change is in X4, where the book value of equity is
directly substituted for the market value of equity.
It has been recognised that non-manufacturing firms, without assets, are prejudiced against
because assets are used in many of the ratios. Two major changes are thus made: X5 is
dropped and the book value of equity is used for Xj.
Finally, a second generation model, known as ZETA, was developed in 1977 to account for
changes in business failure. The most significant change in this model is the recognition
that the size of the firm makes a difference. The general result has been that the larger the
firm, the less likely it is to fail. This results occurs because smaller firms are often newer and
more likely to enter bankruptcy.
While these newer models have different functional forms given their different variables, they
follow the same principles. Finally, it should be noted that many other models follow the same
techniques, particularly credit scoring, in assessing credit risk.
Using stock prices
The criticism of Altman-style models, which use financial ratios, is that they use data from
financial reports. The data are thus 'old' and backward looking, whereas default is about looking
forward to potential defaults. If we consider, however, that the market price of assets and
the market value of debt are forward looking, then using this information, we may be able to
calculate the default probability. This is how KMV Corporation’s expected default frequency
model works, using option modelling.
The lending proposition of a bank is to lend money to an organisation that uses the funds. If the
firm uses the funds well, then the value of assets will rise and the borrower will repay the loan.
If, however, the funds are used badly, then the asset value will fall, the value of the asset may be
383
unable to repay borrowings and a default will occur. If we graph this scenario, we find Figure
11.2. (Note that there is an upper bound on returns from lending.)
Figure 11.2 A sold put option in bank lending
Those who are familiar with option modelling will see that this is a sold put option on assets
where the equity value will be a function of the market value of assets and its volatility, the
liabilities of the firm, interest rates and the term to maturity, in terms of option pricing. The
default decision becomes an issue of how close the market value of assets is to the default point.
The aim then becomes to predict the probability that asset values will fall below the default
point.
If we look at the lending decision from the equity holder's point of view, then the pay-off diagram
shown in Figure 11.3 would result.
Figure 11.3 A call option representing the equity holder's interest in bank lending
384
Interpreting this graph is simple. When borrowing money, the equity holders would repay their
loan if the asset value were greater than the liabilities. Again, those with a familiarity with option
pricing will see that this is a call option on the assets of the company.
The difficulty with the approach is that there are two unknowns in the formulation; the market
value of assets and asset volatility. If we exploit the relationship between assets and equity in
the option pricing formula, as well as the volatility, then we can solve the two unknowns. The
functional forms would be:
Price of risky debt = Option function (asset value, asset volatility,
capital structure, interest rate, term to maturity)
Price of risky debt = Option function (asset value, asset volatility,
capital structure, interest rate, term to maturity)
We are assuming that the asset value and its volatility can be inferred from the option pricing
model. We can now solve the two equations and find the values attached to assets and volatility.
How do we use this number? The following principles apply.
The net worth of an organisation is simply the market value of assets less than the default point.
Under the popular KMV Corporation method, the default point is all the current liabilities
plus half the long-term liabilities. This recognises that not all debt is due at the same time; the
presence of long-term debt does provide breathing space in times of financial stress. The net
worth, however, should be considered in terms of business risk. In other words, some firms
can afford greater leverage (that is, risk) than others can. The market of assets magnifies the
effect of the volatility on the asset size. We can, however, now calculate how far we are from
default:
Distance of default =
(Market value of assets) - (Default point)
(Market value of assets) (Asset volatility)
This provides the distant to default, or the number of standard deviations to default. Once we
have this figure, we can estimate the default probability. It is important to understand that some
of this technology is proprietary to KMV Corporation, which maintains a database of 100 000
companies and 2000 incidents of default or bankruptcy.
If our distance to default turns out to be four standard deviations, for example, then the KMV
Corporation database will indicate the proportion of firms with four standard deviations
distance to default that actually defaulted. This proportion is known as the expected default
probability:
Expected default probility =
Number of default firms
All firms of sample
385
The following sample estimation of default is taken from a technical document made available
by KMV Corporation. There are three steps:
1.
2.
3.
estimate the current value and volatility of the firm’s assets
determine how far the firm is from default (its distance from default)
scale the distance to default to a probability.
Consider Philip Morris Companies Inc., which at the end of April 2001 had a one-year estimated
default frequency of 25 basis points (0.25 percent). The calculation made is as shown in Table
11.5.
Table 11.5 Sample calculation of estimated default frequency - Philip Morris Companies Inc.
Variable
Market value of
equity
Book liabilities
Market value of
assets
Asset volatility
Default point
Value
$110688
million
$64 062
million
$170 558
million
21%
$47 499
million
Distance to
default
3.5
How calculated/information accessed
Share price (accessed from the stock market) X
shares outstanding (from the annual report)
Statement of financial position (from the annual report)
Derived from the option pricing model*, as described earlier in
the chapter
Derived from the option pricing model, as described earlier in the
chapter
Calculated as all short-term liabilities plus half the long-term
liabilities, as described earlier in the chapter
Calculated from the ratio of (170 - 47) (70 X 21%), which comes
from the formula using the information derived above:
/
Distance of default =
Estimated default
frequency
25 basis
points
-
(Market value of assets) (Default point)
, ,
,
,
.
, ... (
(Market value of assets) (Asset volatility)
Empirical mapping between-distance to default and default
frequency
* KMV uses a standard Black-Scholes option pricing model. Students wishing to examine this model should
refer to a text on pricing derivatives.
Again, we note that the default probability, as in the Altman case, is independent of the loan size.
Actuarial Approaches
Many methodologies arise due to the limitations of prevailing methodologies. CreditRisk+ is
no different,
scores have the issue of back looking accounting figures and an assumption
of KMV is dependent on the capital structure. The underlying assumption of Credit Suisse's
CreditRisk+ is that there are no assumptions on why loans default except they do.
z
So, CreditRisk+ model, in its simplicity predicts whether a loan defaults or not and builds a
distribution around this framework. While the statistics involved in this approach are beyond
386
this text, it is noted that the methodology of this approach is well represented by a Poisson
distribution. The approach then takes a three building block approach to predicting credit
default, both on an individual and portfolio basis. This approach is found in Figure 1.
Figure 1 CreditRisk+ risk measurement framework (Source CreditRisk+)
Input
• default rates
• default rates/volatilities
Building Block #1
• exposures
• recovery rates
Building Block #2
Stage 1
Sage 2
Building Block #3
Building block number 1 identifies the average default rate for each obligor banding. Obligator
bandings are normally based on credit ratings. It is obvious that average default rates have similar
problems as other methodologies. Building block number 2 adjusts the average default rate for
individual exposures and expected recovery rates. Building block number 3 then calculates the
expected default losses.
In terms of intuition, this model is attractive given it provides a yes/no answer. However, it does
have its deficiencies. The main one is its strength. While it provides the yes/no result, it takes
no account to credit migration.
CREDITPORTFOLIO VIEW
In previous chapters, we examined the business cycle and its relationship to lending.
Creditportfolio View uses the assumption that lending decisions are connected to the state of
the economy, that is, the business cycle. Therefore, implicit to the model, are macroeconomic
variables such as: unemployment rates, GDP growth, interest rates, foreign exchange rates,
government expenditures as well as savings rates.
Before looking at this methodology, two comments need to be made as regulation may affect
this methodology. Firstly, Basel 3 has through the cycle debt provisioning where probabilities
are assigned for the possibility of debt default. Secondly, the rise of macro-prudential
supervision means that regulators are acknowledging the effect that these types of variables
have on lending.
387
Again, the statistics from this methodology are beyond the scope of this text, however, it is easily
described. Default is modelled on a logit function where the dependent variable is a risky default
with the independent variable an index based on current and lagged macroeconomic variables.
The index itself is estimated by multi-factor model. The logit model is then “corrected” for
transition probabilities indicating when the business cycle changes.
PORTFOLIO MANAGEMENT
While much of this book is devoted to the analysis of a single loan or a number of loans to the
same borrower, attention has been given in recent years to the effect (in terms of the risk-return
pay-off) of adding loans to an existing portfolio of loans. Concentration risk has gained much
attention in this regard.
A portfolio of loans can be viewed as a portfolio of assets, and the proposition has been that
managers can use modern portfolio theory to manage the loan portfolios. The issue becomes
how to manage new loans that are highly positively correlated to the rest of the portfolio.
In good economic times, this is not a problem; if there is an economic downtown, however,
the majority of the loan portfolio could be exposed to the same factors. In particular, smaller
financial institutions that operate in a limited geographical area are subject to this risk.
Portfolio management of loan portfolios is nevertheless a relatively new phenomenon and there
are questions about the relevance of modern portfolio theory to lending portfolios.
■
•
■
Are the assumptions under which modern portfolio theory operates applicable to loans?
■
Modern portfolio theory assumes that the distribution of returns is normal. Loans,
however, are characterised by having a one-sided distribution because the minimum
return is assumed to be zero in default.
•
Modern portfolio theory assumes that all the assets in the portfolio can be revalued.
This is simple in equity markets because shares operate on a share market. For bank
loans, however, this is difficult because the loans are not traded on an exchange.
Is the environment under which modern portfolio theory operates the same as that for
lending portfolios? In terms of equity portfolios, for example, investors have the option of
purchasing or not purchasing a share at a given price and return. Lending portfolio managers
do not have such luxury, because they inherit loans approved by lending managers.
Is concentration risk a problem? It could be argued that concentration risk is a result
of concentrating lending in expert areas and that diversification could be conducted in
different ways, such as by geographic region rather than by industry or company type.
There are a number of different models for portfolio management, including the risk-adjusted
return on capital, the Altman Sharpe Index approach and CreditMetrics™. The next sections are
not designed to illustrate the flaws of existing methods, but will explain the approaches used.
There is no doubt that better methods will become available.
388
Risk-adjusted return on capital
One of the earliest attempts to address the problem of approving loans was prompted by the
introduction of capital adequacy. While lending institutions are required to set aside capital
for each class of loan for capital adequacy purposes, not all loans are equal. If two loans—one
housing and the other corporate, for example—have the same interest rate, then the lending
institution would prefer the housing loan because less capital would need to be put aside under
the current capital adequacy guidelines (although changes to the guidelines are proposed).
Bankers Trust (BT) originally developed the risk-adjusted return on capital. It recognised that
capital was put aside for each loan and suggested that a hurdle rate for loans needed to be
achieved before a lender added the loan to the portfolio. The approach is a return on equity
approach, rather than portfolio approach, but has been used for portfolio purposes. It can be
justified on the basis that loans of various risk classes are added to the portfolio if they provide
the appropriate return. This can be expressed as:
Risk adjusted return on capital =
Income from the loan for one year
Capital at risk
The formula is deceptively easy but has many treatments. The following questions are among
the issues:
What income from the loan should be included? If the loan attracts further business, should
this income be included?
How is capital at risk defined? BT uses a duration number (see below), but many other
methods can be used. A more accurate representation that could be used is the value-at-risk
calculation derived by CreditMetrks™.
■
■
BT uses a simple duration number to define the capital at risk as follows:
AL
ar
where
AL
L
= the percentage change in the market value of the loan over the period
-D'l = Macauley duration (which is the weighted average receipts on a
security or loan, where the weights are present value)
= the maximum discounted change in the credit risk premium over the period'
389
It can be re-arranged to be:
AL = Dl X L X
AR
1+Rt
where
AL = the capital at risk.
A simple example is as follows: the base rate for a loan is 8 percent and the loan also has various
AR
fees that total 0.5 percent. The duration of the loan is two years - ' p — and is 100 basis points.
1+rl
The loan amount is $100 000.
The income is the total of the various fees applied to the loan amount:
0.5% X $100 000 = $500
The capital at risk is as follows:
AL = 2 X 100 000x 1%
= 2000
therefore:
T,. ,
,. . , .
.. ,
500
Risk - adjusted return on capital = ■
- =25%
1
F
2000
As long as the lending institution’s hurdle rate (return on equity) is less than 25 percent, then the
loan would be added to the portfolio. Hurdle rates are normally defined by lending institutions
as the return on equity.
These processes have been enhanced over the years. The most used risk-adjusted return on
capital method was developed by Bank of America. There major departure Bank of America
make from the BT methodology is the recognition that the capital at risk is unexpected losses.
This is calculated by Bank of America as:
CI X ơ. X LGD. X Exposure
Where
■
CI is the confidence interval used by the financial institution;
■
ơ| is the standard deviation of similar types of loans;
•
■
LGD. is the loss given default for the loan type; and
exposure is the principal.
Bank of America uses a confidence interval of six. However, rating agencies, who have stated
a preference for the Bank of America approach, consider ten to be the appropriate number to
obtain the top credit rating.
390
The last issue that we need to consider is that of scarce capital. A financial institution cannot
continually lend, because capital is finite. What does a lender do if, for example, capital is
exhausted and a loan application exceeds the required hurdle rate? The obvious answer would
be to raise new capital, but this takes time. This is where the portfolio approach applies. Most
financial institutions would allocate marginal capital for each loan, in recognition that it takes
time to raise new capital for lending purposes.
Altman'S Sharpe Index approach
Altman's approach to portfolio management is characterised by the Sharpe Index where the
portfolio return is adjusted by the portfolio risk. This approach is not unlike the risk-adjusted
return on capital for a single loan. Altman's approach is simple: optimise the Sharpe Index
subject to various constraints. It is important then to identify the various components.
The portfolio return is the weighted average return of each asset in the portfolio. This recognises
that the risk that a loan brings to a portfolio depends on the amount of the loan relative to the
portfolio. The return of each asset is defined as the promised yield to maturity, less expected
annual loss. There are many ways in which to calculate the expected annual loss, with many
financial institutions using their historical experience for each loan type. To calculate annual
losses, Altman uses an insurance concept called mortality rates and losses. This is the risk
premium implied by the default experience of the credit rating of the loan. Financial institutions,
therefore, would need to develop ratings for those loans without a credit rating.
Using Altman’s terminology, the problem can be broken into steps. The first step is to calculate
the return on the portfolio, using the following equation:
N
R=
p
£
i=
X,EAR,
i
i
1
where
R = the return on the portfolio
X. = the proportion of each asset invested
EAR, = the expected annual return.
The next step is to calculate the variance of the portfolio using the following equation:
N
N
z
z
i=
1
Wm
j. 1
where
V. = the variance of the portfolio.
391
Then, maximise the following relationship, which is the Sharpe Index:
where
N
s x-1
i=
1
Rp > the target return
X<
the individual bond investment limit.
Care must be taken when using this and other techniques, to ensure adequate data are available,
particularly for portfolio variances. Otherwise, other forms of variance would need to be used.
CreditMetrics™
CreditMetrics™ is a portfolio method that is growing in popularity. Its great attraction is that
it incorporates the fact that credit risk changes over time. The technique would be familiar
to those who have an exposure to JP Morgan's RiskMetrics™ and value-at-risk methods.
CreditMetrics™, also developed by JP Morgan, seeks to model portfolio risk by tracking value
changes in lending assets by assessing the probability of credit changes.
CreditMetrics™ is best understood by using an example. The following example is taken from
a JP Morgan technical document (available for free on the website www.riskmetrics.com.au).
Suppose we have a BBB-rated bond with a maturity of five years. The bond has a coupon of
6 percent. The credit rating of this bond over a period of time can rise to AAA, fall to ccc or
default. Each rating change has a probability, as shown in Table 11.6.
Table 11.6 One-year transition matrix
Initial rating
AAA '
AA
A
BBB
BB
B
ccc
AAA
90?81
0.70
0.09
0.02
0.03
0.00
0.22
AA
8.33
90.65
2.27
0.33
0.14
0.11
0.00
A
R.a ting at y ear end (i<')
- BB_ . ..
" 0.68
7.79
91.05
5.95
0.67
0.24
0.22
Source: Standard Poor's 1996, CreditWeek, 15April.
...»...
BBB
0.06
0.64
5.52
86.93
7.73
0.43
1.30
’
07’12
" Ó.OÓ
0.06
0.74
5.30
80.53
6.48
2.38
0.14
0.26
1.17
8.84
83.46
11.24
ccc
Default
0.0Õ
0.00
0.02
0.01
0.12
1.00
4.07
64.86
0.00
0.06
0.18
1.06
5.20
19.79
392
Table 11.6 shows that the most obvious scenario will be that the bond stays at the same rating,
with little probability of it moving higher. (These credit migration probabilities are available
from Standard & Poor's.)
Given a yield curve and assuming that each credit rated bond has a coupon of 6 percent, we can
calculate the value of each bond as shown in Table 11.7.
Using a zero coupon yield curve, we can calculate the zero coupon rates as shown in Table 11.8.
Table 11.7 Distribution of value of a BBB par bond in one year
Year end rating
AAA
AA
A
BBB
BB
B
ccc
Value (s)
Probability (%)
109.37
0.02
0.33
109.18
108.66
5.95
107.55
86.93
102.02
5.30
98.10
1.17
0.12
83.64
Source:JPMorgan 1997, CreditMetrics™—TechnicalDocument, www.riskmetrics.com.
Table 11.8 Zero coupon rates, by credit rating category
Category
AAA
AA
A
BBB
BB
B
ccc
1 Year 1 (%)
3.60
3.65
3.72
4.10
5.55
6.05
15.05
Year2 (%)
4.17
4.22
4.32
4.67
6.02
7.02
15.02
Year3(%) j Year 4 (%)
4.73
5.12
5.17
4.78
4.93
5.32
5.25
5.63
7.27
6.78
8.03
8.52
14.03
13.52
Source: JPMorgan 1997, CreditMetrics™—Technical Document, www.riskmetrics.com.
The above interest rates are taken from the corporate bond market for each of the credit ratings.
Note that zero coupon rates are used rather than par coupon rates. While standard discounted
cashflow method is used to reach the above values, you need to note the following treatments
used by CreditMetrics™:
■
The first year's cashflow is not discounted.
■
The subsequent cashflows are discounted on an annual basis, despite most bonds being
semi-annual in nature.
■
The value of the bond in default is not a discounted cashflow; rather, it represents a recovery
393
rate of 51.13 percent. This demonstrates that not all cashflows are received and that some
need to be estimated. The value of the BBB-rated bond, therefore, will be:
6
6
6
107.55 = 6 + ——— -■ + ~1---- _ __ 2 + (1__ ______ 3 +
(1 + 0.041)
(1 + 0.0467)2
(1 + 0.0525)3
6
(1___ ______ _
(1 + 0.0563)4
The previous equation is simply the normal discounted cashflow formula that is used in finance:
Cashflow from bond
(1 + Zero coupon rate)"
where
n = the year.
Figure 11.4 shows how the distribution appears visually.
Figure 11.4 Distribution for a five-year BBB-rated bond in one year
Frequent V
0 '.IM p
■I; ftp >
0 !Tó
I.I.iljO -
li t hoikoỉl
Source: JPMorgan 1997, CreditMetrics™—Technical Document, www.riskmetrics.com.
You will notice that the distribution is very skewed, not normal. This presents some statistical
issues, but we will assume that the distribution is normal. Completing the calculations, we can
find the standard deviation of the distribution, as shown in Table 11.9.
The following are explanations of the terms in Table 11.9:
• year-end rating: the credit rating at the end of year one
■
probability of state: the transition probability indicated by Standard & Poor’s as per Table
11.6
■
new bond value plus coupon: the value of the bond as per the yield curve in Table 11.8
394
■
■
■
probability-weighted value; the transition probability multiplied by the value of the bond.
(The sum of this column is the transition-weighted value mean.)
difference of value from mean; the new bond value less the value-weighted mean
probability-weighted difference squared; the difference squared multiplied by the transitional
probability. (The sum of this column is the value-weighted variance and the square root is
the standard deviation.)
