The board of a credit institution should be keen to establish a holistic policy that covers a whole range of requirements: from regulatory capital (Chapter 10) to eco- nomic capital.11Equally critical is the seamless integration of a stress testing sys- tem that, as of 2007, has become a cornerstone of Pillar 2 of Basel II. (Pillar 2 is the steady review of capital adequacy, along with other criteria of prudential bank supervision exercised by national regulatory authorities.)
Since the time of Basel I (1988), regulatory capital has been associated with expected losses. Originally, under Basel II, the computation of expected losses (EL) under the internal ratings-based (IRB) method (with two alternatives: F-IRB and A-IRB) was provided in a comprehensive manner expressed through the algorithm:
EL⫽PD⫻LGD⫻EAD (5.2)
where:
PD⫽probability of default
LGD⫽loss-given default (also a probability)
EAD⫽exposure at default (the amount of money involved in the exposure).
In a credit risk distribution, this area of expected losses lies on the left side, as shown in Figure 5.4. By contrast, unexpected losses (UL) are found at the long leg (right side) of the credit risk distribution. The algorithm is:
UL⫽SPD⫻SLGD⫻SEAD (5.3)
where:
SPD⫽stress probability of default
SLGD⫽stress loss-given default (also a probability) SEAD⫽stress exposure at default (expressed in money).
Banks evaluate credit risk of the non-stress type through a credit request and approval process, ongoing credit and counterparty monitoring, and a credit qual- ity review process. Common practice demands that experienced credit officers prepare credit requests and assign internal ratings based on their analysis and evaluation of the clients’ creditworthiness and type of credit transaction.
As the careful reader will recall from section 1, among well-managed banks this analysis emphasizes a forward-looking approach, concentrating on eco- nomic trends and financial fundamentals. Credit risk examiners also make use of peer analysis, industry comparisons and other quantitative tools, while the final
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rating also requires the consideration of qualitative factors relating to the coun- terparty, its industry, country and management.
In contrast to this approach, stress tests represent a risk-adjusted performance measurement. Typically, stress testing goes beyond the statistical limits confining a normal distribution (the mean plus or minus three standard deviations, x–⫾3s), aiming to foretell exposure that may materialize in connection to extreme events.
The latter might take place at:
● x–⫹5s
● x–⫹10s
● x–⫹15s.
The basic objective of developing stress credit risk measurements is to facilitate prudential management of exposure by prognosticating likelihood and impact of outliers, as well as by identifying sources that give rise to risk concentrations.
Even if such events do not happen frequently, they are plausible.
This contrasts with normal risk testing because most often normal tests are made under simplifying assumptions regarding the probability of counterparty default over a specified time interval. By contrast, a more sophisticated estimate of the counterparty’s exposure requires:
● Probability of default (PD, a percentage)
● Loss-given default (LGD, a percentage)
● Volatility of LGD estimates
● Exposure at default (EAD, expressed in money)
Chapter 5
119 Expected
losses Unexpected losses
Very high
Frequency
Medium
Lower High
Value
Figure 5.4 Expected losses and unexpected losses from one risk distribution – not two
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● Effective maturity (M, at the discretion of national supervisors), and
● The stress metrics – SPD, SLGD, SEAD.
The computation of SPD may be based either on a historical scenario by examin- ing, for instance, stress default conditions over the last 100 years (which will include the Great Depression), or through hypothetical scenarios of unlikely but plausible PD (criteria for stress tests are discussed in Chapter 7).
As Figure 5.5 shows, macroeconomic variables may significantly affect PD.
Notice the significant difference in the behaviour of PD and SPD during expan- sion, while the two curves tend to approach one another during recession. This is reasonable, since recession is a stress economic condition.
Plenty of factors, including collateral, enter into LGD volatility. A dependable approach to stress loss-given default (SLGD) should capitalize on the effect of dif- ferent levels of volatility. Ongoing research on LGD presents some interesting stat- istics on LGD volatility connected to bonds, and could be used as proxy for loans.
Just like the PD, LGD must be stress tested under hypotheses of both external and internal developments unfavourable to the firm, therefore affecting its credit- worthiness. A similar statement is valid in connection to stress exposure at default (SEAD). It is appropriate to notice that credit institutions continue it find it diffi- cult to aggregate risk across sectors, as they are confronted by some of the chal- lenges faced in any aggregation, such as:
● Intra-group exposures, and
● Heterogeneous risk types.
One problem is that common definitions, let alone metrics, for risk concentrations across risk types are not currently available. Neither is there a generally accepted definition of risk concentration per se. Moreover, the amplitude and scope of the concentrations, and of the domain of exposure defined by them, have widened in recent years to include large commitments to one obligor, product, region or industry – as well as multiple exposures of different member firms of the same conglomerate.
Even more challenges than those presented with PD and LGD confront stress testing EAD. This practice focuses on how the relationship between lender and borrower evolves under adverse business conditions. To a substantial extent, EAD is influenced by decisions and commitments made by the credit institution before default. These are taken as basically depending on:
● Type of loan, and
● Type of borrower.
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This fairly widespread approach is technically correct but incomplete, because it does not account for the impact of novel instruments, from securitization to other derivatives that alter the classical way of treating loans (Chapter 6), and tends to stray from the typical notion of a borrower.
Because all loan transactions have associated with them a number of charac- teristics qualifying the credit given to a client, there exist several variables affecting EAD. Apart from type of loan and type of borrower, these include obligor-specific references, current use of loan commitments, covenants attached to the loan, time to maturity, fixed versus floating interest rate, revolving versus non-revolving credit, conditions in the case of restructuring, and the obligor’s alternative ways and means of financing.
Notes
1 D.N. Chorafas, Management Risk: The Bottleneck is at the Top of the Bottle. Macmillan/Palgrave, London, 2004.
2 http://www.bloomberg.com/apps/news (accessed 10 February 2006).
3 During the 1912 investigation of the Money Trust by the House Banking and Currency Committee, whose special legal counsel was Untermyer.
Chapter 5
121 0%
Probability of default
55%
50%
45%
40%
35%
30%
25%
20%
15%
10%
5%
Time
Expansion Recession
Macroeconomic variables*
Stressed PD
Unstressed PD
Figure 5.5 Unstressed and stressed probability of default, over time. * GDP growth, or downturn, exchange rates, market psychology, etc. (Based on a study by Basel Committee on Banking Supervision, Working Paper 14, ‘Studies on the Validation of Internal Rating Systems’.
BIS, Basel, February 2005)
4 Bryan Burrough, Vendetta: American Express and the Smearing of Edmond Safra. Harper Collins, New York, 1992.
5 Even when he was President of Chase Manhattan, David Rockefeller was spending two-thirds of his time around the world meeting clients, and he had in his New York office a database with 10 000 of the most important names – other senior bankers, large clients, heads of state, prime ministers and ministers of finance.
6 D.N. Chorafas, Modelling the Survival of Financial and Industrial Enterprises: Advantages, Challenges, and Problems with the Internal Rating-Based (IRB) Method. Palgrave/Macmillan, London, 2002.
7 Financial Products, Issue 109, 11 March 1999.
8 European Central Bank, Annual Report 2002.
9 D.N. Chorafas, The Management of Bond Investments and Trading of Debt. Butterworth- Heinemann, London, 2005.
10 Findings of the Research Task Force, Basel Committee on Banking Supervision, Working Paper No. 15, ‘Studies on Credit Risk Concentration’. BIS, Basel, November 2006.
11 D.N. Chorafas, Economic Capital Allocation with Basel II: Cost and Benefit Analysis. Butterworth- Heinemann, London, 2004.
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