Risks that have to be aggregated largely fall into three categories: credit exposure, including counterparty risk (Chapter 5) and country risk; market risks (Chapter 7), including interest rates, currency exchange, equity price and commodity price risks;
and operational risks, including legal risk. The case study in section 2 will demon- strate how other risks tend to morph into legal risk (which is examined further in Chapter 9).
Wrong hypotheses about transacted and inventoried exposure work to the detriment of risk management. An example is the statement: ‘Derivatives do not really add risks to the financial system, and all they do is to provide the ability to identify, price and transfer risks.’ Plenty of evidence documents that time and again this sort of assumption has proved to be utterly wrong, because derivatives:
● Add enormous risks to the bank holding them, and
● Have both mass and momentum to destabilize the global financial system.
The careful reader will recall from Chapter 1 that while, in general, derivatives can be useful instruments, leveraging sees to it that they carry with them plenty of unwanted consequences. Therefore, they have to be most carefully watched.
To test how well they positioned themselves in terms of their survival, some institutions are using a method known as Time Until First Failure (TUFF). In this, a sequence of days is plotted and the outlier is the occurrence of a loss exceeding a predetermined value or limit. This method is interesting because it permits the use of techniques and mathematical models already popular from statistical qual- ity control (SQC) and reliability engineering.
As shown in Figure 4.1, a statistical quality chart has upper and lower tolerance limits, and within them upper and lower quality control limits. As long as the sam- ple measurements are kept within the quality control limits, the process is in con- trol because the engineering specifications (tolerances) are being observed.
This concept of limits applies to many financial issues as well (see the discus- sion of credit limits in Chapter 5). A chart showing the trading range is not quite the same as a statistical quality control chart, but it is one familiar to bankers and, as far as visualization is concerned, its effects are positive.
A ‘plus’ of statistical quality control charts is that they are flexible. The one shown in Figure 4.1 has been designed to track currency exchange rates. Statistical theory says that if there are three points in a row, then there is high probability a fourth one will follow in the same direction. Indeed, point P shows a bifurcation.
● If the curve had a bend upwards, then the process would have remained in control
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● Since it continued downwards, the currency concerned had to be immedi- ately supported or fall out of tolerance – as happened in the early 1990s with the British pound.
Other quality control charts are by attributes. Something either happens or it does not. These are most suited to lower impact risks, as they help to bring them in control by tracking a specific operation. An example is given in Figure 4.2.
Chapter 4
71 Upper quality control limit
x
P Time Upper tolerance
Lower quality control limit Lower tolerance
Figure 4.1 Using a statistical quality control chart to track currency exchange rates
60
40 30 20 10 70
50
Monday Tuesday Wednesday Thursday Friday
c
Upper control limit
Number of hourly adjustments
Lower control limit Figure 4.2 Quality control chart for number of defects per unit in a week on an hourly basis
An extra reward from the implementation of quality control charts by variables and by attributes is the possibility of post-mortem evaluations. In my experience, post-mortems are critical in:
● Building a risk awareness culture, and
● Providing consensus on risk control policies.
Developed in connection with the Manhattan Project during World War II and thoroughly tested for nearly 70 years in the manufacturing industry, quality con- trol charts have a significant role in the financial industry. Graphics help to con- vey to board members and senior management the seriousness of an exposure, and they also facilitate the task of:
● Setting guidelines, and
● Assuring these are enforced.1
One of the best scenario analyses that I have seen makes good use of SQC charts, within a pattern of sound risk control policies. This financial institution treats unexpected losses as a matter of volatility of expressed losses beyond a predetermined threshold. Then it charges the consolidated profit and loss statement with an amount that corresponds to the statistically derived expected losses beyond that threshold. Information is obtained from the bank’s credit portfolio:
● Loss expectation is based on assumptions about developments over the medium term, covering a full economic cycle, and
● This amount is credited in the balance sheet as credit risk reserve for unex- pected losses (UL), beyond expected loss (EL) provisions.
The annual amounts being charged fluctuate depending on the economic cycle and occurrence of actual losses beyond the aforementioned threshold. Outliers in actual losses are posted as events, an exceptional reserve that directly impacts the bank’s economic capital base.
A scenario associated with the output of this model documents for members of the bank’s executive committee, and of the board, why such amounts are necessary to assure that spikes in loss volatility can be taken care of (with the exception of catastrophic losses), and how reserves and capital needs could balance each other out over a longer time horizon, on which is based the bank’s economic plan.
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