Table 11.9 Standard deviation calculation - calculating volatility in value due to credit quality
changes
Year end
rating
AAA
AA
A
BBB
BB
B
ccc
Default
Probability
of state (%)
0.02
0.33
5.95
86.93
5.30
1.17
0.12
0.18
New bond
value plus
coupon ($)
109.37
109.19
108.66
107.55
102.02
98.10
83.64
51.13
Mean = $107.09
Probabilityweighted
value ($)
0.02
0.36
6.47
93.49
5.41
1.15
1.10
0.09
ProbabilityDifference of
weighted difference
value from
mean ($)
squared
0.0010
2.28
2.10
0.0146
0.1474
1.57
0.1853
0.46
1.3592
(5.06)
0.9446
(8.99)
(23.45)
0.6598
(55.96)
5.6358
Mean = 8.9477
Standard deviation = $2.9900
Source: fP Morgan 1997, CreditMetrics™—Technical Document, www.riskmetrics.com.
The standard deviation is the stand-alone credit risk of the BBB-rated bond. It is the unexpected
loss of the distribution and, technically, it is the capital that a lending institution should put
aside.
Here, we are concerned about the capital at risk due to credit risk changes. This is measured as
the amount that can be lost depending on the number of standard deviations. A 1 percent value
at risk is one standard deviation (1.65), while a 5 percent value at risk is two standard deviations
(2.33). (1.65 and 2.33 are the number of standard deviations under the normal curve.) In terms
of the calculations, the capital at risk becomes as follows:
■
5 percent value at risk: 1.65 X 2.99 = $4.93
■
1 percent value at risk: 2.33 X 2.99 = $6.97
In the next chapter, we will deal with capital adequacy. It is sufficient to say here that capital
adequacy deals with unexpected losses and would require that banks put aside $8 million.
(Under capital adequacy guidelines, this loan would have a 100 percent risk weighting, requiring
8 percent to be put aside as capital.) Under a more 'scientific' method, we see that less capital
could be put aside.
395
Moving from the stand-alone risk to a portfolio risk becomes a more difficult proposition
because we now need to deal with joint probabilities. This is beyond the scope of this text, but
we can describe the problem if we imagine adding another bond to the portfolio. The following
issues then need to be considered:
■
As seen in the stand-alone example, there are eight different possible values. With a second
bond in the portfolio, there would be sixty-four different values (eight multiplied by eight
possibilities of new values).
■
We then need to calculate from the transitory probabilities the sixty-four probabilities.
Given the correlations among the credit correlations, it is not a matter of multiplying the
probabilities as if they are independent.
■
We then calculate the mean and variance of the value of the distribution. As loans are added
to the portfolio, the calculations become more complicated.
Now we have moved through the computational issues, we can recap with the following
procedure to calculate the portfolio risk of a lending portfolio for a credit-rated bond.
1. Define the portfolio as individual assets
2. For each asset, define the cashflows and calculate the present values for each potential state,
using a zero coupon yield curve
3. Using a transition matrix, calculate the probability-weighted present value and standard
deviation. These three steps could be construed as one stage because they calculate the
stand-alone risk of each loan
4. The next stage is to calculate the portfolio risk by executing the above procedure for the
joint probabilities for a loan in the portfolio to derive the standard deviation of the portfolio.
While the CreditMetrics™ approach overcomes some of the stated objections to using modern
portfolio theory (i.e. the assumptions of normality of distributions), it has its own concern: like
many other methods, it always involves the problem of valuing bank loans.
MANAGING THE PORTFOLIO
In this chapter, we have moved from analysing the single loans to analysing the overall portfolio
risk. The question now becomes: what does the lending manager do if he/she finds unacceptable
concentrations of risk? To put this into more familiar terms, what does the portfolio manager do
if he/she finds that the lending assets fail to sit on the efficient frontier?
In these circumstances, the lending portfolio manager would need to shed some of the assets
that cause some of the problems. For many years, the only technique was to sell the loans off the
statement of financial position. Known as securitisation, this technique was limited to various
selected assets. The development of the financial markets, however, has given rise to a new class
of derivative instruments - credit derivatives. We will now discuss both techniques.
396
Securitisation
Securitisation is a method of packaging the cashflow from an asset into an investment (not
collateral) security and selling it to investors. The most recognised securitisation structures are
those that package home loans, but many other structures are available. These include packaging
cashflows from:
■
utility bills such as electricity and water bills
■
royalties such as those of recording artists
•
car loans and leases
■
rentals from large, well-tenanted commercial buildings.
Financial institutions securitise for different reasons and are not restricted to credit risk
management. This is particularly the case in Australia, although lenders in countries such as
the United States use securitisation or similar techniques such as loan sales to remove risk
concentration. In Australia, securitisation is also used for:
■
capital management
■
liquidity management
■
interest rate risk management.
The usual reasons for securitisation in Australia are capital and liquidity management. Note that
securitisation, contrary to appearances, does not mean selling the lending off the statement of
financial position. The two main types of securitisation structure are:
1. pass through structures, and
2. pay through structures.
The type of structure used depends on the purpose of securitising lending assets. Before
discussing these structures, it is important to recognise the characteristics of the following types
of asset that can be securitised:
■
The assets must have high-quality cashflows; in other words, there must be a low probability
of default. This makes home loans an attractive asset because they have historically low
default rates.
■
The lending assets have to be homogenous, which means they have the same risk profiles.
Again, home loans have a similar risk profiles.
■
Credit rating agencies also impose conditions on structures when they apply ratings.
■
Individual lending assets must be seasoned (which usually means that at least one
repayment has been made) and must have insurance (which usually means, for home
loans, that the loan is mortgage insured).
■
On a portfolio basis, structures will normally be penalised if there is geographic risk.
Pass through structures
The major characteristic of pass through structures is that the lending assets are completely sold
off the statement of financial position. Those financial institutions that are using securitisation
397
for capital management purposes favour these structures, which have the following component
(Figure 11.5):
■
the owner of the assets
■
a special-purpose vehicle through which the assets are sold
■
a trustee/manager that manages the assets and their cashflows. This involves receiving the
cashflow from the lending assets—for example, the principal and interest repayments that
would be received via the lending institution—and passing them onto the investor. The
trustee/manager is also responsible for investing surplus cashflow received to maintain
the value of the asset.
■
investors who buy the security.
In terms of the asset pool, there are two types of structure:
1. static pools
2. dynamic pools.
A static pool has a fixed number of assets in the pool. To maintain the value of the pool, the pool
is either insured for the value or overcollaterised. Overcollaterisation means a greater value of
assets is put into the pool than the value for which the pool is to be sold. This is obviously at a
penalty to the lender wishing to securitise assets. Both measures are important to investors, who
would be concerned that loans would lose their value through pre-payments or default.
Figure 11.5 Process for pass through structures
Dynamic pools have assets with maturities that are shorter than the actual securitisation
structure. As cashflows come in from the lending asset, the trustee/manager re-invests the
proceeds to maintain the value of the pool. This often requires the trustee/manager to carry out
risk management or purchase guaranteed investment contracts (known as GICs in the market),
which guarantee a yield.
Pay through structures
Pay through structures are not much different from pass through structures, particularly in
terms of the cashflows. The one major difference is that the lending institution does not sell the
398
assets off the statement of financial position; rather, it packages the cashflows into the special­
purpose vehicle.
Pay through structures are more popular with institutions that are not regulated by the Australian
Prudential Regulation Authority. The reason is that if the purpose of the securitisation were
capital management, then the regulator would not consider the asset as having been removed
from the statement of financial position using this structure. The most popular assets that are
securitised through these structures are credit card receivables from retailers and car lease
receivables from nonbank lenders.
Securitisation and credit risk management
The question now becomes: how does securitisation help credit risk management? The most
obvious purpose for using securitisation for credit risk management is to sell off concentrations
of credit risk. Financial institutions, having identified a concentration of lending assets, can sell
off these assets through securitisation vehicles. At this stage, the assets that can be securitised
are limited to housing loans, credit card receivables and car lease receivables, but the overseas
experience reminds US that the variety of assets that can be securities will slowly rise over time.
Three other issues need to be kept in mind when securitising via pass through structures:
1.
2.
3.
It is important that there is no recourse, either legal or moral, to the lender if the securitised
loan defaults. The Australian Prudential Regulation Authority has guidelines regarding
recourse. Apart from the regulatory requirements, the lending institution will not have
removed the credit risk if investors are able to claim recourse.
An often-overlooked issue is that when credit risk is sold off, fair value for the credit should
be received. This may seem to be a liquidity issue, but it becomes a credit issue because the
reward-risk issue arises when assets are sold at deep discounts.
Securitisation also brings up a relationship issue for the lender and borrower. When a
lending asset is securitised, the institution may be required to inform the borrower that
its loan is being sold. This may be viewed negatively by the borrower, particularly in the
corporate lending market. Much of this problem is overcome by assigning the asset to
special-purpose vehicles, which means that the title remains with the lending institution.
There is a growing tendency in the corporate lending markets to use credit derivatives
to lay off credit risk, because this does not disturb the relationship between the lending
institution and the borrower. We will now deal with these instruments.
Credit derivatives
Credit derivatives are a set of financial instruments that allowparticipants in the financial markets
to either assume or remove credit risk to a portfolio without buying or selling the lending asset.
Credit derivatives have a major benefit over securitisation. With most securitisation structures,
the lender loses the relationship with the borrower, which is unacceptable with many loans,
399
particularly with corporate customers. Credit derivatives allow the removal of credit risk
without breaking the relationship.
The financial institution that lends money but wishes to lay off the credit risk is known as the
protection buyer, while the institution that assumes the credit risk is known as the protection
seller. In many respects, the financial institution is insuring the statement of financial position
against credit risk, much like insuring the statement of financial position against changes in
interest rates. Many credit derivatives have a corollary in the interest rate derivatives market.
There are many different credit derivatives, but they fall into three main categories: (1) credit
default swaps; (2) total return swaps; and (3) credit options. A credit default swap acts like an
insurance policy: for a periodic fee, a lender can hedge a loan with a protection seller, who would
pay an agreed amount on the instigation of a credit event. The types of credit event covered are
normally negotiated between the protection seller and buyer, and could include:
■
bankruptcy
■
credit rating change
■
capital structure changes
■
default on a loan
■
changes in credit spreads above an agreed level.
Figure 11.6 shows how the credit default swap works. The residual payment is normally defined
as the face value of the lending asset less the market value (or recoverable value) of the loan.
Figure 11.6 Credit default swap
While the credit default swap structure looks like an insurance policy, the total return swap
looks more like an interest rate swap, as shown in Figure 11.7. Under this swap, the protection
seller pays the protection buyer any losses in market value on the reference loan (or, in the case
of the market value of the asset increasing, the protection seller receives the increased value).
In return, the protection buyer pays a funding rate such as the London Inter Bank Offer Rate
(LIBOR) or BBSW.1 The asset on the statement of financial position of the financial institution
maintains its value regardless of any change in credit rating (up or down).
1
BBW is a page on the Reuters information system that provides average bank bill rates over a
range of maturities to 180 days.
400
Figure 11.7 Total return swap
Some credit options operate as normal options where the strike price is based on credit spreads
widening. They are not unlike a credit default swap, except the fee is paid in full at the beginning
of the derivative rather than over the life of the instrument. If the fee is paid upfront, then the
derivative is generally referred to as an option; otherwise, it is a swap.
The protection sellers tend to be a limited number of institutions, mainly banks (both domestic
and international). In offshore markets, many insurance companies are now protection sellers,
gaining diversification benefits by moving into new markets. Australia is expected to move
in the same direction. In the near term, however, given that banks are using only credit rated
bonds, the market is expected to be limited as financial institutions attempt to lay off the same
credit risk. This will be a problem until the market deepens. The following are among the other
major problems:
■
Good-quality pricing data are not available.
•
The pricing models have not been tested during periods of financial distress.
■
Where numerous credit derivatives have been written on a particular asset, if that asset
defaults, then the value could paradoxically be higher than the default value because the
asset (loan) would be deliverable.
■
Credit derivatives, operating in an illiquid market, cannot be easily valued.
■
With the risk now removed from the statement of financial position, the incentive for the
financial institution to monitor the reference asset is diminished.
■
At this stage, the Australian Prudential Regulation Authority penalises any hedge using a
credit derivative if it does not match the maturity of the underlying reference asset. Many
protection sellers, being risk averse, will write protections for maturities less than those of
the reference assets.
INDUSTRY INDIGHT
Banks' ability to assess iendsng risk
A New York-based risk analysis company has found that banks in Australia are less proficient
than their American counterparts in being able to assess commercial lending risks.
The analysis of loan book samples at two of the five biggest Australian banks by Zeta Financial
Services showed that up to 17 percent of their loans carried a high propensity to default in the
next five years.
401
Zeta Financial Services is the Australian arm of Zeta Services Inc., which has been working in the
area of credit risk rating for the past seventeen years. Its software is now used in 30 percent of the
top fifty US banks and Zeta is a consultant to the US Federal Reserve Board.
A director of Zeta Financial Services, Mr Graham Soper, said the sample studies conducted by
his firm had highlighted the need for banks to have an independent assessment of their loan
books. He said the sample survey results showed a major flaw in the Reserve Bank's proposed
new regulatory requirement that chief executive officers of banks sign off on the adequacy of
internal risk management systems.
bank management is going to sign off a declaration that their risk management systems are
inadequate', he said.
‘No
‘What is needed is an external' objective test of their credit risk management.
'The auditors can't do it because they don't have the software to be able to take a sample of a loan
book and give them a rating.'
Mr Soper, who spent twenty years in insolvency and corporate reconstruction work before
starting a company called Corporate ScoreCard' said much had been written about the risks to
banks from derivatives while ignoring the dangers from 'the common old garden corporate loan'.
seem to have ignored the enormous potential for banks to lose money in corporate
lending', he said... the top six banks alone wrote off SI7.45 billion between 1991 and 1994, and
hit a peak of bad loans in 1992 of $25.7 billion.
‘People
Mr Soper said the pilot studies of two of the top five Australian banks had found that only 65
percent of loans matched the Zeta credit risk ratings whereas in the United States 70 percent of
loans matched the Zeta ratings.
Of the remainder of the loans in the Australian samples, half were rated higher than the Zeta
ratings and half were lower.
•
The chairman of Zeta, Mr Brian Wright, said banks that failed to assess accurately the credit risk
of customers ran the risk of either overcharging or undercharging for risk. Mr Wright' who had
a long experience of dealing with small to medium-sized enterprises [SMEs] when he was head
of the Commonwealth Development Bank' said Zeta's work had shown that up to 15 percent of
SMEs were far stronger than the banks thought they were.
Zeta was started in 1979 by Professor Ed Altman and Mr Bob Haldeman. Professor Altman, who
is professor of finance and chairman of the MBA program at the New York University Business
School' developed the idea that default or credit risk could be measured directly from financial
statement data.
Zeta Services Inc. in New York licensed the Australian and New Zealand rights to its software
in 1993.
Source: T Boyd 1995, ‘The art of picking losers', Australian Financial Review, 6 November, p. 28.
402
The above article highlights the veracity of the credit risk management system. It makes a very
important point: differing credit risk management systems provide different answers. In this
case, the divergence is between in-house systems designed by lending institutions and those
available 'off the shelf ’. Is the comparison valid?
It depends. Systems designed in-house have the advantage of including those characteristics
peculiar to the lending institution. They can be difficult to keep current, however, in terms of
financial market developments. This iầ where off-the-shelf systems are valuable.
There are two points to the article. First, how are lending institutions kept accountable for the
way in which they manage credit risk? Second, the article implies that banks may experience bad
debts five years from 1995, and we are now starting to observe spectacular corporate collapses
that affect lending institutions.
LOAN PRICING
The discussion on credit risk analysis and portfolio management would be incomplete without
a discussion of the elements of loan pricing. We mentioned earlier in the chapter- that lending
as a whole rarely lies on the efficient frontier, inferring that the interest rate charged on loans
does not reflect its risk. This does not necessarily reflect all lending products, but portfolios as a
whole. In this section, we will highlight the elements that should be considered in loan pricing:
1.
2.
costs of the statement of financial position
non-credit risk costs.
Costs of the statement of financial position
Given that the loan is funded from the statement of financial position, a number of costs that are
generated from the statement of financial position need to be considered:
1. capital costs
2. liquidity costs
3. the cost of funds.
Capital costs
Lending institutions charge capital to loans using differing methods. As a base method, they
would set aside capital based on the capital adequacy guidelines. This would mean that some
capital would need to be used when funding a loan. Investors would require a return on the
equity they provide. This cost to the lending institution is determined by the board of directors.
Liquidity costs
The element of liquidity is often forgotten in the lending equation. While the Australian
Prudential Regulation Authority has relaxed the formal guidelines for liquidity management, a
prudent lending institution would hold a portion of its assets in liquid securities or cash. This
403
approach implies that liquidity should be held against every lending asset, depending on the
policy.
Cost of funds
When we take the above elements into account, we can calculate the cost of funds. The required
capital and deposits fund the lending and liquid assets. The following returns would be fixed or
set by the market:
■
The board sets the return on equity.
■
The market sets the return on liquidity.
■
The market also sets the cost of deposits.
■
The only variable is the return on the loan, which is the balancing figure.
This should become clearer in an example later in the section.
Non-credit risk costs
Lending also involves risks that are not directly related to the statement of financial position but
affect the cost of the loan and thus its price. These risks include:
1.
2.
3.
interest rate risk
pre-payment risk
origination costs.
The first two risk types occur generally when the loan is a fixed interest loan, while the third
occurs with all loans.
Interest rate risk
Many financial institutions provide the majority of their loans on a variable basis and fund these
loans with at-call deposits. As interest rates rise, many borrowers tend to want to switch to fixed
interest rate loans. In a rising interest rate environment, the costs of deposits also rise. If the
lending institution does not take this into account, then a loss could arise.
There are a number of solutions to this scenario. The institution can fund the loan from term
deposits (or similar instruments) of the same maturity, obviating the interest rate risk. The
longer the maturity, however, the more difficult it is to raise term deposits. If term deposits can
be raised, then that would be the cost of these funds.
If funds cannot be raised, then the alternative would be to execute a derivative that would fix the
rate. Likewise, the rate at which the derivative is fixed would be the cost of funds.
Pre-payment risk
The opposite situation to interest rate risk is pre-payment risk. Again, this is a risk only for fixed
rate loans, but in this instance when interest rates fall. When interest rates fall, many borrowers of
fixed rate loans seek to refinance their loans at lower rates. If successful, the financial institution
404
incurs a loss because it is unable to reinvest the funds at the same rate. Further, if the cost of
funds has been fixed by term deposits or a derivative, then this arrangement might have resulted
in a relatively high cost of funds.
There are two alternative solutions to this phenomenon. The lending institution can estimate
the average cost of pre-payment risk and add this cost to the loan (although this may make the
loan uncompetitive). More often, the lending institution specifies a penalty that should cover the
majority (if not all) of the costs. This penalty is easy to calculate and easy to understand.
The economic solution, however, is to calculate the present value of the cashflow on pre­
payment, with the lending institution receiving a penalty or paying a benefit depending on the
interest rate of the loan and current interest rates.
Originating and operating costs
The cost of marketing and then monitoring the loan needs to be incorporated into the loan. The
more complex and risky the loan, the more monitoring it requires. This effort should result in a
higher interest rate being applied.
Credit costs
We have deliberately not addressed credit costs first. Hopefully, you will observe that the margin
over base rates is more than simply credit risk. Credit risk, nevertheless, makes up an important
element in loan pricing. The two elements in default risk pricing are:
1. expected losses
2. unexpected losses.
Expected losses
Lending institutions always expect some loans to default. We cannot predict the future, and a
loan that seems good today can default later as a result of unforeseen events. Lending institutions,
given their experiences, expect some loans to default. They can price their expected losses into
the loan price using the following formula:
Expected losses = Default probability X (1 - Recovery rate)
Both the default probability and recovery rate for the loan type would be determined from the
lending institution's experience (or, in some cases, credit rating agencies).
Unexpected losses
It is more difficult to deal with unexpected losses. These losses are generally said to reflect the
volatility of expected losses and thus change from period to period. The tail of the CreditMetrics™
distribution shown in Figure 11.3 could indicate unexpected losses.
Unexpected losses cannot be priced. The correct way to deal with them is to set capital aside
(much like capital adequacy) and price unexpected capital as a return-on-equity issue.
405
Loan pricing - an example
Now we have identified all the elements of a loan, we can consider the following example. We
will also examine what occurs when the wrong relationships are assumed. The price of a $150
000 five-year housing loan needs to be considered. The lending institution determines that there
needs to be 5 percent liquidity against lending assets, which currently earn 4.9 percent. At-call
deposits are priced at 3.5 percent and five-year swap rates are at 5 percent. The annual cost of
operating and managing the loan is $1000.
The lending institution’s default probability for housing loans is 2 percent, while its recovery
rate is 95 percent. It considers its unexpected losses in terms of losses that are outside the capital
put aside under capital adequacy guidelines. Home loans have a small probability of loss and
the lending institution puts an extra 1 percent aside. To deal with pre-payment risk, the bank
charges an extra 30 basis points.
What would the price of the loan be if investors require an after-tax return on equity of 20
percent and have a tax rate of 30 percent?
It is important to view the pricing of the loan in stages. Our first step is to calculate the return on
equity. Under capital adequacy guidelines, the amount of capital required is:
$150 000 X 8%
X
50% = $6000
The above 50 percent is as per the capital adequacy guidelines. The after-tax return on equity
becomes:
$6000 x 20% = $1200
We can also work out the amount of liquid assets, using simple algebra required under the 5
percent policy, as shown on the next page.
Liquid assets = Assets X 5%
.
where
Assets = (Loans + Liquid assets) = ($150 000 + Liquid assets)
Liquid assets = ($150 000 + Liquid assets) X 5% = $7500 + 0.05 Liquid assets
Liquid assets - 0.05 Liquid assets = $7500
0.95 Liquid assets = $7500
$7500
Liquid assets ■ • ■ ■— = $7895
4
0.91
We can now construct a first-stage simple statement of financial position as follows.
406
Table 11.10 Statement of financial position
Assets
$150 000
Loan
Liquid assets
Total
$7 895
$157895
Liabilities
Deposits
Equity
Total
$151 895
$6 000
$157 895
You will note that the deposits are the balancing figure. Given that we know all the costs above
(except the loan price), we can make the first calculation, using at-call deposits.
Tabid l.ll Fừst calculations
Loan
Liquid assets
Less interest on deposits
Profit before tax
Financial performance ($)
6 643 .74
386.86
5 316.32
1 714.28
Less tax at 30 percent
Profit after tax
1
514.28
200.00
Yield (%)
4.43
4.9
3.5
Balance ($)
150 000
7 895
151 895
20.0
6 000
The balancing number is loan financial performance, from which we can infer the loan rate. It
is not immediately obvious that to find the answer we need to work backwards from the profit
after tax financial performance to obtain the balancing figure of $6 643.74, which gives a yield
of 4.43 percent.
The other issue to be considered here is that the loan is fixed for five years. If interest rates rise,
then investors will not obtain their 20 percent return on equity. In theory, lending institutions
should fund these Ioans at the five-year rate and transfer price the deposit at this rate, as we do
below in Table 11.12. In practice, this is difficult to do. Table 11.13 shows the effect.
Table 11.12 First calculations
Loan
Liquid assets
Less interest on deposits
Profit before tax
Financial performance ($)
8 92217
386.86
7 594.75
1 714.28
Less tax at 30 percent
Profit after tax
1
514.28
200.00
Yield (%)
5.95
4.9
5.0
Balance ($)
150 000
7 895
151 895
20.0
6 000
407
Table 11.13 The effect
Loan
Liquid assets
Charge for pre-payment risk
Less interest on deposits
Less monitoring cost
Less expected losses
Profit before tax
Financial performance ($)
10 050.75
386.86
450.00
7 594.75
1 000.00
150.00
2 142.86
Less tax at 30 percent
Profit after tax
1
Yield (%)
6.70
4.90
0.30
5.00
Balance ($)
150 000
7 895
150 000
151 895
0.001
150 000
20.00
7 500
642.86
500.00
We can now deal with the remaining issues and complete a new table (as above).
■
The cost of monitoring is $ 1000.
■
Expected losses are as follows:
Expected loss = Default probability X (1 - Recovery rate)
■
=2% X (1 - 95%) = 0.001
■
Extra capital of 1 percent changes the $6000 capital to $7500.
■
There is a 30 basis point charge for pre-payment risk.
Practical loan pricing
If this theoretical approach were adopted, then the price of loans would undoubtedly be higher
than what is observed in the market. Why? There are two major reasons:
1. Competition often pushes down the price of loans.
2. Lending institutions price the borrower on the whole connection. The fees on one facility
(such as transaction fees on cheque accounts) are often expected to offset a lower interest
rate. Lending institutions often lose money using this’scenario because some of the expected
income does not materialise.
Lending institutions need to exercise caution so their good credits do not subsidise the lesser
credits.
A DAY IN THE LIFE OF...
A credit risk analyst
8.15 a.m.
I work for the Commonwealth Bank Group as a credit risk analyst. I read the Australian Financial
Review and Sydney Morning Herald as a daily routine.
408
9.00 a.m.
A submission for the April meeting of the bank's risk committee is due today. The risk committee
is chaired by Mr David Murray and convened every second month. Issues discussed at the
meeting arise from the challenges of integrated risks (credit, operational, market and liquidity)
that the bank is facing in the short to medium term. My part of the submission concerns the
concentration level of the bank's lending to the Australian commercial portfolio, as well as the
quantitative measure of the risk-return profile of each industry grouping in the Australian
commercial portfolio. (The return measure is the Sharpe ratio and the risk measure is the portfolio
unexpected loss. At individual asset level, risk is measured by the contribution of risk to the
portfolio unexpected loss, accounting for the diversification of the portfolio.) The submission is
a summary result of a routinely run model, which takes two to three weeks to process. The model
produces vast amounts of information but space reserved in the risk committee submission is
normally 300 words and one attachment.
10.00 a.m.
attend a portfolio management team meeting on the subject of portfolio stress testing. I have
to break off work on the risk committee submission because the meeting is pre-arranged. The
executive credit committee and risk committee requested the portfolio management team
to perform portfolio stress testing at the middle of 2001, in the wave of apparent credit down
cycle and big corporate collapses, which caused the bank to increase its loan loss provision
substantially. This task was given more priority as the result of the 11 September 2001 events
in the United States. The format of the meeting is brainstorming. One team member is not
entirely happy with the current method of using a credit migration matrix derived from the
Early Warning System (a forecast model that I run quarterly, to predict the portfolio expected loss
in one year), because gradual migration does not constitute a stress event. Echoing the concern
from the capital management team, one other team member questions the validity of stress test in
reference to its usefulness in capital allocation. He argues that the stress test should only help the
senior management to understand the risks faced by the bank. The chief manager of the portfolio
management team decides to continue the current method subject to further discussion.
I
The official meeting finishes at 11.00 a.m. I spend the next half hour talking about the weekend
footy results, the euphoria of the Corporate Games and gossip such asjustin Timberlake dumping
Britney Spears.
1 finish work on the risk committee submission by 12.30 p.m. I have lunch at my desk, while
surfing on the Internet, then walk to the Domain after lunch.
2 p.m.
deal with an e-mail from the Credit Management Unit at the Customer Services Division
regarding the impact of Colonial integration on its agriculture portfolio. It is putting together
a working paper to the board, outlining the current status of agribusiness in Australia and the
bank's strategy in dealing with the new challenges in this area. It is concerned with the current
economic environment and the potential negative impact on its agriculture portfolio.
I
409
3 p.m.
attend a scheduled meeting with two consultants from Risk Solution of Standard & Poor’s
regarding the proposal to build a national database on loss given default. The primary purpose of
this database is to make the Australian Prudential Regulation Authority comfortable and may be
used to satisfy the Basel II requirement for advanced status. Today’s meeting is the follow-up of a
Melbourne meeting of the major banks. A further meeting is scheduled to customise the database
structure and definitions. The meeting finishes in just under one hour.
I
4 p.m.
I receive a telephone call from the financial control of institution banking to voice concern with
the assignment of R-square (a correlation parameter) to the business-in-government sector in the
running of KMV Portfolio Manager™. A meeting is arranged for 10.30 a.m. on Thursday.
I have a chat with my work partner about a wide range of subjects, while flipping through some
industry/economic magazines.
4.45 p.m.
Just as I am getting ready to go home, I receive a request to assess a syndicated credit from the
office of the bank’s chief credit officer. The portfolio management team does not assess credit
generically (that is, on an individual client’s credit quality); rather, we put the applicant into a
subportfolio and examine its impact in terms of change to the risk-return profile of the portfolio.
It takes 45 minutes to finish trimming the relevant data, which are then input into our portfolio
model. I run the subportfolio overnight. Analysis of the preliminary result is the work of another
day.
Source: Mr Harvey Yu, Portfolio Analyst, Portfolio Management, Group Risk Management,
Commonwealth Bank of Australia, 2001.
It can be easy to think that the task of a credit portfolio manager is boring and mechanical.
Most of the tasks carried out by someone in this position are involved and time consuming. A
particular problem is that requests can appear 'out of left field'. Given that an incorrect credit
decision may have an adverse impact on the lender's profits, the credit portfolio manager’s task
requires insight and accuracy of judgement.
SUMMARY
1.
What is the main benefit of credit risk management?
The major benefit of credit risk management is that it removes the subjectivity from
lending decisions. Ultimately, loans can be assessed on a scientific rather than human
basis.
410
2.
What is Altman’s z score? How is it used?
Altman used multidiscriminant analysis to distinguish between two populations: good
borrowers and bad borrowers. A score is constructed using accounting ratios, then this
score is measured against a benchmark.
3.
How can stock prices be used to explain credit risk?
The problem with Altman’s z score is that it is considered by many to be backward
looking. KMV Corporation, via its expected default frequency method, used share
prices to predict default. This assumes that share prices imply all future possibilities
regarding the share.
4.
Explain how actuarial approach can be used by examining CreditRisk+
This approach does not take into account the reason for default but uses an actuarial
approach to credit risk
5.
Explain how a macroprudential approach can be used by looking at Creditportfolio view
This approach uses a macroeconomic approach which connects credit risk to the
business cycle.
6.
How can risk-adjusted return on capital be used for portfolio purposes?
Given that loans require capital, it has been suggested that a portfolio could be
constructed on the basis of the return on capital adjusted for risk. The method also
recognises that capital is a finite resource and marginal capital can be used to fund a
loan.
7.
How can the Sharpe Index be used for lending purposes?
The Sharpe Index measures the risk-adjusted return, like the return on equity method,
using traditional portfolio management. Lenders can maximise their returns using the
method, with suitable constraints.
8.
Why is CreditMetrics™ useful for calculating the risk of a loan portfolio?
Again, the Sharpe Index can be considered to have problems because some of the
assumptions do not equate well to lending. CreditMetrics™ recognises this problem,
as well as the fact that credit risk is not static.
9.
What are the elements of loan pricing?
The elements of loan pricing include the consideration of costs of the statement of
financial position, as well as credit risk costs and costs that are not of the statement of
financial position.
411
DISCUSSION QUESTIONS
1.
2.
3.
Outline the problems of traditional lending methods and possible solutions. Are there any
problems with the solutions?
Compare and contrast the approach of the score model and the KMV expected default
frequency model.
Use the following extracts from the Harvey Norman 2000 annual report to calculate the
Altman score.
z
z
Statement of financial position as at 30 June 2000
Consolidated
2000
($’000)
Parent entity
1999
($’000)
2000
($’000)
1999
($’000)
■
! Current assets
37 385
3 147
-
476 077
358 477
151 669
61 001
24 599
-
13 552
6
-
588 015
9616
395 839
151 675
174 576
Receivables
9 067
8514
-
ị
Investments
10 396
37 881
55 596
55 592
ị
Property, plant and equipment
547 129
388 560
-
>
Intangibles
590
—
-
J
Other
Total noncurrent assets
3 747
1 131
950
569 749
438 702
56 727
157 764
834 541
208 402
56 542
231 118
312 124
216 373
77
64
ị
Cash
ị
I
Receivables
Inventories
•
Other
•
J
Total current assets
Noncurrent assets
1
J
ị
ị Total assets
Current liabilities
ị Accounts payable
. .. .
2 567
1
•
174 576
;
1
Borrowings
33 591
12 401
-
-
Ị
Provisions
58 115
48 547
21 517
26 085
403 830
277 321
21 594
26 149
1 Total current liabilities
412
Parent entity
Consolidated
■
2000
($’000)
2000
($’000)
1999
($’000)
1999
($’000)
;
ị
Noncurrent liabilities
Borrowings
j Provisions
1 Total noncurrent liabilities
152 151
-
388
238
-
203 608
152 389
-
21 594
186 808
26 149 i
204 969 ị
ff
142 869
;
Total liabilities
607 438
429 710
1
Net assets
550 326
404 831
i Shareholders' equity
Share capital
Reserves
Retained profits
; Total shareholders' equity
_ Ị
203 220
i
1
- !
— ■Íi
I
187 792
142 869
142 869
83 551
58 614
-
278 983
203 348
43 939
550 326
404 831
186 808
i
i
Ị
— it
62 100 J
204 969 ỉ
Statement of financial performance for the year ended 30 June 2000
J
1
““
Ị
i
; Operating profit before abnormal item
■ Abnormal item
;
Operating profit before income tax
! Income tax attributable to operating profit
;
Operating profit after income tax
Retained profits at the beginning of the financial year
! Total available for appropriation
■
ị
Dividends provided for or paid
;
Retained profits at the end of the financial year
! Basic earnings per share (cents per share)
ị
Parent
entity
Consolidated
ị
2000
($’000)
1999
($’000)
2000
($’000);
173 897
136 843
-
7 374
28 881 ị
— Ịi
173 897
129 469
62 626
49 344
111 271
80 125
203 348
153 895
17 475 ;
62 100 í
314619
234 020
79 575 ị
35 636
30 672
35 636 •
278 983
203 348
43 939'
10.83
7.89
28 881
ỉ
11 406
•
-■
f
Source: Harvey Noman 2000, Haney Norman Holdings Limited and its Controlled Entities—Annual Report
2000.
413
Would you lend to Harvey Norman? What is the difficulty in using the Altman z score?
1. Under what circumstances does KMV's expected default frequency model not work
correctly?
2. Explain the limitations of the concept of the risk-adjusted return on capital.
3. What financial basis does Altman use to construct his portfolio management model? Why
does he use constraints in maximising the return on the portfolio?
4. In the example given for CreditMetrics, we calculated the stand-alone risk for a BBBrated bond. Using the same data, calculate the stand-alone risk of a five-year AA-rated
bond with a coupon of 5 percent. Is there anything about your answer that you find
unusual?
5. Explain the circumstances in which you would use securitisation and the circumstances in
which you would use credit derivatives.
6. Is securitisation a credit risk management tool?
7. If a protection seller under credit derivatives is assuming the risk of a loan, why does the
protection seller not just provide the loan?
REFERENCES AND FURTHER READING
Altman E 1968, 'Financial ratios and discriminant analysis and prediction of corporate bankruptcy’, Jour­
nal of Finance, 23, pp. 189-209.
Altman, El 2000, 'Predicting financial distress of companies: revisiting the Z-score and Zeta models', New
York University Working Paper, New York.
Beaver, w 1967' 'Financial ratios as predictors of failure’, Journal of Accounting Research.
Caoeutte, J' Altman, E & Narayanan, p 1998' Managing Credit Risk, John Wiley & Sons, Toronto.
Croughy, M Galai' D & Mark, R 2000' 'A comparative analysis of current risk models’, Journal of Banking
and Finance, 24' pp. 59-117
Crosbie, p. & Bohn, JR 2001, 'Modelling default risk’, www.moodyskmv.com , accessed May 2001.
Morgan JP 1997, CreditMetric™—Technical Document, www.riskmetrics.com.
Saunders, A 1999, Credit Risk Measurement, John Wiley & Sons, Toronto.
White, G' Sondhi' A & Fried' D 1998' The Analysis and Use of Financial Statements, John Wiley & Sons, To­
ronto.
CREDIT RISK FROM
THE REGULATOR'S PERSPECTIVE
LEARNING OBJECTIVES
By the end of this chapter you should be able to:
1. understand the issues of credit risk from the perspective of the regulators
2. relate capital adequacy to credit risk considerations
3. express the issues of large exposures
4. identify securitisation issues for regulators
5. identify credit derivative issues for regulators
6. describe the credit rating process
7. discuss the new capital adequacy guidelines.
KEY TERMS
Basel II
clean sale
moral hazard
tier 1 capital
Basel III
credit rating
prudential regulation
tier 2 capital
415
capital adequacy
large exposures
recourse
416
INTRODUCTION
Until the 1980s, the then regulator (the Reserve Bank of Australia) had a large role to play­
in credit risk issues. The regulator would often dictate maximum lending rates and direct
financial institutions to sectors of the economy that the government considered needed lending
support. While it is difficult to assess the effects of these actions, the following conclusions can
be drawn:
*
The sectors that were not specified for lending assistance would have been subject to credit
rationing.
■
Lending institutions might have been subject to concentration risk.
•
Given the directives of the regulator, banks might have approved marginal lending
applications in the directed sectors.
From the 1980s, however, successive governments dismantled this regime and moved to
prudential regulation. The strictures on interest rate ceilings and lending strictures were
removed and replaced with the less prescriptive requirements of capital adequacy, large
exposure, concentration risk, bad debts and credit issues for securitisation as well as credit
derivatives. Regulation was simply established to reduce the possibility of insolvency of financial
institutions, with the onus being on lending institutions to comply.
The global financial crisis has accelerated prudential regulation reform. Banks now need to
consider compliance with Basel III requirements as well as meet the requirements of credit
licensing under the National Consumer Credit Protection Act. In terms of Basel III, the main focus
is on liquidity risk and therefore not in the purview of this text. However, the guidelines do
address the treatment of bad debts, and that is commented on later in this chapter.
Since 1998, the regulator for credit risk issues for financial institutions in Australia has been the
Australian Prudential Regulation Authority, which supervises institutions known as approved
deposit-taking institutions (ADIs). ADIs include banks, building societies and credit unions.
Most issues canvassed in this section are based on standards or guidelines that are maintained
by the authority.
The Australian Prudential Regulation Authority does not directly address credit risk. Given
that capital adequacy has become the catch-all for credit risk, however, this discussion will
focus on the way in which the authority addresses credit matters via capital adequacy. Capital
requirement is an important tool used by regulators to assess credit risk, so it is important that
you have full understanding of the present capital adequacy regulation.
Australian Securities Investments Commission (ASIC)
The failure of some banks to manage their retail lending activities properly during the global
financial crisis has hastened the Federal Government to adopt the National Consumer Credit
Protection Act to protect retail borrowers. The main points of this legislation address:
417
1.
2.
3.
4.
licensing by ASIC,
the rights of the borrower,
the obligations of the lender, and
the nature of the contracts.
These laws are designed to protect the consumer from undertaking loans that they essentially
or potentially cannot pay back. Banks are prohibited from creating onerous conditions for their
borrowers.
CAPITAL ADEQUACY
Note: this chapter has been written during the transition of Basel 2 to Basel 3. While there are dates that
this transition will be completed, given events in other jurisdictions such as Europe, the final transition is
unclear.
Recent history has shown that financial institutions normally fail as a result of poor credit
decisions. The responsibility for credit decision-making rests with the board of directors of
a financial institution. Given that shareholders elect the board, this means that shareholders
explicitly assume the credit risk decisions of the institution.
Credit risk would not be a concern if the bank's board and shareholders invested only their own
funds, but the board invests the funds of other people. These 'other people' are depositors, who
keep funds in banks but have no control over the way in which these funds are invested. The
control is ultimately with the board and senior management, and decisions could be taken that
are not in the interest of the depositors. This is known as moral hazard.
Depositors need to be protected against poor decisions by management— this realisation led to
the birth of capital adequacy regulations. Under the base capital adequacy, financial institutions
are required to put aside capital for each credit risk exposure, whether it is on or off the statement
of financial position.
Under capital adequacy, financial institutions are required to put aside a minimum of 10.5%
plus a countercyclical buffer of up to 0.25% of capital for risk-weighted assets. (If the Australian
Prudential Regulation Authority believes that a financial institution has a high risk profile,
then it will impose a higher benchmark.) This benchmark may be increased when Basel III is
finally adopted. The term 'risk-weighted assets’ needs explanation. Under the capital adequacy
regulation, certain types of loan are considered to be safer or less risky than others; for example,
capital adequacy considers home loans to be less risky than business loans. (Note that this is a
generalisation and the reverse could easily be true in specific situations.) The risk weight or the
capital that needs to be put aside for a housing loan will therefore be less than that for a business
loan for a similar amount. The Basel Committee of the Bank for International Settlements
decides the risk weight for each type of asset. These recommendations have been mostly adopted
in Australia and elsewhere in the world.
418
Under Basel II, prudential regulation is split into three pillars. Pillar 1 deals with the capital
that needs to be put aside for credit, market and operational risk. Under this pillar, there are a
number of methodologies to calculate credit risk. For the purposes of this chapter we look at the
standardised version and touch on other methodologies in the chapter on quantitative finance
(Chapter 16).
Basel III is a direct response to the Global Finance Crisis of 2007 and 2008. While the major
issue of this crisis was not capital rather than liquidity, capital has been strengthened.
Pillar 2 is essentially a catch-all of all other risks and is managed under the process known as
the Internal Capital Adequacy Assessment Process (ICAAP). ICAAP is a document that outlines
all the risks outside Pillar 1, and how they are identified, measured and assessed for capital
requirements. It is a mistake to think that it has no credit implications whatsoever. The most
obvious credit risk that should be assessed under Pillar 2 is concentration risk. Concentration
risk is the risk created by lending too much to either a counterparty or sector. Concentration
risk is difficult to measure, and Basel II has some controversial assumptions as well. These issues
are dealt with later under quantitative finance.
Pillar 3 is known as market discipline and refers to transparency in reporting. This is an all
encompassing requirement, but its importance can be seen in that banks now provide reports
on capital adequacy and impaired loans.
The formula for capital adequacy is:
,
Total capital (Tier 1+Tier 2)
Risk-based capital ratio = ------ Z7-"——
——————-------r
Risk-adjusted assets
As of 2016, this ratio is 10.5%
Capital is much more than ordinary equity and includes instruments that perform like equity.
Tier 1 capital exhibits permanence like capital, while tier 2 capital has the ability to absorb credit
losses but is not permanent. Tier 2 capital cannot make up more than 50 percent of overall
allowable capital. Table 12.1 indicates the various capital classes.
Common Equity Tier 1 Capital consists of the sum ofl:
(a) paid-up ordinary shares issued by an ADI that meet the criteria in Attachment B;
(b) mutual equity interests issued by a mutually owned ADI that meet the criteria in
Attachment K;
(c) retained earnings;
(d) undistributed current year earnings (refer to paragraphs 20 to 24);
(e) accumulated other comprehensive income and other disclosed reserves (refer to
paragraphs 25 and 26);
1
For fuller explanations, refer to the guidelines.
419
(f) minority interests (calculated in accordance with Attachment C) arising from the
issue of ordinary shares to third parties by a fully consolidated subsidiary[3] included
in the Level 2 group where:
(i) the shares giving rise to the minority interest would, if issued by the ADI, meet the
criteria in Attachment B; and
1.
(ii) the subsidiary issuing the shares is itself an ADI or an overseas deposit-taking
institution that is subject to equivalent minimum prudential requirements and
level of supervision as an ADI
To qualify as Tier 2 Capital, an instrument must satisfy the following minimum criteria:
(a) the instrument must be paid-up and the amount must be irrevocably received by the
issuer;
(b) the instrument represents, prior to any conversion to Common Equity Tier 1
Capital the most subordinated claim in liquidation of the issuer after Common
Equity Tier 1 Capital instruments and Additional Tier 1 Capital instruments^?];
(c) the paid-up amount of the instrument, or any future payments related to the
instrument, is neither secured nor covered by a guarantee of the issuer or related
entity[50l, or other arrangement that legally or economically enhances the seniority
of the claimt511. The instrument may not be secured or otherwise subject to netting or
offset of claims on behalf of the holder or issuer of the instrument;
(d) the principal amount of the instrument:
(i) has a minimum maturity of at least five years; and
(ii) is only recognised in Tier 2 Capital (and so in Total Capital) in the five years
prior to maturity on a straight-line amortised basis (refer to paragraph 2 of this
Attachment);
(e) the instrument contains no step-ups or other incentives to redeem. The issuer and any
other member of a group to which the issuer belongs must not create an expectation
at issuance that the instrument will be bought back, redeemed or cancelled before its
contractual maturity. The contractual terms of the instrument must not provide any
feature that might give rise to such an expectation^;
(f) the instrument may only be callable at the initiative of the issuer and only after a
minimum of five years from the date on which the issuer irrevocably receives the
proceeds of payment for the instrument. The issuer:
(i) must receive prior written approval from APRA to exercise a call option. For
instruments issued by subsidiaries not regulated by APRA included in a Level 2
group, prior written approval from APRA must also be obtained;
420
(ii) must not do anything that creates an expectation that a call will be exercised; and
(iii) must not exercise a call unless:
(A) the issuer, prior to or concurrent with the exercise of the call, replaces the
instrument with a capital instrument of the same or better quality, and the
replacement of the instrument is done at conditions that are sustainable for
the income capacity of the issuer; or
(B) the ADI meets the requirements relating to reductions in capital in APS 110.
An instrument may provide for multiple call dates after five years. However, the
specification of multiple call dates must not act to create an expectation that the
instrument will be redeemed upon any call date;
(g) issuers must not assume, or create market expectations, that supervisory approval
will be forthcoming for the issuer to redeem, call or purchase an instrument;
(h) the instrument must confer no rights on holders to accelerate the repayment of future
scheduled payments (coupon or principal) except in bankruptcy (including wind-up)
and liquidation. Wind-up of the ADI must be irrevocable (that is, either by way of a
court order or an effective resolution by shareholders or members). The making of
an application to wind-up or the appointment of a receiver, administrator, or official
with similar powers, including the exercise of APRA’s powers under section 13A(1)
of the Banking Act, must not be sufficient to accelerate repayment of the instrument;
(i) the instrument must not provide for payment to investors other than in the form of
a cash payment;
(j) the instrument cannot have a credit sensitive distribution/payment feature (i.e. a
distribution/payment that is based in whole or part on the credit standing of the issuer
or the group or any other member of the group to which it belongs). However, an
instrument may utilise a broad index as a reference rate for distribution or payments
calculation purposes. Where an issuer is a reference entity in the determination of the
reference rate, the reference rate must not exhibit any significant correlation with the
issuer's credit standing. APRA will not allow inclusion of an instrument as part of Tier
2 Capital where it considers that the reference rate is sensitive to the credit standing
of the issuer;
(k) the instrument is directly issued by the issuer, and, except where otherwise permitted
in this Prudential Standard, the issuer, any other member of a group to which the
issuer belongs, or any related entitỵ[531, cannot have purchased or directly or
indirectly[5411 funded the purchase of the instrument;
(l) the instrument has no features that hinder recapitalisation of the issuer, or any other
members of the group to which the issuer belongs. This includes features that require
421
the issuer to compensate investors if a new instrument is issued at a lower price during
a specified timeframe;
(m) where the terms of the instrument provide the ability (even in contingent
circumstances) to substitute the issuer (i.e. to replace the ADI with another party),
the relevant documentation must set out the mechanism to ensure that there will
be a simultaneous capital injection into the ADI to replace the transferred capital
instrument. Any replacement capital injection must occur at least simultaneously
with the substitution and must be unconditional. The capital injection must be of
equal or better quality capital and at least the same amount as the original issue,
unless otherwise approved by APRA in writing;
(n) the instrument does not contain any terms, covenants or restrictions that could
inhibit the issuer■' s ability to be managed in a sound and prudent manner, particularly
in times of financial difficulty, or restrict APRA’s ability to resolve any problems
encountered by the issuer;
(o) the rate of dividend or interest on the instrument, or the formulae for calculating
dividend or interest payments, must be predetermined and set out in the issue
documentation;
(p) where an issuer defaults under the terms of the instrument, remedies available to the
holders must be limited to actions for specific performance, recovery of amounts
currently outstanding or the winding-up of the issuer. The amounts that may be
claimed in the event that the issuer defaults may include any accrued unpaid dividends
and interest, including payment of market interest on these unpaid amounts. All such
unpaid dividends and interest must be subordinated to the claims of depositors and
other creditors of the issuer;
(q) the instrument must not provide for payment of a higher dividend or interest rate
if dividend or interest payments are not made on time, or a reduced dividend or
interest rate if such payments are made on time;
(r) where an issue of an instrument involves the use of an SPV, the issue of the instrument
is subject to Attachment I;
(s) the instrument includes provisions addressing loss absorption at the point of non­
viability in accordance with Attachment J;
(t) the instrument is clearly and separately disclosed in the issuer’s financial statements
and, at Level 2, in any consolidated financial statements; and
(u) issue documentation clearly indicates:
(i) the subordinated nature of the instrument, and that neither the issuer nor the
holder of the instrument is allowed to exercise any contractual rights of set-off;
422
(ii) the application of requirements relating to loss absorption at the point of non­
viability under Attachment J; and
(iii) the instrument does not represent a deposit liability of an issuing ADI.
The amount of the instrument eligible for inclusion in Tier 2 Capital is to be amortised on
a straight-line basis at a rate of 20 per cent per annum over the last four years to maturity as
follows:
2.
Years to Maturity
More than 4
Less than and including 4 but more than 3
Less than and including 3 but more than 2
Less than and including 2 but more than 1
Less than and including 1
Amount Eligible for Inclusion in
Tier 2 Capital
100 per cent
80 per cent
60 per cent
40 per cent
20 per cent
The ratio of capital for each Tier, in summary is:
Common equity is 4.5 96
Tier 1 Capital is 6.0%
Total capital ratio is 8% plus the conservation buffer of 2.596 of Tier 1 capital.
Table 12.1 Various capital classes
This is a very generalised table and more specific rules can be gained from the guidelines.
In particular, there are various conditions now surrounding perpetual debt and other non­
permanent forms of equity.
The amount of capital that is allocated for each loan depends on the type of loan, its risk categories
and the credit rating, as perceived by the regulators. These risk categories (a selective list, based
generally on loans) are as follows:
Table 12.2 Risk categories
Claim
Credit rating
grade
Risk rate
Class I - Cash items
1.
2.
Notes and coins.
All Australian dollar balances and other Australian dollar claims on
the Reserve Bank of Australia
0
0
423
3.
4.
5.
6.
Gold bullion held in the ADI's own vaults or on an allocated basis
by another party to the extent that it is backed by gold bullion
liabilities.
(Gold bullion held on an unallocated basis by another party, though
backed by gold liabilities, is weighted as a claim on the counterparty
unless a lower risk-weight is approved in writing by APRA.)
Cash items in the process of collection (e.g. cheques, drafts and
other items drawn on other ADIs or overseas banks that are payable
immediately upon presentation and that are in the process of
collection).
Class II - Claims on Australian and foreign governments and central
banks
All Australian dollar claims on the Australian Government.
Claims on overseas central governments and state or regional
governments, State or Territory Governments in Australia
(including State or Territory central borrowing authorities),
central banks (including the Reserve Bank of Australia) and foreign
currency claims on the Australian Government....
0
20
1
2
3
4.5
6
Unrated
13.
Class VI - Past due claims
The unsecured portion of any claim ... that is past due for more than
90 days and/or impaừed:
a)
14.
15.
16.
0
0
20
50
100
150
100
where specific provisions are less than 20 percent of the
outstanding amount of the past due claim or impaired asset;
or
b) where specific provisions are no less than 20 percent of the
outstanding amount of the past due claim or impaired asset.
Refer to the risk-weighttng... for loans and claims secured against
eligible residential mortgages that are past due for more than 90
days and/or impaired.
Class VII - Other assets and claims
Claims (other than equity) on Australian and international corporate
counterparties (including insurance and securities companies) and
commercial public sector entities.
Alternatively, if an ADI has obtained approval in writing from
APRA, it may risk-weight all claims (other than equity) held on
the banking book on Australian and international corporate
counterparties (including insurance and securities companies) and
commercial public sector entities at 100 percent. If an ADI has
obtained approval in writing to use a 100 percent risk-weight for
these claims, it must do so in a consistent manner and not using any
credit ratings for any of these claims.
All claims (other than equity) on private sector counterparties (other
than ADIs, overseas banks and corporate counterparties).
150
100
1
2
3.4
5.6
Unrated
20
50
100
150
100
100
424
Table 12.3 Risk-rates for residential mortgages
LVR (%)
Standard eligible mortgages
Risk-weight
Risk-weight
(no mortgage
(with at least 40% of
insurance)
the mortgage insured
%
with an acceptable
LMI)
_ . —...
_
Non-standard eligible mortgages
Risk-weight
Risk-weight
(with at least 40% of
(no mortgage
insurance)
the mortgage insured
with an acceptable
%
LMI)
%
0-60
60.01 - 80
80.01 - 90
90.01 - 100
> 100.01
35
35
50
75
100
%
35
35
35
50
75
35
50
75
75
100
50
75
100
100
100
Where the credit ratings are given as:
Table 12.4 Credit ratings
Credit rating grade (short-term claims on corporates,
ADIs and overseas banks)
Risk-weight (%)
1
£2
2
4
20
50
100
150
To be used in conjunction with the following credit rating grades:
Table 12.5 Recognised long-term ratings and equivalent credit rating grades
Credit rating
grade
1
2
3
4
Standard and Poor's
Corporation
AAA
AA+
AA
AAA+
AA-
BBB+
BBB
BBBBB+
BB
BB-
Moody’s Investor
Services
Aaa
Aal
Aa2
Aa3
Al
A2
A3
Baal
Baa2
Baa3
Bal
Ba2
Ba3
Fitch Ratings
AAA
AA+
AA
AAA+
A
ABBB+
BBB
BBBBB+
BB
BB-
425
5
6
B+
B
B-
Bl
CCC+
Caal
Caa2
Caa3
Ca
ccc
ccccc
c
D
B2
B3
c
B+
B
B-
CCC+
ccc
ccccc
c
D
LARGE CREDIT EXPOSURES
An 'exposure' under prudential regulations generally means a potential for loss under a finance
facility that has been provided. In terms of credit exposure, when an ADI provides a large loan to
a business, it exposes itself to that business in the event that it defaults. A large exposure means
that the capital base of the ADI is exposed.
The Australian Prudential Regulation Authority regulation for large credit exposures is found
under Prudential Standard APS 221, Large Exposures. This guideline states the objective as
follows:
This standard aims to ensure that locally incorporated ADIs implement prudent measures
and limits to monitor and control risk of concentrations in respect of large credit exposures
to individual counterparties or groups
of related- counterparties on a consolidated group
basis.
The obvious issue here is that the Australian Prudential Regulation Authority feels that large
exposures have the potential to affect ADI solvency. The standard states that credit risk exposure
increases when spread through only a small number of lending counterparties.
Under the standard, a large exposure is defined as an exposure to an individual or group of
counterparties that exceeds 10 percent of the consolidated capital base. The capital base is that
specified in the capital adequacy guidelines and includes both tier 1 and tier 2 capital.
Note, however, that it is not illegal to have exposures greater than 10 percent as long as the
Australian Prudential Regulation Authority is notified. In these circumstances, the authority
may choose to place additional requirements on an ADI (such as a higher capital adequacy
benchmark ratio) to address such risks.
The Australian Prudential Regulation Authority requires that each ADI report its large exposures
(10 percent and above) quarterly. This highlights the need for ADIs to specify correctly the
relationships between common counterparties, to ensure all exposures are recognised.
426
SECURITISATION
Securitisation is a good credit risk management tool in certain circumstances. As with large
exposures, however, the Australian Prudential Regulation Authority imposes some guidelines
in allowing securitisation to be effective as a technique. The issues for securitisation are to be
found in the Prudential Standard APS 120, particularly, Guidance Note AGN 120.3.
Guidance Note 120.3 is expansive and we cannot canvass all issues in this section. It works
around the concept of a clean sale, which:
■
■
absolves the financial institution from any legal recourse from the sale of loans
results in the financial institution not holding capital against the loan.
The above can be likened to the sale of a motor vehicle. Once the sale has been effected, the
seller is not liable for any defects or accidents. If, however, there is an obligation held by the
securitising financial institution, then the Australian Prudential Regulation Authority will deem
that the credit risk remains with the financial institution. How the obligations are removed is
summarised in the following sections.
Clean sale supply of assets
This section of the prudential note deals with the sale of loans that are not revolving, such as
home loans. The following rules govern a clean sale:
■
■
■
■
There should be no beneficial interest in the sold assets and absolutely no obligation to the
financial institution.
There should be no recourse (including costs) to the lending institution. In addition, there
should be no obligation for the lending institution to re-purchase the lending assets.
The amount paid for the loans should be fixed and should be received by the time the assets
are transferred from the lending institution.
Any assets that are provided to the special-purpose vehicle as a substitute or provided at
below book value are not considered as relieving credit risk.
The above guidelines have not had the benefit of substantial testing and the concern is not about
legal recourse; rather, the concern is about moral recourse. The selling of financial assets is
always subject to the problems of asymmetric information where the seller knows more about
the loan than the buyer. There is a concern that in the event of economic downturn, when
defaults become more frequent, sellers of assets maybe under a moral obligation to re-purchase
assets. The moral obligation occurs because there might have been something defective about
the loan that was not obvious at the time. Under these circumstances, the financial institution
would have been carrying the implicit credit risk.
427
Revolving facilities
Credit card receivables are the most common revolving facility that has been securitised. The
conditions for a clean sale of these assets are different, given that the lending assets can have
redraw facilities attached to them.
The following is a summary of conditions:
■
The rights, details and obligations of each party must be clearly specified, including the
distribution of cashflows.
*
As with
asset securitisation, the financial institution cannot supply additional assets
to the pool.
■
Liquidity shortfalls for the financial institution share must not exceed the interest receivable.
■
The financial institution always has the right to cancel any undrawn amounts on the
revolving facilities.
■
Again, like normal lending securitisation, the financial institution must be under no
obligation to re-purchase assets that have defaulted.
While there will be a new Standard as of 2018, it does not substantially change the management
of credit risk.
CREDIT DERIVATIVES
Chapter 11 discussed the innovation of credit derivatives, which had the ability to dramatically
alter the credit profile of the statement of financial position of a financial institution. Given the
infancy of the credit derivatives market, however, the Australian Prudential Regulation Authority
acknowledges the benefits of these instruments but has imposed conservative guidelines.
The guidelines for credit derivatives are divided into Guidance Note AGN 112.4, which deals
with credit derivatives in the banking book, and Guidance-Note AGN 113.4, which deals with
credit derivatives in the trading book. We are dealing with the management of credit risk (that
is, the management of an underlying loan), so our focus here will be on the former guideline. For
the purposes of the discussion, we will also assume that the financial institution is a protection
buyer (that is, it is protecting a credit position). This does not negate that the possibility that a
protection seller is assuming a credit position for the purposes of managing the concentration
risk of a portfolio.
We have already addressed large exposure issues in a previous section. Note that large exposures
include credit derivative transactions. The guidance note, however, is not clear as to whether
large exposures include those credit derivatives that result in the acquisition of credit risk or
all credit derivatives. Given the current conservative stance taken by the Australian Prudential
Regulation Authority, it may be monitoring the market as a whole.
428
The effectiveness of credit derivatives becomes an issue of how well the instrument reduces
the requirement of capital adequacy. Although a credit derivative may reduce the overall credit
exposure, its effectiveness may be reduced if capital relief is not forthcoming. The Australian
Prudential Regulation Authority will only provide regulatory capital relief if, for example, it is
satisfied that the set of credit events is not restrictive and allows the transfer of sufficient risk.
It is important, therefore, that it is made clear that the credit events clearly and unambiguously
transfer credit risk to the protection seller. The Australian Prudential Regulation Authority has
noted that it expects one of the credit events to be bankruptcy to allow capital relief. Further,
note that some materiality thresholds (an amount that is lost before the credit event is triggered)
may disallow relief.
The final issue in the regulatory treatment of credit derivatives is that of mismatches, which are
discussed in terms of asset mismatches and maturity mismatches. A credit derivative is deemed
to afford protection if the physical settlement has a deliverable obligation. If the settlement is in
cash, however, then the following requirements must be met for capital recognition:
■
■
■
The underlying and reference assets are the same.
The underlying asset is an obligation under the terms of the contract. An obligation is
defined as a financial obligation.
The reference asset ranks lower than the underlying asset.
In terms of maturity, a financial institution is deemed to have full protection if the maturity of
derivative equals the maturity of the underlying asset. If the maturity of the derivative is shorter
than the underlying asset, then the residual term of the underlying asset does not count for
capital adequacy. If, for example, a loan has a term of five years and the credit derivative has a
maturity of four years, then only 80 percent of the exposure is counted for regulatory relief.
Other issues such as currency mismatches and in-built options on credit derivatives are outside
the scope of this chapter. For those interested, see the guidance note for discussion.
DEVELOPMENTS IN REGULATION
Regulation is now an ongoing conversation. While Basel III has a final deadline of 2019, there
is no doubt that the ongoing problems in Europe, where there are shortages of both capital
and liquidity, will compel regulators to continually review prudential regulations in light of
developments in financial markets.
Credit ratings
The credit rating agency discussed here is Standard & Poor's, which is a private company owned
by the McGraw publishing group. Its main business is to rate debt issues or actual borrowers.
In defining its rating objectives, Standard & Poor’s (2000) state that A credit rating is Standard
429
& Poor's opinion of the general creditworthiness of an obligor, or the creditworthiness of an
obligor with respect to a particular debt security or other financial obligation, based on relevant
risk factors'.
The ratings are generally divided into two types: short term and long term. There are other
types but they are beyond the scope of this chapter. The definitions of these ratings are found
in Table 12.6.
Table 12.6 Standard & Poor's credit ratings
Rating I
AAA
AA
A
BBB
*
BB
B
ccc
cc
c
D’ .
Description
Long term
This is the highest rating. The obligor’s capacity to meet its financial commitment on the
obligation is extremely strong.
An obligation rated AA differs from the highest rated obligations only to a small degree.
The obligor’s capacity to meet its financial commitment on the obligation is very strong.
An obligation rated A is somewhat more susceptible to the adverse effects of changes in
circumstances and economic conditions than obligations in higher rated categories. The
obligor’s capacity to meet its financial commitment is still strong, however.
An obligation rated BBB exhibits adequate protection parameters. Adverse economic
conditions or changing circumstances, however, are more likely to lead to a weakened
capacity of the obligor to meet its financial commitment on the obligation.
An obligation rated BB is less vulnerable to non-payment than other speculative issues,
but it faces major ongoing uncertainties or exposure to adverse business, financial or
economic conditions that could lead to the obligor’s inadequate capacity to meet its
financial commitment on the obligation.
An obligation rated B is more vulnerable to non-payment than obligations rated BB, but
the obligor currently has the capacity to meet its financial commitment on the obligation.
Adverse business, financial or economic conditions will likely impair the obligor’s
capacity or willingness to meet its financial commitment on the obligation.
An obligation rated ccc is currently vulnerable to non-payment, and depends on
favourable business, financial and economic conditions for the obligor to meet its
financial commitment on the obligation. In the event of adverse business, financial or
economic conditions, the obligor is not likely to have the capacity to meet its financial
commitment on the obligation.
An obligation of cc is currently highly vulnerable to non-payment.
The c rating may be used to cover a situation where a bankruptcy petition has been taken
but payments on this obligation are being continued.
The D rating, unlike other ratings, is not prospective; rather, it is used only when a default
has occurred.
430
A-l
A-2
A-3
B
c
D
Short term
The short-term obligation of A-l is rated in the highest category by Standard & Poor's.
The obligor's capacity to meet its financial commitment on the obligation is strong.
A short-term obligation of A-2 is somewhat more susceptible to the adverse effects of
changes in circumstances and economic conditions than obligations in higher categories.
The obligor's capacity to meet its financial commitment on the obligation is satisfactory,
however.
A short-term obligation of A-3 exhibits adequate protection parameters, but adverse
economic conditions or changing circumstances are more likely to lead to a weakened
capacity to meet its financial commitment on the obligation.
A short-term obligation of B is regarded as having significant speculative characteristics.
The obligor currently has the capacity to meet its financial commitment on the obligation,
but it faces major ongoing uncertainties that could lead to the obligor's inadequate
capacity to meet its financial commitment on the obligation.
A short-term obligation of c is currently vulnerable to non-payment and depends
on favourable business, financial and economic conditions for the obligor to meet its
financial commitment on the obligation.
See the definition of D under the long term ratings.
* This credit rating starts what is known as speculative grades.
Note: Plus and minus signs are used to modify the ratings AA to ccc to indicate relative standing in
the rating category.
Source: Standard & Poor’s 2000, '2000 Corporate Ratings Criteria', www.standardandpoors.com, accessed 15
July 2002.
If credit ratings are to be the basis of alteration to capital adequacy, then it is important to
understand the rating process. The process starts with a meeting with the company under
consideration, to help the company know what to expect and what will be required of the rating
process. The corporate credit risk factors are divided into two: business risk and financial risk.
Both risks are further divided, as we will consider in the following sections.
Business risk
When measuring the business risk of a company, a credit rating agency will evaluate the following
factors.
Industry characteristics
An assessment is made of the prospects and risks attached to the industry in which the business
operates. This process will also indicate the cap of ratings for the business within the industry.
The riskier the industry, the lower is the cap. Issues that would be addressed include:
431
■
■
■
key rating factors for that industry, such as profitability factors and risk vulnerability
diversification factors for businesses that have exposures to different industries
size, and geographic and market dominance.
Management evaluation
The management of the business will be assessed for their ability to plan and implement their
business plan. An opinion is formed of management's appetite for risk, and the inclusion of past
business successes would be considered. There is also a focus on the structure of the organisation
and any undue reliance on one person.
Industry-specific factors
For many industries, Standard & Poor’s will provide specific factors that it will consider. The
following is a summary of these considerations:
■
industry regulations
•
markets
■
operations (revenue and costs)
■
competitiveness.
Financial risk
The following factors are considerations when measuring financial risk:
■ Accounting quality. Given that the qualitative assessment of the business is based on the
audited accounts, financial reporting quality starts as a base.
•
Financial policy. An assessment of the firm’s financial policy depends on whether such a
policy exists, how it is used, how it assesses risk and how it proposes risk mitigation.
■
Profitability and coverage. Given that earnings and cashflow repay debt, this issue is very
important in Standard & Poor’s analysis. Some measures that are examined are:
■
pre-tax pre-interest return on capital
•
■
operating income as a percentage of sales
■
earnings on business segments assets.
A number of issues are also examined in this area, including:
■
trends
■
an analysis of historical trends and the reconciliation to projected earnings
■
earnings in relation to fixed charges.
A number of adjustments are made to ensure accounting issues do not cloud the earnings power
of the business.
■
Capital structure/leverage and asset protection. The following ratios are considered by Standard
& Poor’s:
■
total debt divided by (total debt plus equity)
432
(total debt plus liabilities off the statement of financial position) divided by (total debt
plus liabilities off the statement of financial position plus equity)
■
total debt divided by (total debt plus market value of equity).
■
Asset valuation. In terms of capital structure, the value of the business’s assets can make
a material difference to viability.
Financing off the statement offinancial position. Standard & Poor’s factor the following into
leverage considerations:
■
operating leases
■
debt of joint ventures and unconsolidated subsidiaries
■
guarantees
■
take or pay contracts and obligations under throughput and deficiency agreements
■
receivables that have been factored, transferred and securitised
■
contingent liabilities.
Preferred stock. The characteristics of the preference shares are examined to ascertain
whether they exhibit the features of debt or equity. Redeemable preference shares, for
example, would be considered to be debt.
Cashflow adequacy and ratios. Cashflow, not accounting earnings, is what repays debt. It is
the most critical factor in Standard & Poor’s assessment. The ratios used are:
■
funds from operations divided by total debt
■
earnings before interest, tax and depreciation divided by interest
■
(free operating cashflow plus interest) divided by interest
■
(free operating cashflow plus interest) divided by (interest plus annual principal
repayment obligation)
■
total debt divided by discretionary cashflow
■
funds from operations divided by capital spending requirements
■
capital expenditure divided by capital maintenance.
The need for capital. Standard & Poor’s examine a business’s requirements for equity and
working capital, relative to its capital works needs.
Financial flexibility. Standard & Poor’s examine issues such as overreliance on any one
finance source.
■
■
■
■
■
■
Much more could be added to the above, but these are the major issues that are considered in the
rating process. If you are interested in more details, the full process is available on the Internet
(www.standardandpoors.com).
How Standard & Poor's combine the above analysis is a trade secret. All that is publicly known is
the set of elements that are considered in rating an issuer or obligation. Knowing these elements,
we can move onto the new capital adequacy proposals.
433
INDUSTRY INSIGHT
Credit and equities: Banks ponder a double-whammy
Just when Australia’s banks were back in favour with investors, a leading bank director has
raised the spectre of a double-whammy from stronger credit growth and a greater investment
in equities.
There was nothing alarmist about the warning from National Australia Bank executive director,
finance, Mark Joiner. He pointed out that the big four banks would be forced to have a greater
reliance on offshore wholesale funding markets when deposits shifted out of the banking
system.
Joiner is flagging an issue that was a top priority for policymakers in the two years after the
global financial crisis but has since slipped into obscurity. The issue can be summed up with the
following questions: Is Australia content to keep funding the economy through the big four banks
in offshore markets? What needs to be done to encourage market-based financing of business
investment? How can banks lock in deposit funding? What impact will new capital rules have on
the ability of banks to fund credit growth?
Joiner’s comments were made at the release of NAB’s s 1.4 bihion cash profit for the three months
to March alongside chief executive Cameron Clyne. They are timely and worthy of broader
debate for two reasons. They come amid a groundswell of academic and political comment about
the need for a financial system inquiry. Those calls have been backed by shadow treasurer Joe
Hockey.
A second reason is that the global banking system is in the midst of implementing new capital
rules called Basel III that will force banks to match the maturity of their loan assets with deposits
of similar time frame.
Joiner has tried hard to get a discussion going about the role banks play in the financial system.
He has been a strong advocate for developing a corporate bond market so that superannuation
savings can be recycled more efficiently and reduce Australia's reliance on banks. Joiner thinks
the war for deposits will continue for five years but he can see the day when the money on deposit
will shift elsewhere.
He is not alone in worrying about this. Credit rating agencies have warned that stronger economic
growth will result in Australia's banks being forced to draw more heavily on offshore markets for
wholesale funding;.
Moody’s Investors Service cut the ratings of the big four banks about a year and a half ago because
of concerns that a stronger domestic economy would increase the need for offshore funding.
The Australian Prudential Regulation Authority (APRA) keeps a close eye on the deposit and
wholesale funding structures of the banks. It made historic concessions to allow the banks to
diversify their funding through the issue of covered bonds, which have a higher credit worthiness
than bank bond issues.
434
While it is true that the banks have diversified their wholesale funding by geography, type and
quality, they continue to rely heavily on short-term deposits. Joiner says the level of deposits in
the banking system from Australian companies is at a record level. The APRA data makes it clear
that if it were not for this business conservatism, the overall deposit to loan ratio would be a lot
worse than it first appears.
This is evident from an examination of loan to deposit ratios using data published by APRA.
Household loan to deposit ratios are 10 times worse than those of big business. The shortfall in
household deposits relative to loans as of March this year was: ANZ Banking Goup $112 billion,
Commonwealth Bank of Australia $173 billion, NAB $126 billion, Westpac Banking Corp $186
billion. The shortfall between non-household loans and non-household deposits was: ANZ $28
billion, CBA $5.7 billion and NAB $24.8 billion. Westpac had deposits in excess of loans of $6
billion.
The slump in credit growth in Australia, which has been welcomed by Reserve Bank of Australia
governor Glenn Stevens, has allowed the banks to fund their lending with deposits. Stevens has
said that the reduction in credit to sustainable levels is positive for Australia and he doubts if
there will soon be a return to the levels of credit growth seen over the 20 years leading up to 2008.
However, Joiner is more concerned with retail depositors turning to other asset classes, and
businesses taking their cash to invest in growth. Deposits are usually a short-term liability, unlike
most banking assets. Rating agencies like to talk about the behavioural characteristics of assets
and liabilities. They say an asset like commercial paper, which is usually issued for three to six
months, has much better behavioural characteristics than a balk deposit, which can often be at
call.
The regulators have recognised the problems that can be caused when there is a mismatch of
assets and liabilities in institutions that are operating at very high leverage. Before the financial
crisis, Australian banks had leverage of about 20 to 30 times their capital. Banks overseas such as
Deutsche Bank and UBS had leverage more than double those levels.
The new capital rules are designed to lessen that problem by forcing banks to match deposits
and liabilities. But that regulatory approach will penalise banks with assets that have long-term
maturities relative to banks with short-term asset maturities. The heavy proportion of Australian
lending on mortgages, which have terms of 20 to 30 years, means they will be penalised relative
to a commercial bank lending in syndicates or through commercial paper for periods of three to
five years.
One way to lessen the reliance of Australian banks on mortgages is to encourage securitisation
of mortgage loans. This is one of the features of the Canadian financial system, which has a
government guarantee of mortgage insurance.
The Canadian model for securitisation has gained strong support in Australia from various
commentators and academics and is said to be attractive to Hockey. Australian Securities and
Investments Commission chairman Greg Medcraft threw his support behind it before be joined
ASIC.
435
However, the Canadian model of guaranteeing mortgage insurance so that mortgage securitisation
programs carry a government guarantee has several drawbacks. Canada's home loan default rates
are double that of Australia, which suggests that government intervention has increased the level
of imprudent lending. The Canadian Mortgage and Housing Corporation had accumulated total
liabilities of SC570 billion at the end of March this year.
One positive side effect of the Canadian approach is that Canadian banks can use a zero risk
weight for securitised mortgage loans compared with a 50 percent risk weight for Australian
mortgages on the books of Australian banks. Securitised Australian mortgages could be sold to
Australian superannuation funds and thereby lessen the need for banks to tap offshore markets.
The issues and questions raised by Joiner do not need to be answered immediately.
It will take many years for Australians to recover from the financial crisis. Deposits will remain
popular. But deposits are not sticky. They can move fast when interest rates fall. Australian banks
will probably continue to source about $80 billion a year in funding from offshore markets,
which is manageable. But those markets are a lot less stable and less liquid than they were before
the crisis. The deferral of the TRUenergy initial public offering is not unexpected considering
the state of equity markets. The joint lead managers were happy to be on tap for a deal but were
aware the listing could slip into next year.
The second half of this year was looking a little crowded on the IPO front, despite the deferral of
the Genworth IPO earlier this year. TRƯenergy was in a pipeline for 2012 that included the $3
billion Coates Hire IPO and the McAleese Transport float.
The first half of next year could be a boomer for investment bankers and the various IPO advisers.
But that does not help with the here and now.
There is a possibility that 2012 will end with an IPO bang of sorts but that will require a strong
profit reporting season with plenty of positive surprises. He said regional banks would come
under pressure to invest heavily in advanced risk management systems to avoid having to meet
more onerous capital obligations than the major banks.
•
Souice: Boyd, T 2012, 'Credit and equities: banks ponder a double-whammy’,
Australian Financial Review, 15 August, p. 42.
When capital adequacy was introduced in 1980, it was a simple calculation that was designed
to be comparable across all banks, regardless of jurisdiction. However, its simplicity was also its
weakness - for two reasons. The risk weighting of 50 percent for home loans, regardless of LVR
or mortgage insurance and 100 percent for corporate risk, pushed banks to heavily weight their
lending books toward mortgages. Secondly, the 100 percent risk weighting for corporates failed
to differentiate the various risks. This was an added cost of regulation.
These issues were resolved in Basel II, introduced in 2004, whereby home loans were better
articulated and corporate credit risk was risk weighted by credit rating. However, it should be
mentioned that these new measures were not successful in averting the global credit crisis. The
436
major issue here was liquidity, and this has been addressed in Basel III. But this has led to the
joining of credit and liquidity issues in Basel III, and this issue is the basis for the above article.
The major import of this article is that prudential regulation, while providing safety, also has a
cost. Under Base III considerations, assets and liabilities maturities might have been matched.
This points out the ever increasing layers of regulation. And, as the article points out, it would
come at a cost, as regulation increases costs. But it also points to another observation - that
credit behaviour is now being influenced by regulation and is becoming more complex.
SUMMARY
1.
What are the issues of credit risk from the perspective of the regulators?
Regulators do not directly regulate the credit risk exposures of ADIs. A number of
regulations, however, are in place primarily to protect the interests of depositors.
2.
How is capital adequacy related to credit risk considerations?
For every loan that a lending institution makes, the institution is obliged to set aside
capital. This is to protect depositors in the event of impaired assets. The minimum
benchmark for capital adequacy is 8 percent.
3.
What are the issues of large exposures?
Internationally, many financial institutions have failed because they have lent too much
to a single entity. To ensure this is not the case in Australia, the Australian Prudential
Regulation Authority requires that a lender report any exposure in excess of 10 percent
of its capital.
4.
What are the securitisation issues for regulators?
Lenders use securitisation for many reasons. The credit risk factor, however, is of most
concern to the Australian Prudential Regulation Authority. If ADIs use securitisation
for capital purposes, which is the most common reason, then the authority requires a
clean sale which means that the ADI cannot assume the credit risk of a securitisation
structure in the event of the default of an underlying security.
5.
What are the credit derivative issues for regulators?
Credit derivatives are an effective tool for dealing with credit risk. The Australian
Prudential Regulation Authority requires, however, the direct matching of terms for
the regulatory relief for these instruments.
6.
What is the credit rating process?
Credit rating is a process whereby an independent body assesses the probability that
debt issues will be repaid in a timely manner. The two most well-known credit rating
agencies are Standard & Poor's and Moody's. Such agencies will assess both the business
risks and the financial risks of a company that desires a credit rating. Each agency's
437
exact process of assessment, however, is a trade secret. The credit rating process has
become important because it will be the basis of new capital adequacy guidelines.
7.
What are the new capital adequacy guidelines?
One major criticism of capital adequacy is that it does not distinguish the various credit
risks of business. Under the original guidelines, a company nearing default could have
the same profile as those companies with AAA ratings. The new guidelines seek to
differentiate these types of credit risk.
DISCUSSION QUESTIONS
Explain how the capital adequacy guidelines deal with the regulator’s concern for credit
risk.
2. Westpac’s 2001 statement of financial position is presented opposite. Calculate the capital
adequacy ratio.
3. Discuss the shortcomings of the current capital adequacy guidelines.
4. How do the proposed capital adequacy guidelines deal with the shortcomings that you
noted in question 3?
5. Referring to the Westpac financial statement again, what difficulties do you encounter if
you need to calculate capital adequacy under the new guidelines?
6. Should all financial institutions be able to use internal ratings?
7. What would be the difficulty in identifying large exposures?
8. Discuss the advantages and disadvantages of concentrated credit portfolios.
9. From a regulatory point of view, what are problems with securitisation as a credit risk tool?
10. Credit derivatives are an effective credit risk tool. Why are the regulators concerned about
them?
11. Read the ‘Industry insight' (Regional banks and the Basel II capital standards’. Consider
which sections of a regional bank’s lending portfolio are riskier than those of a major bank’s
lending portfolio. Then, assess what you consider to be an appropriate capital adequacy
provision for regional banks. You should consider the difficulty of distinguishing between
regional banks and major banks.
12. What are the difficulties with using credit rating agencies in the due regulatory process?
1.
438
Statement of financial position as at 30 September 2001—Westpac Banking
Corporation and its controlled entities
Consolidated
2000
2001
(Sm)
(Sm)
Assets
Cash and balances with central banks
1
079
836
Due from other financial institutions
5 094
3 325
10 629
7 174
2 960
2 731
122 250
107 533
15 700
15 665
7 352
7 547
482
620
Goodwill
1 501
1 535
Fixed assets
1034
1
Trading securities
Investment securities
Loans
Acceptances of customers
Life insurance investment assets
Regulatory deposits with central banks overseas
175
441
467
21 323
19010
189 845
167 618
5 954
3 972
Deposits and public borrowings
96 157
89 994
Debt issues
27 989
19 203
Acceptances
15 700
15 665
706
651
Life insurance policy liabilities
7 123
6 991
Provisions
1038
989
20 635
175 302
15 999
153 464
Subordinated bonds, notes and debentures
4 045
4 175
Subordinated perpetual notes
Total loan capital
793
4 838
717
4 892
180 140
158 356
9 705
9 262
Deferred tax assets
Other assets
Total assets
Liabilities
Due to other financial institutions
Tax liabilities
Other liabilities
Total liabilities excluding loan capital
Loan capital
Total liabilities
Net assets
439
Consolidated
2001
2000
($m)(Sm)
Equity
Share capital
2 233
2 258
465
465
Reserves
2 819
3 099
Retained profits
Equity attributable to equity holders of
Westpac Banking Corporation
4 174
3 435
9 691
9 257
Outside equity interests in controlled entities
14
9 705
5
Trust originated preferred securities
Total equity
9 262
Note: The above statement of financial position should be read in conjunction with the accompanying
notes and discussion and analysis in the following source.
Source: Westpac Investor Relations. See Westpac Banking Corporation 2001, www.westpac.com.au, accessed
July 2002.
REFERENCES AND FURTHER READING
Australian Prudential Regulation Authority 1999, Capital Adequacy of Credit Derivatives, Canberra.
Australian Prudential Regulation Authority 2000, Guidance Note AGN 120.3, Purchase and Supply of
Assets (including securities issued special-purpose vehicles), September, Canberra.
Australian Prudential Regulation Authority 2000, Guidance Note AGN 220.1, Impaired Asset Definitions,
September, Canberra.
Australian Prudential Regulation Authority 2000, Guidance Note AGN 220.2, Security Valuation and Pro­
visioning, September, Canberra.
Australian Prudential Regulation Authority 2000, Guidance Note AGN 220.3, Prescribed Provisioning,
September, Canberra.
Australian Prudential Regulation Authority 2000, Prudential Standard APS 120, Funds Management and
Securitisation, September, Canberra.
Australian Prudential Regulation Authority 2000, Prudential Standard APS 221, Large Exposures, Sep­
tember, Canberra.
Basel Committee on Banking Supervision 1999, A New Capital Adequacy Framework, Bank for International
Settlements, Basel. (Re-issued for comment May 2001.)
Standard & Poor's 2000, accessed 15 July 2002, 2000 Corporate Rating Criteria, www.standardandpoors.
com.
PROBLEM LOAN MANAGEMENT
LEARNING OBJECTIVES
By the end of this chapter you should be able to:
1. outline why loans default
2. highlight the extent of problem loans
3. explain why the business cycle is important for problem loans
4. define problem loans, provisions and regulatory issues
5. discuss the capital issues of problem loans
6. define ‘structure dynamic provisioning'
7. restructuring problem loans
8. illustrate a case from law.
KEY TERMS
bad debt write-off
dynamic provisioning
liquidation
provisions
business cycle
general provisions
mild financial distress
severe financial distress
441
coordination problem
impaired assets
moderate financial distress
specific provisions
442
INTRODUCTION
For most of this book, our focus has been on the assessment and approval of loans. Lending is
clearly a risky activity, however, and lending institutions occasionally grant loans that incur a
loss. The loss may occur as a result of many factors, from poor management of the borrower to
the timing of the business cycle.
Bad loans, known commonly known as Non Performing Loans (NPLs), have the following effect
on lending institutions (Alvarez and Marsel):
■
Reduction in net interest income;
■
Increase in impairment costs;
■
Additional capital requirements for high risk weighted assets;
■
Lower ratings and increased of funding adversely affecting equity valuations;
■
Reduced appetite for new lending; and
■
Additional management time and servicing costs to resolve the problems.
The first course of action, therefore, is not to foreclose, but to manage the asset or firm. In more
recent times, analysts have also viewed bad loans as a risk to the profitability of lenders and their
ability to pay dividends.
In the eyes of the regulators, credit default is a serious issue and is now being viewed carefully.
While we make the occasional comment about regulatory issues, it is considered more fully in
the chapter on credit risk from the regulator's perspective (Chapter 12).
CAUSES OF DEFAULT
A default is defined here as a loan for which the repayments are overdue. Lending institutions
may experience defaults and problem loans for the following reasons (Golin 2001):
■
lack of compliance with loan policies
■
lack of clear standards and excessively lax loan terms
■
inadequate controls over loan officers
■
overconcentration of bank lending
■
loan growth in excess of the bank’s ability to manage
■
inadequate systems for identifying loan problems
■
insufficient knowledge about customers' finance
■
lending outside the market with which the bank is familiar.
All these reasons for default are found within a lending institution. Many problem loans could be
avoided by better lending procedures and policies. Also important is credit culture. This refers
to the culture of personnel in dealing with procedures and policies. Often, when a loan becomes
bad, it is because the policies and procedures are either circumvented oi ignored. While this may
443
not be a problem in the short term, when loan portfolios become stressed, credit culture becomes
exposed. This was a common characteristic in American banks during the global financial crisis.
Credit risk is never static, however', and many loans that were validly granted can become bad for
many different reasons. Two examples are when a recession affects firms that rely on cashflow
or when firms wind up because their products have become outdated. The issue then becomes
how best to monitor these situations. Monitoring is easier said than done.
While it may be easy to monitor a small portfolio of loans, the situation becomes more complex
as the financial institution becomes larger. This complexity introduces higher and higher costs
for monitoring. To ensure efficiency, indicators (such as consecutive missed payments) are
normally implemented. These indicators are normally ' noisy', however, which means that they
do not present a clear picture of the situation or indicate remedial action. In many cases, these
indicators highlight a problem loan when it is too late, resulting in a less than optimal situation
for the lending institution.
In Chapter 11, we noted that one function of default models is that they can provide early
warnings of developing problem loans. This can be helpful for monitoring purposes.
THE EXTENT OF PROBLEM LOANS
Table 13.1 shows the experience of Approved Deposit Institutions since 2004 with the value
of impaired assets (bad loans) they are managing. From lows in the early 2000s, the value of
impaired assets increases sharply from the September 2008 quarter when the global financial
crisis hit.
September 2004
December 2004
March 2005
June 2005
September 2005
December 2005
March 2006
June 2006
September 2006
December 2006
March 2007
June 2007
September 2007
December 2007
Impaired Assets
5,113
4,146
4,045
4,030
3,685
3,587
3,610
3,423
3,386
3,732
3,807
4,121
4,289
4,414
Total assets
1,714,182
• 1,788,696
1,778,739
1 828,435
1,874,146
1,937,173
2,045,476
2,095,849
2,163,357
2,257,753
2,329,638
2,469,109
2,658,223
2,753,381
% Impaired
0.30%
0.23%
0.23%
0.22%
0.20%
0.19%
0.18%
0.16%
0.16%
0.17%
0.16%
0.17%
0.16%
0.16%
444
March 2008
June 2008
September 2008
December 2008
March 2009
June 2009
September 2009
December 2009
March 2010
June 2010
September 2010
December 2010
March 2011
June 2011
September 2011
December 2011
March 2012
June 2012
September 2012
December 2012
March 2013
June 2013
September 2013
December 2013
March 2014
June 2014
September 2014
December 2014
March 2015
June 2015
September 2015
December 2015
March 2016
June 2016
September 2016
December 2016
March 2017
June 2017
8,339
8,817
13,323
20,941
24,991
29,214
29,103
31,020
32,743
33,057
32,386
31,301
30,084
30,208
30,467
30,295
29,260
28,660
29,185
26,672
26,396
25,772
24,687
22,367
21,657
19,874
17,513
15,896
15,228
14,381
13,751
13,756
14,574
15,011
15,231
15,306
13,480
13,219
2,852,091
2,904,256
3,119,934
3,343,185
3,215,760
3,152,011
3,104,346
3,156,131
3,142,801
3,264,926
3,263,358
3,278,906
3,278,423
3,350,943
3,542,142
3,452,212
3,480,149
3,594,762
3,608,774
3,637,839
3,632,929
3,827,501
3,797,762
3,950,661
3,947,367
4,039,336
4,145,486
4,324,342
4,468,795
4,414,940
4,569,924
4,569,792
4,520,480
4,639,799
4,511,346
4,628,430
4,531,575
4,634,819
0.29%
0.30%
0.43%
0.63%
0.78%
0.93%
0.94%
0.98%
1.04%
1.01%
0.99%
0.95%
0.92%
0.90%
0.86%
0.88%
0.84%
0.80%
0.81%
0.73%
0.73%
0.67%
0.65%
0.57%
0.55%
0.49%
0.42%
0.37%
0.34%
0.33%
0.30%
0.30%
0.32%
0.32%
0.34%
0.33%
0.30%
0.29%
445
Since 2004, when the statistics were revised to be more consistent, Australia's impaired loan
performance has been an average 0.51% of total assets. The peak of this was 1.04% as a result of
the Global Finance Crisis of 2007 and 2008. Recent performance has fallen 0.29% (June 2017),
which outperforms most of the western world.
Lenders also need to recognise the potential for some loans to default and build this potential
into loan pricing. It is equally important for lenders to recognise that loans that default have a
corresponding impact on the profitability of the institution and, ultimately, this loss is borne by
the shareholder.
While bank bad loan performance can be put down to poor individual loan decisions, sometimes
the overall performance can be influenced by over reliance on a given sector. This can be seen
during mining boom when banks over lent to this sector without taking into account that
commodity prices can fall. This risk is called concentration risk and we will address this in
other chapters as well. It is the subject of this Industry Insight.
INDUSTRY INSIGHT
The Australian
New Commentary
The article highlights how over lending to one sector, in this instance, mining creates a
domino effect in other sectors. Over lending to one sector is referred to as concentration
risk and this article highlights that it rarely stops at one sector but affects others.
In this instance, the growing problem in the mining sector has knock on effects in other
sectors as well as geographical locations. Here, the knock on effect has been to contractors
and small business, plus the city of Perth (and obviousfy, in the greater scheme, Western
Australia). The article highlights the affect the down turn has on both lending facilities
of others, here an extension of a facility for one month but the effect on employment as
businesses struggle.
When banks are over exposed to one sector, a correction can be damaging. The primary
issue here is the drop of iron prices from USD 140 a ton ne to just USD 60. As pointed out,
when looked at in terms of income, this increased the debt charge from 14 basis points for
loans to 73 basis points.
The article infers that this problem is partly caused by the business cycle and this is the next
topic in this chapter.
446
THE BUSINESS CYCLE
A bank's experience with problem Ioans can normally be tracked by examining the business
cycle. The new Basle guidelines address this in the new guidelines. It is useful, therefore, to
consider the characteristics of the business cycle results in the problem loan experience.
This issue is becoming more important. There is a tendency to grant a loan at a point in time and
determine that it has become impaired at another point. This distorts management information
and portfolio management. Regulators now ask financial institutions to consider loan approvals
through the cycle. While we learn later that regulators focus on through the cycle provisions,
it is important to realise that rather than provide for a loan at a point of time, loans should be
provided through the cycle.
Recovery and expansion
During this period, confidence in the economy flourishes and new investment increases.
Increased consumer confidence leads to increased spending, which normally finds its way
into bank deposits. This gives banks an overall increase in money supply. With this increased
liquidity, banks look for more opportunities to lend. Unfortunately, this impetus often leads to
the relaxation of lending standards. While interest rates also increase at this time, they tend to
be relatively low. This exacerbates the situation because it encourages marginal investments by
individuals and business, as well as poor lending strategies by lending institutions.
Boom
This period of the business cycle is exemplified by asset inflation. Much investment goes into
real assets such as real estate, with much borrowing against these assets. The other characteristic
of this part of the cycle is overconfidence, which may lead to declining credit standards. While
interest rates are probably rising, this rise still does not dampen economic activity.
Downturn
While it is not easy to explain, the confidence in economic activity reaches a peak and then enters
a downturn. While a book can be written on the economics of this situation, one characteristic
of this section of the cycle is that asset prices decline. The decline of asset prices leads to less
spending and, ultimately, declining cashflow for many businesses. Where there have been lax
credit standards, loans approved under those standards no longer perform because they were
written during more optimistic times. Banks experience their greatest problem loans at this
time, because many loans were written against asset valuations that are now worth much less.
The marginal investment opportunities taken during more optimistic times become losses for
both investors and lending institutions.
447
PROBLEM LOANS, PROVISIONS AND REGULATORY ISSUES
When a borrower misses a repayment on a loan, a number of questions are triggered within the
lending institution. The first question is whether the missed payment is a temporary situation
or one that threatens to be permanent. If the situation is temporary, then the lending institution
will manage it differently than a more permanent situation.
Internationally, if the situation persists for longer than ninety days, then the loan is defined
as an impaired asset or non-performing loan. This is also the situation in Australia. It is called
'impaired' because the lending institution is not receiving full return on the loan and, therefore,
the loan is not fully valued on the financial institution's statement of financial position. Even
if a loan is partly repaid under these circumstances, then it becomes impaired; that is, a full
repayment does not need to be missed for an asset to be declared impaired. Given that the
financial institution has funded the loan, any portion of the loan not repaid in a timely fashion
will result in the loan being declared impaired, because it has the potential to create negative
income. The position of the loan in the institution's statement of financial position and income
statement will depend on the likelihood of the loan being repaid and the time for which it has
been impaired.
For regulatory purposes, the Australian Prudential Regulation Authority considers that if a
borrower has multiple facilities and one of those facilities becomes impaired, then all facilities
should be classified as impaired. The level of impairment is generally seen to be the face value
of the facility less the market value of any security. There are also guidelines on the valuing of
security (as discussed in Chapter 12).
When a lending institution recognises a problem loan, it needs to raise provisions. Chapter 12
highlights how the provisions are calculated.
OTHER CONSIDERATIONS WITH PROBLEM LOANS
We have been considering the regulatory and accounting definitions for the classification
of problem loans. It is worth recognising that many banks have internal classifications for
provisioning purposes, apart from any statistical purposes. One may question why lenders come
up with their own schemes as well as the ones required by the profession and regulators.
Lenders allocate capital against loans, so apart from the normal capital adequacy guidelines,
problem loans are going to affect the efficient use of capital, particularly because they are not
generating optimal income. Many lenders will therefore seek to redefine provisioning to align
with their capital policy. The internal policy will reflect the risk appetite of the lender. If the
internal policy results in greater than required provisions, then the lender could be argued to
have a conservative risk profile, while the opposite would represent a higher appetite for risk.
448
Chapter 11 highlighted that lending institutions often allocate capital to lending using, apart
from capital adequacy, the risk-adjusted return on capital (RAROC) or the CreditMetrics™
method. Considering these methods, we could conclude that:
■
a loan that has become impaired would fail the RAROC hurdle rate
■
CreditMetrics™ should provide a higher capital allocation amount.
THROUGH THE MARKET PROVISIONS
Reviewing Table 13.1 again, holding impaired assets on the statement of financial position does
not generate income. It is true that bad debts occur at the lowest point of the business cycle,
which is when financial institutions' profit statements are particularly vulnerable. What this
situation shows, however, is that credit will deteriorate over time until some loans default. In
other words, lenders need to recognise that:
1.
2.
credit risk is not static, but changes over time
bad debts should not come as a surprise.
While loans are costly to monitor, models examined in earlier chapters, if properly used, should
highlight any loans becoming problematic. Financial institutions nevertheless should be looking
at methods that smooth the bad debt experience and charge doubtful debts against income over
time rather than at the bottom of the economic cycle. The process should lead—rather than
lag—the bad debt experience. This approach will minimise income volatility that occurs as a
result of problem loans. The process as originally known as dynamic provisioning but will now
be part of the Basle 3 guidelines to ensure adequate capital is available for bad debts.
The process of estimating through the cycle debt provisions is shown in Chapter 12.
DEALING WITH DEFAULTS
Having discussed definitions, regulatory requirements and financial institution practice, we are
now in a position to examine the processes to undertake when identifying a problem loan.
Lending institutions are most reluctant to discuss their experiences and practices in this area, so
here we will identify three types of potential default situation and develop principles for dealing
with these situations. The three situations are:
1.
2.
3.
mild financial distress
moderate financial distress
severe financial distress.
In dealing with these categories, keep in mind that some problem loans can be difficult to
categorise and may display characteristics of more than one situation. Two principles always
apply, however, in dealing with these loans:
449
1.
2.
The primary aim of the bank is to minimise the loss to the bank. In many circumstances,
this will mean not liquidating the loan because the collateral will be worth only fire sale
value.
To manage these problems correctly, the economic worth of the loan is compared with the
economic worth of the borrower.
We will discuss the three types of financial distress and then complete this section with some
comments on the coordination problem loans where multiple lenders and priorities are involved.
Mild financial distress
Mild financial distress occurs when companies experience temporary cashflow shortages.
In most cases, this type of distress never enters the public arena and is not captured by the
regulator's definitions. As long as the loan does not stay in arrears for longer than ninety days,
this type of situation is opaque to the general community.
In many instances, the cashflow shortages are temporary and may be rectified within days. A
major receipt might have been delayed, for example. The shortage is sometimes more lasting and
more serious remedial action needs to be considered.
The overall condition of a company experiencing mild financial distress is that its economic
worth is of higher value than the repayment schedule of the loan. In other words, creating a
situation of default could rapidly depreciate the value of the firm's assets, causing both the
borrower and lender to lose money—a situation to avoid.
A number of remedies can be used in this instance. The simplest approach is for the bank to agree
to an extension on the repayment, in recognition of the temporary nature of the situation. In
most instances, the bank would charge penalties to ensure there are disincentives to prevent the
situation arising again. A review should be undertaken to ensure all potential revenues are being
exploited and costs are under control. The default of the telephone company One.Tel in 2001
was partly due to the failure of its billing systems to bill its clients properly and identify those
customers requiring further action. Some One.Tel customers had accounts that were overdue by
more than three hundred days. In other words, expected cashflow did not materialise.
Note, however, that banks should take further reviews or actions to ensure that their position is
protected. Remembering that cashflow repays the loan, a lender should take steps to protect the
cashflow of the borrower. The following suggestions are not exhaustive and critical review skills
should be used when examining each situation.
The most common cause of cashflow shortages in firms is overly rapid growth of the firm. As
firms grow, they need more investment in productive capital, whether computers, plant or land.
While this investment is undertaken, revenue normally does not keep pace until the growth
stage is complete. The solution may be to delay the investment until the temporary cash shortage
has disappeared, because there may be a logical milestone in the expansion where cashflows
450
reach a necessary level. In other words, the maturity of the development is so finite that it is
worthwhile accepting the temporary repayment of loan facilities.
In some instances, borrowers may hold assets that perform poorly. In many cases, these assets
are not core to the borrower and were acquired either through acquisition of another firm
or where diversification of firms in a group was seen as a positive step. In any event, selling
non-core assets, particularly if they are poor performers that degrade overall performance, can
generate valuable cashflow.
The final suggested solution is a simple one. Where lending can be a matter of transferring risk
to the appropriate party, mild financial distress may be a matter of requiring the shareholder to
supply more equity to the business. In other words, it may not be appropriate for the lender to
make any concessions.
Moderate financial distress
As mentioned previously, the difference between the various levels of distress is one of degrees.
In the case of moderate financial distress, a temporary cashflow shortage again is evident, but
the economic worth of the company is less than the repayment schedule of the loan. If the bank
were to wind up the borrower, then it would generate a loss in the process. This loss would
depend on the value of any offered collateral. A registered residential first mortgage would have
little negative effect on the value of its collateral (the home), while a manufacturing firm in
default may find its economic worth rapidly degrades in the absence of a buyer of its assets,
particularly if the assets are unique. The lender should simply liquidate the registered residential
first mortgage but exercise more care in the latter case. It may be more beneficial for the lender
to restructure the loan.
As a simple example, Little Company owes Big Bank $300. The owner of Little Company has
some special skills to run the firm for $15. These special skills result in the firm being worth
$315 with a probability of 0.8, otherwise zero. We will use these values to calculate the expected
value of the firm. (Additional information on the calculation of expected values can be obtained
from any good business or corporate finance textbook.) The liquidation value of the firm is
$200.
Before going through the options available, it is important to point out that the Little Company's
owner's special skills align with the fact that liquidating the firm would cause the assets of the
firm to depreciate in the absence of a buyer. This is the same as saying that the assets are unique.
In this example, there are two options.
The first option is to liquidate. This is the appropriate strategy for Little Company because its
expected value (or net present value) is negative, as follows:
0.8 (315 - 300) + 0.2(0)- 15 = -3
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What this tells US is that Little Company's pay-off will be $315, with a probability of 0.8 in the
good state. The firm needs, however, to make a repayment of $300. It receives nothing in the
bad state and spends (exerts) $15 worth of energy to produce. Its expected value is zero, so it
would prefer to liquidate and receive nothing. Big Bank would receive $200—a loss of $100—in
liquidation, which is the difference between the loan amount and liquidation value.
The second option is to restructure the loan to give the owner the incentive to continue. This
restructure is determined by calculating the break-even amount of the loan, which we will call
x—that is, the circumstances under which Little Company would continue:
0.8 (315
-x) + 0.2(0)- 15 = 0
The above formulation is the same as for the first option, except we are solving for X when the
expected pay-out is zero (the minimum that Little Company will accept). We find that X, or the
break-even loan amount, is $296.25.
A loan of $296.25 would be better than liquidation value of $200 and the owner of Little
Company would have the incentive to continue. Note that Little Company would continue the
gain as the value of the loan falls. In these circumstances, the bank loses $3.75 rather than $100.
Severe financial distress
This financial distress is the most obvious of all. It normally finds its way into the provisions
for doubtful debts of a bank. Under this scenario, severe financial distress is characterised by a
missed debt payment as well as the borrower having an economic worth less than the repayment
schedule. The normal course of action is to wind up the firm, but this may not always be the best
course of action. A number of issues need to be considered.
The first issue is whether the borrower has a sound business. Is the default due to reasons other
than the nature of the business? When Fairfax Limited defaulted on its loans in the early 1990s,
for example, the business was quite sound. The banks were able to arrange the company as a
going concern and refloat it. Given the level of intangibles (being the mastheads, such as the
brand name of the Sydney Morning Herald), there would have been little point to winding up the
company. Can we demonstrate how this would occur? The following example should help.
Big Bank is again having problems with Little Company (some lenders never learn!). Having
found its way out of trouble, Little Company finds that it owes senior bond holders $75 and
Big Bank $500. It still costs Little Company $15 to run the company and, if it continues, it will
be worth $520 with a probability of 0.75, otherwise zero. The liquidation value of the firm is
$90 and Little Company wants to default. What should Big Bank do? This is a simple process of
computing pay-offs.
1.
The bond holders would prefer to liquidate because they would receive $75 with certainty;
otherwise, their expected payment is $67.50 ($90 multiplied by 0.75, taking into account
that we are looking at the start of the period, not the end).
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2.
3.
Big Bank's expected pay-off is $350 ($500 multiplied by 0.75) if the firm continues;
otherwise, it receives only $15 from liquidated funds. The bond holders are seen to be the
senior lenders in this example and $15 would be the residual after they are paid.
Little Company has little incentive to continue, not wanting to lose $ 15.
Big Bank, therefore, needs to restructure the debt to ensure Little Company will want to
continue, but not lose in liquidation. The process is quite simple. Big Bank needs to buy out the
senior bond holders and find Little Company's break-even point.
Big Bank's loan is now $575 (original loan plus bond holder debt of $75), so we need to work
out the break-even debt that keeps Little Company interested in continuing. We do the same
calculation as before:
0.75 (520-x)- 15 = 0
where
X = 500, which represents the loan amount that ensures Little Company will not lose in
liquidation.
We need to compare the $500 against the liquidation value rather than the debt of $575.
The coordination problem
In the previous problem, we assumed that the senior bond holders would be happy to be bought
out by the junior debt provider. In our example, this would be true because the bond holders
would receive full value for their debt. In many restructures, however, not all debt holders
receive their full entitlement and they can hold up restructures by demanding to receive their
repayments in full. This is known as the coordination problem.
Much of the syndicated debt in the 1980s was arranged along these lines, with many banks being
involved. The important point of these arrangements was that relatively junior lenders were in
a position to affect the restructuring process by stopping the rollover of facilities. That junior
lenders were in such a position of power may be construed as faulty contract design.
We return to Big Bank and Little Company to see how junior lenders can have an effect on the
senior lenders' position with problem loans. In this example, we find that Big Bank is the senior
creditor, ahead of two bond holders (one senior and the other junior). Big Bank is owed $300,
the senior bond holder is owed $100 and the junior bond holder is owed $50. Given business
conditions, Little Company wants to restructure the business and offers two plans: A and B. Plan
A offers a pay-off of $400 with a probability of 0.7 and $100 with a probability of 0.3. Plan B
offers a pay-off of $500 with a probability of 0.5 and $100 with a probability of 0.5. How would
we consider the problem?
The first issue is to look at the pay-off of the company under each plan:
Plan A = $400 X 0.7 4- $100 X 0.3 = $310
Plan B = $500 X 0.5 + $100 X 0.5 = $300
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The expected pay-off for Big Bank as the senior lender would be:
$300x0.7 + $100x0.3 = $240
What does this tell US? Plan A would leave $70 for the remaining lenders and Plan B would
leave $60. This is clearly not enough. There are two possible outcomes to this situation: (1) the
junior lenders will need to take a smaller pay-out under the restructure or (2), as occurs in many
instances, the junior lenders force the senior lender (Big Bank, in this case) to take out some of
their loans in return for allowing the restructure to continue.
The latter case demonstrates what is known in academic literature as a 'time inconsistent
contract'. When loan contracts and lending policy are put in place, they should not be exposed to
any unintended renegotiation. This destroys the incentive of borrowers and lenders to perform
their obligations.
In the next section, we will discuss covenant breaches. Whereas covenant breaches normally put
loans into technical default, there are many instances where borrowers offer a penalty payment
for breaches of covenants rather than go into default. To accept this offer, the lender implicitly
destroys the incentive of the borrower to exert effort to continue to perform under the terms of
the lending contract. Such contracts go from time consistent (no re-negotiation in the event of
default) to time inconsistent. Knowing that the lender acceded once, the borrower is under less
pressure for the next breach.
A Process
Alvarez and Marcel have come up with the following process that gives the above some structure:
I
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INDUSTRY INSIGHT
Centro debt-for-equity deal unique in its complexity
Nabila Ahmed
The $3 billion debt-for-equity swap that brought Centro Properties Group and its satellite
Centro Retail under the one umbrella and the ownership of hedge fund lenders last year was
the most complicated restructure struck in Australia.
454
It involved several different classes of stakeholders: shareholders, senior lenders, hybrid
debt holders and junior lenders including bondholders and put option holders. Refinancing
negotiations dragged on for two years. The amalgamated group re-floated in December
last year on the Australian Securities Exchange. Among the hedge funds that emerged as its
biggest shareholders were Silver Oak Capital (7.3 percent), Burlington Loan Management
(5.8 percent) and Varde Investment Partners (5.5 percent).
Trio bail out Alinta
US private equity group TPG led a $2.1 billion bailout of West Australian utility Alinta
Energy in 2010. Oaktree Capital and Anchorage Capital partnered TPG and the trio agreed
to a compromise deal after other hedge fund lenders baulked at their first proposal. Lenders
who were owed $2.8 billion accepted a 'haircut' with the debt falling to $1.55 billion. They
acquired all the generation, retailing and pipeline assets, except for the Redbank power
station in NSW.
I-Med restructure
cvc Asia Pacific-owned radiology group I-Med was restructured last year. The new entity
was owned 90 percent by senior lenders owed about $550 million; and 10 percent by
mezzanine lenders owed more than $300 million.
cvc,
Total debt in the group was slashed to about a third of the original $900 million,
which
had acquired it in 2006 as part of its $2.7 billion takeover of the listed DCA Group, did not
receive any equity but a small fee of about $5 million.
in
Of the restructure, Freehills partner lohn Nestel says:
contrast to Centro and Alinta we
had to transfer the 1-Med group as might be done in a receivership. For the first time in our
market we were able to effect a so-called ■ credit bid' whereby the senior creditors essentially
exchanged their debt for ownership of the business. This was achieved outside a receivership
and through some very unique intercreditor terms. It would be dangerous to assume that the
same is possible in other restructures. For instance in Centro the court rejected the senior
lenders' attempt to 1 nullify' certain junior creditor rights, despite an argument they were 'out
of the money' because of the specific intercreditor terms in that case.
‘There is always plenty of contingency planning but in none of these restructures did the
creditors ultimately appoint receivers. Lawyers and insolvency practitioners may be keen
but typically lenders don't want their investment used to test these unchartered waters.
There is a price for certainty and in 1-Med it was very cheap given how deeply underwater
the other stakeholders were.
455
'Warring stakeholders was terrible for business but once peace was achieved, the business
was able to be quickly deleveraged and the new management team have done remarkable job
restoring confidence and value.'
Source: Ahmed, N 2012, ''Centro debt-for-equity deoi unique in its complexity’, Australian Financial
Review, 15 September.
Centro is a good example of managing a bad loan. If the lenders had foreclosed on the loans,
there would have been massive losses. Part of the problem was the nature of the loans, and ASIC
banned the auditor for poor practices. However, there was significant value in the business.
There would have been a number of ways to restructure the debt:
1. restructure the loan completely;
2. sell some businesses to pay down debt;
3. do a debt to equity swap.
In the article, we note that it is the third option taken by the lenders. However, as the article
points out the structure is complex and there are warring stakeholders. What this may mean in
the future is interesting. The question we need to ask ourselves is there any possibility for the
co-ordination problem.
Other breaches
In concluding this section, it is worth recognising that not all defaults are generated by missed
loan repayments. With many company loans, the approval of the loan is subject to the company
agreeing to various conditions, based on financial ratios. These give a lender some comfort that:
■
cashflow will not be unduly withdrawn from the company that would be available to repay
loans
■
the overall risk of the company cannot be substantially changed.
Such ratios are known as covenants and are normally written in such a way that a loan is
repayable if a covenant is breached. Covenants can include:
■
gearing ratios
■
dividend pay-out ratios
.
■
interest coverage.
The question then becomes: what is the correct procedure to follow when a company breaches
its covenants (a technical default) rather than misses a repayment? There is no reason not to
follow the principle of assessing the economic worth of the firm.
If the breach proves that the firm has become unviable, which is what a breach of a cashflow
covenant would probably show, then remedial actions can be taken. Often, however, a breach of
covenant has not affected the company's ability to make normal repayments, in which case the
covenant should be renegotiated.
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EXAMPLES FROM THE LAW
The previous section was designed to highlight that liquidation is not always the appropriate
action when debts are not repaid. In many cases, however, restructure of debts will not be possible
and liquidation of the company will be the only course of action. This text is not intended to be
a legal text, but the following information should be useful for understanding the winding up of
debts.
In most cases, a lender appoints a liquidator to wind up a company that is not in a position to
repay its debt. If the lender is secured, then actions will be taken to sell the security to repay
the lender. The situation is not so easy if the lender is not secured. The liquidator assesses the
amount that can be recovered from asset sales. Generally, this amount is less than the total
amount owing and lenders normally write off the remaining amount. While this is simplistic,
some court decisions on lending and problem loans highlight the care that should be taken when
lending.
In a celebrated case, the State Bank of Victoria lent money to the Victorian Division of the
National Safety Council. The monies were meant to be secured by containers of sophisticated
rescue equipment worth $250 000 in total; in reality, however, they were empty and sold for
$1,592 each. The problem here was that lenders had not exercised due care in investigating the
security and examining the company. The judgement of the case was scathing of the conduct of
bank officers.
A FINAL WORD
The fall out from the Global Financial Crisis not only focused lending institutions on their
lending decisions, but also how they structure their recovery operations. Again, Alvarez and
Marshal are helpful here in outlining how banks can improve their operation:
■
aligning their businesses with regulatory requirements such as setting up separate dedicated
in-house NPL units;
■
identifying, categorising and provisioning NPLs more rigorously;
■
standardising and improving work-out, legal enforcement and underwriting processes; and
■
developing additional restructuring products.
SUMMARY
1.
Why do loans default?
Loans default for a number of different reasons. Most reasons are not 'bad luck'; rather,
they are poor lending practices. The reminder is always there that some loans in a
portfolio will default.
457
2.
What is the extent of problem loans?
The experience has been that the level of problem loans does not remain static through
time. Quite often, the number of problem loans is influenced by factors such as the
business cycle.
3.
Why is the business cycle important for problem loans?
The ability to repay loans depends on the ability to generate cashflow. A firm's or
individual's ability to generate cashflow can be affected by the general state of the
economy. During a recession, demand is dampened, as is earning; the reverse occurs
during expansion and booms. It is no surprise to find that problem loans increase
during a recession.
4.
How would you define problem loans, provisions and regulatory issues?
The treatment of problem loans tends to be driven by regulatory requirements. A
problem loan is generally defined by the lateness of repayment, with the benchmark
being ninety days. After this time, a lending institution will make provisions for that
loan. The provision will be either general or specific. The regulatory authorities specify
statutory provisions. If the loan is deemed to be irrecoverable, it is written off.
5.
What are the capital issues of problem loans?
Problem loan management also has an impact on the allocation of capital. Capital
allocation for loans covers unexpected losses, so it is not surprising that many lending
institutions align their provisioning policies with their capital policies.
6.
What is structure dynamic provisioning?
As noted, problem loans can introduce volatility into a lending institution's earnings.
In recognition that problem loans will occur, lending institutions seek to use their
historical experience to forecast future problems’and thus smooth the problem loan
experience. This method is known as dynamic provisioning.
7.
How are problem loans restructured?
There a number of ways of dealing with defaults. The approach taken will depend on
the extent of financial distress being experienced by the borrower. There are three types
of financial distress: mild, moderate and severe. The general approach is to compare
the economic value of the firm with the repayment schedule.
8.
What do cases from law illustrate?
In illustrating the issue of generating problem loans, legal cases often find that blame
lies with the conduct of lending officers investigating the security and examining the
company.
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DISCUSSION QUESTIONS
,
Why are problem loans an issue?
Explain the difference between accounting, regulatory and internal provisioning policies.
Why are some parts of the business cycle identified with increased numbers of problem
loans?
4. Compare and contrast dynamic provisioning and other methods of assessing provisioning.
5. Discuss the advantages and disadvantages of dynamic provisioning.
6. Explain how to distinguish between the various forms of financial distress.
7. Would the timing of the business cycle influence the management of the business cycle?
8. Ifi Corporation has two loans outstanding. One loan is to Certain Bank for $400, while a
senior bond holder is owed $150. Ifi Corporation wants to put itself into liquidation and
default on its loans. The liquidation value is $160. The management of Ifi Corporation has
special qualities that would result in a pay-off of $420 with a probability of 0.8, otherwise
zero. For the management to continue, it would have to be paid $10. Carefully outline the
options available to Certain Bank.
9. In the case of a syndicated loan, there are often senior and junior debt providers. Where
a borrower defaults under this arrangement, the senior debt providers would be assumed
to be relatively well protected. Under what circumstances does this not occur? What steps
should be taken to protect senior debt providers?
10. What steps would you take if a borrower breached a covenant, leading it to technical default?
Your answer should highlight the contract issues.
1.
2.
3.
REFERENCES AND FURTHER READING
Alvarez and Marshal, 2016, Best Practices for Effectively Managing Non-Performing Loans
Australian Prudential Regulation Authority 2000, Guidance Note AGN 220.1, Impaired Asset Definitions,
September, Canberra.
Australian Prudential Regulation Authority 2000, Guidance Note AGN 220.2, Security Valuation and Pro­
visioning, September, Canberra.
Australian Prudential Regulation Authority 2000, Guidance Note AGN 220.3, Prescribed Provisioning,
September, Canberra.
Bennet, M October 2017, "’’Banks getting nervous about exposure to mining loans”, The Australian
Golin,J 2001, The Bank Credit Analysis Handbook, John Wiley & Sons, Singapore.
Greenbaum, SJ & Thakor, AV 1995, Contemporary Financial Intermediation, The Dryden United States of
America Press, Orlando, Florida.
Hogan, W, Avram, K, Brown, c, Ralston, D, Skully, M, Hempel, G & Simonson, D 2001, Management of
Financial Institutions, John Wiley & Sons, Brisbane.
Mellish, M 2001, 'Majors likely to lift bad loan provisions', Australian Financial Review, 23 April, p. 41.
Shanmugham, B, Turton, c & Hempel, G 1992, Bank Management, John Wiley & Sons, New York.
QUANTITATIVE FINANCE
LEARNING OBJECTIVES
By the end of this chapter you should be able to:
1. create a framework for modelling
2. explain and measure concentration risk
3. define expected losses
4. define and measure probability of default
5. define and measure loss given default
6. define and measure prepayment risk
7. identify the problems with quantitative modelling.
KEY TERMS
asymptotic single risk factor
expected losses
loss given default
prepayment risk
burnout
granularity
macro factors
probability of default
519
concentration risk
Herfindahl-Hirschmann index
micro factors
transformation regressions
520
INTRODUCTION
As mentioned in the chapter on credit scoring, modelling and the explosion of technology has
allowed the development of a plethora of financial models. This is the case also for credit-related
models. There are many ways of looking at these models, but we will take an approach that is
initially regulatory driven, and then concentrate on areas that are of primary concern to lenders.
However, this chapter needs to be understood by those who are not necessarily quantitative
driven, and so those who require more advanced treatments should consult more advanced
texts.
In this introduction, we need to address two issues. Firstly, what it is that we are trying to
measure, and, secondly, the assumptions that drive some of our models. After defining these
models, we will look at a number of other modelling issues.
For the most part, most of the modelling we have looked at has focussed on the issue of measuring
the probability of default (PD). While this is vitally important, it is only half of the task at hand.
The other task is to measure the amount of capital that should be provided for PD. We use the
PD nomenclature because it is prevalent in regulatory literature, but is generally the same as
credit risk. There are two types of capital: regulatory and economic. In terms of this discussion,
we will focus more on economic capital since lenders focus more on this type of capital and seek
to minimise it. More importantly, lenders will seek to provide capital on a portfolio basis, and
this is where we need to understand the assumptions behind Basel guidelines.
There are two fundamental assumptions behind the Basel guidelines. The first assumption is
known as granularity, while the second one is the asymptotic single risk factor (ASRF) model.
The fundamental understanding of granularity is that the nature of a credit portfolio is such that
an additional loan will not change the risk of the portfolio. In other words, the portfolio is near
or fully diversified.
The second assumption is that credit portfolios are subject to or affected by the ASFR model
which is denoted by:
v.-pM. + ^i-pZ,
where:
V is the value of the assets of lender i at time t
M is the systematic risk
z is the unsystematic risk
p is the risk attached to the systematic risk.
The importance of this model is that the value of the lenders portfolio is affected by a single
variable.
521
The import of these assumptions cannot be understated. If they do not hold for a portfolio, then
the assumptions around Basel II, and in particular credit portfolios, are violated. Therefore, we
will start with concentration risk, and then move onto the following modelling issues:
■
expected losses
■
probability of default
•
loss given default
■
prepayment models.
CONCENTRATION RISK
The assumptions mentioned above for Basel II are highly restrictive and, if breached, would
need extra capital to cover this. There are two types of concentration risk: name and sectoral. If
either of these are breached, then capital calculated by prudential guidelines may be understated.
Name risk is lending excessively to one counterparty. To highlight the issue clearer, name risk
breaches the granularity assumption of Basel by adding risk to the portfolio by adding a new
loan. In other words, the portfolio is not perfectly diversified. The second assumption of the
ASRF model can be breached by sector concentration. Sector concentration is broader than
name risk. It can be across industries, jurisdictions or geography. If any of these are existent, then
the ASRF model will be breached as it assumes that a single risk factor can affect the value of
the portfolio. Clearly, one risk factor will not affect all industries equally, let alone jurisdictions
or geographical regions. One other issue that affects this is that there will also be interactions
between industries as well. Given that it is unlikely that credit portfolios will be compliant with
the assumptions, the task is to measure concentration and then assign capital to it.
The normal starting point is the Hefindahl-Hirschman index (HHI), which is defined as:
i= 1
where:
H is the HHI
s
is the proportion of each firm's Ioans to the overall portfolio
n is the number of loans.
The HHI in reality measures the amount of concentration without reference to credit risk;
in other words, the credit risk is homogeneous. Regardless of this assumption, while it does
measure concentration risk, there is no reference to the amount of capital that needs to be put
aside.
522
There are a number of ways to refine this measure to make it more useful, albeit it will not be
perfect. What is required is a model that indicates how far away the portfolio is from granularity,
in other words, the level of diversification. Semper and Beltran provide an approach for this
(Semper & Beltran 2011). There are two steps:
First, reformulate the HHI into a vector:
HHI= SHT.I.SH
where SH are the individual exposures of each sector and I is the identity matrix.
Then, as the identity matrix assumes equal weighting, we replace it with a variance covariance
matrix of the exposure between sectors (VCM):
CI=SHT.VCM.SH
where CI is the concentration index. For complex reasons, the minimum covariance will be 0, so
negative covariances will be counted as zero.
There are a number of attractions to this approach:
1. The maximum concentration will be in the sector of the highest variance. Thus, if all loans
were in this sector, there would be maximum concentration.
2. If there is maximum diversification, then the CI = 0.
3. If loans are provided to a new industry, then the concentration will only be reduced when
variance of the new loans is lower than the variance of the overall portfolio.
While there is no specific charge for capital, this approach gives a more refined view of
concentration risk.
Most other approaches use value at risk methodologies. Gurtler, Hibbeln and Vohner (2010) give
a good summary of such approaches. However, they also point out that a number of statistical
properties are breached in this approach.
EXPECTED LOSSES
All credit portfolios make losses; it is a fact of life. However, there are a number of reasons why
forecasting expected losses is important. There are two major reasons, which can be divided
neatly into a now issue and a future issue. We have dealt with the future issue in the problem
loans section, that is, the need to put capital side.
Expected losses are an issue for lenders now because they need manage this risk. For the present,
lenders tend to estimate expected losses and price them in the interest rate on borrowings. In
simple terms, banks can estimate these from past experience, and many banks have become
quite adept in estimating such losses. However, more and more lenders are estimating the
components that make up expected losses.
523
Expected loss is normally defined as:
EL = PD
X
LGD
X
EAD
where
EL is expected losses
PD is probability of default
LGD is loss given default
EAD is exposure at default.
It is the first two that banks focus on, so we will concentrate on them.
PROBABILITY OF DEFAULT
.
Probability of default is a dừect result of a lender's culture, policies, systems and models.
Therefore, it can be said that there is no one approach to modelling default probabilities. For
example, the probability of default for a lender who predominantly provides home loans will
be quite different to one who provides commercial loans. For this reason, the approach we take
here, again, will be a general approach. However, the advantage of a well-specified model is the
ability to stress test the credit portfolio. Stress testing allows for variables to be changed and the
results to be observed. Regulators are increasingly using stress testing to ensure financial system
stability.
The probability of default can, generically, be defined as:
P(d) = f(lf, ne)
where:
P(d) is the probability of default
If is liquidity failure
ne is negative equity
The functional form above is intuitively satisfying, as the two states that affect default is
insufficient cashflow to repay loans in the case of If, and the disincentive for borrowers to
repay when the value of their assets falls below the value of their borrowings. The ne variable is
often resolved by using option pricing techniques such as KMV. However, the simplicity of the
functional form does not represent reality.
In reality, If and ne are both microeconomic variables which are affected by macro economic
variables and therefore will not fully explain the probability of default. The following simple
example may assist. Imagine a home loan borrower who loses their job and is unable to repay
their loan. The resultant unemployment may be the result of such factors as the level of the
524
Australian dollar, inflation or interest rates, all of which the individual borrower has no control
over. Such factors would also affect the value of the property. For a model to correctly specify
probability of default, it needs to include both micro and macro economic variables. We can
then specify the probability of default as follows:
P(d) = f(micro - factors, macro - factors)
In general, the data for estimation would be defaulted and non-defaulted entities, which lends
itself to a probit regression where the probability of default would be 0 for solvent entities and 1
for defaulted entities. So using an expanded form, it would be common to express the probability
of default as:
V. Pl micro - factors +
i
2
p. macro - factors +
i
Where p is the probability of default.
In building this model, the major task will be the selection of variables for both micro and
macro factors. To some extent the micro factors are easily specified, given the long period of
research into this area. Less so are the macrofactors, although macroprudential regulation, the
stress testing of the financial system, has brought this issue to the fore. The micro-factors can
be found in the credit scoring chapter. Miu and Ozdemir (2009) provide some useful insights
to the macro-factors. Before indicating them, it is important to understand that the probability
of default will be heavily dependent on the sector. So, if we use Miu and Ozedemir's example of
home loan lending in Canada, the models have the following variables.
Table 16.1 Home loan lending in Canada
■
■
■
•
■
■
■
•
■
•
■
Canada general model
(a broad-based model)
Real GDP
Industrial production
Unemployment
Corporate profit
Slope of yield curve
High-yield spread
Equity indices
Composite index of leading
indicators for Canada
Consumer credit
Delinquency rate
Short-term interest rate
Real estate model
(a sector-based model)
General health of the economy
- GDP
■ Unemployment
■ Interest rate
■ Retain sales
■ Composite index of leading indicators
• Consumer sentiment index
■ Durable goods
■ s&p 500, TSE Composite index
Specific to the sector:
• S&P/TSX Capped real estate index
■ New building permits
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• New housing starts
■ Residential real estate (US)
■ House price appreciation
■
Commercial real estate (US)
o Vacancy rates
o Rents per square foot
Source: Mỉu & Ozedemir 2009.
They also provide some helpful insights on picking the explanatory variables:
■
Explanatory power. Naturally, we would like to use variables with high explanatory power in
the modelling of the systematic credit risk.
■
Forecastability. We need to make sure the variables selected can be forecasted by the bank
(typically its economic department) under the specified stress events in a consistent fashion.
■
Stress testing versus forecasting models. If we would like to use these models also for forecasting
of PD using the current values of the explanatory variables (rather than during the stressed
scenario), we need to consider the use of various leading indicators as explanatory variables
(e.g. the national composite leading indicators).
■
Model coverage. For broad-based models, we need to use more generic macroeconomic
factors (e.g. interest rate and GDP); whereas, for sector-specific models, we should also
include industry-specific factors.
■
Data availability. Data availability is essential for modelling and forecasting. We need to be
careful that, even though the historical time series of some of the variables are available, the
calculation methodology of the variables has been changed, creating consistency problems.
■
Correlations among variables. We should not include highly correlated variables in the same
model to prevent multicollinearity.
■
Representation and coverage. We should try to include variables explaining the credit
environment (e.g. ratio of downgrade to total rating actions, S&p's outlook distribution,
etc.), as well as general economic and financial indicators.
LOSS GIVEN DEFAULT
Modelling loss given default (LGD) has proven to be difficult when compared to the modelling
of probability of default. It is interesting to note that most of the investigations into LGD have
focussed on the variables rather than the modelling technique. The variables have tended to
focus on the nature of loan, contract characteristics and economic conditions. To some extent,
this looks almost like the probability of default. Qi and Zhao (2011) rightly point out that
taking the distribution into account is important, and their survey of methodologies is a helpful
framework for this section.
Whereas the probability of default often uses regression methodologies, such as probit
regression, this is more difficult with LGD. This is because most LGD distributions are not
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normal. Therefore we will split two sections into two: variable selection and modelling. The
variables for loss given default are relatively standard:
■
type of loan,
■
seniority,
■
collateral,
■
term,
•
seniority of mortgage,
■
characteristic of company's liquidity, and
■
level of interest rates.
Depending on the nature of the lending institution, there will be other important variables.
Again, like probability of default, they may be industry specific variables.
As mentioned, most modelling is based on ordinary least squares. The problem with this
approach is that LGD should be bounded by 0 and 1; however, most regression methodologies
are bounded by infinity. So the discussion below summarises Qi and Zhao's survey.
The first model that they discuss is fractional response regression. The functional form of this
model is:
£(LGD|x) = G(x0)
where X is a series of explanatory factors for LGD and G(.) is a transformation function usually
a logistic function as follows:
G(xJJ)
=
1
1 + exp (-X0)
or a log function as follows:
G(x0) = exp (- exp (-xp))
To estimate the coefficeints, then, the following function is maximised:
E
1, /3 - ỵ ÍLGD( X log (G(x,3)] + (1 - LGDj) X log [1 - G(x,|3)] Ị
TRANSFORMATION REGRESSIONS
Transformation regressions use the inverse Gaussian function (as below) by taking LGD (0,1)
and transforming it to LGD(-O°,O°) to estimate the factors using OLS and then transform them
back to LGD(0,l).
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If you use this, be aware that LGD (0,1) will not be defined, and therefore some small adjustments
will be required.
DECISION TREES AND NEURAL NETWORKS
Decision trees and neural network, while being developed differently, have a similar concept.
Decision trees use rule-based trees which split as decisions are made about the factor. Neural
networks use rules based on the way humans think. Again, the results will be trees of decision
matrices. Figure 16.1 is an example of a decision tree.
Figure 16.1 Example of a decision tree
As a final comment regarding the accuracy of the above methods, assuming that the right
variables are selected, each of the models provide relatively accurate predictions.
PREPAYMENT RISK
The last model that we will look at is that of prepayment risk. Given that a whole set of fixed
rate lending is prepaid, lenders are constantly refining their models to estimate prepayment
risk. This is because lenders cannot re-invest in new loans at the same rate, thus making a loss if
borrowers refinance at lower rates. Many prepayment risk models are based on options. This is
because there tends to be a level of interest rates. However, these models tend to perform suboptimally because borrowers do not exercise the option optimally.
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The general formula that is used is:
Monthly prepayment rate =
(Refinance incentive) X (Season multiplier) X (Mont multiplier) X (Burnout)
The interpretation of this formula is:
1.
2.
3.
4.
Refinance incentive is the current level of interest rates with respect to interest rates in the
portfolio. If current interest rates are higher, then there will be little incentive to refinance
or prepay loans.
Season multipliers recognise that there are times when there are higher than normal
prepayments.
The month multiplier is similar to the season multiplier
Burnout recognise that the longer loans exist, the more likely they are to be prepaid.
One problem with the above modelling is that it is heavily dependent on interest rates as
the dominant singular factor in loan prepayments. However, the problem is that most of the
factors use historical data to estimate future prepayments. The historical data does not fit
comfortably with interest rates being the dominant factor. What this should highlight is that
there are other factors that are taken up, but not exclusively, in the burnout factor. A simple
example helps here. Home loans that are written for terms of 25 years usually refinanced after
about 7 years. This is because people normally refinance to trade up at this time, rather than
for interest rate purposes. Therefore, to successfully model prepayment risk, the other factors
must be recognised.
CONCLUDING REMARKS
To some extent, writing a chapter on quantitative credit modelling is a challenge, as this type
of modelling is relatively young and in a constant state of flux. Improvements are likely come
along as technology and the understanding of credit risk improves. Much of the development is
driven by regulatory change and as we go through periods of regulatory change, modelling and
the approach to modelling will continue to change.
However, there is a much deeper issue. Modelling cannot be carried out without data, and most
lending systems were established well before models were required. So, when it comes to data, it
is either not available or it is available in a form that is not particularly useful. Carrying out good
modelling will, therefore, be a challenge for analysts into the future.
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SUMMARY
1.
What is the framework for modelling?
Given the importance of prudential regulation, these regulations and their assumptions
drive modelling. The main assumptions around these regulations are granularity and
ASRF. Any breach will mean higher capital charges.
2.
Why is concentration risk important and how do we measure it?
The granularity assumption for prudential regulation means that credit portfolios are
well diversified. The ASRF assumption means that credit portfolios can be measured
by a single factor. Breaches to either of these policies will result in concentration risk.
The current measure of concentration is the Herfindahl-Hirschmann index. However,
as this does measure the amount of capital used, we must transform it.
3.
What are expected losses?
Expected loss is the losses that normally occur when taking risks. Therefore, losses on
loans are expected. Currently, expected losses are priced into loans, but in the future
they will be the basis of loan provisions. Expected losses are the product of probability
of default, loss given default and exposure given default. It is the former two that
command the most modelling.
4.
How do we define and measure the probability of default?
Probability of default measures the probability that a repayment will not be made for
a loan. Historically, it has been measured by micro factors. However, modelling now
include macro-factors as it is recognised that these affect the ability to repay. The task
is to select the correct variables.
5.
How do we define and measure loss given default?
Loss given default is a prediction the loss generated in default. Like probability of
default, much of the research is given to selecting the correct variables. However, it is
more important to note that ordinary least squares, the method normally used, does
not provide accurate results.
6.
How do we define and measure prepayment risk?
Prepayment risk is the risk that fixed rate loans will be repaid early and cannot be
reinvested in loans at a similar rate. Prepayment rate models predominately use option
model technology around interest rates. However, non-interest rate variables are
required and these are captured in the burnout variable.
7.
What are the problems with quantitative modelling?
Quantitative modelling is still in its infancy. It is affected by constant regulatory change.
However, the biggest issue is the access to the appropriate data.
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DISCUSSION QUESTIONS
Why are the assumptions of Basel II important when discussing concentration risk?
2. What is the major problem with the Herfindahl-Hirschmann index?
3. What are expected losses used for?
4. What have been the developments in the development of probability of default models?
5. When developing probability of default models, what would you use as the major variables
if you are addressing a portfolio of mortgages?
6. Do the same task as Question 5 above for a portfolio of construction loans.
7. What is the problem with using ordinary least squares when measuring loss given default?
8. What are the alternatives to ordinary least squares when estimating loss given default?
9. What is the problem in using interest rates as the major variable when estimating prepayment
risk?
10. What are the overall issues in developing credit models?
1.
REFERENCES AND FURTHER READING
Bessis, J 2010, Risk Management in Banking, John Wiley and Sons, West Sussex.
Bolocan, DM and Litan, CM 2011, 'Estimating the Probability of Default with Applications in Provision­
ing the Portfolio of Clients of a Credit Institution' in Transition Finance and Banking Research.
Choudhry, M 2007, Bank Asset and Liability Management, John Wiley and Sons, Singapore.
Gurtler, M, Hibbeln, M & Vohringher, c 2010, 'Measuring Concentration Risk for regulatory purposes' in
The Journal of Risk, Volume 12/Number 3, Spring 2010.
Min, Q and Zhao, X 2011, 'Comparison of modelling methods for Loss Given Default' in Journal of Banking
and Finance, 35.
Miu, P and Ozdemir, B 2009/10 'Stress Testing the Probability of Default and migration rate with respect
to Basel II requirement' in The Journal of Risk Validation, Volume 3/Number 4, Winter 2009/10.
Saita, F 2007, Value at Risk and Bank Capital Management, Academic Press, Amsterdam.
Sy, w 2007, A Causal Framework for Credit Default Theory, APRA Working Paper, Sydney.
Semper, JDC and Beltran, JMT 2011, 'Sector Concentration Risk: A Model for estimating capital require­
ments' in Mathematical and Computer Modelling, Elseviei'.