STRESS TESTING
Stress testing is a form of testing that is used to determine the stability of a given system or entity. It involves testing beyond normal operational capacity, often to a breaking point, in order to observe the results. Stress testing may have a more specific meaning in certain industries.
Main article: stress testing (software)
In software testing, stress test refers to tests that put a greater emphasis on robustness, availability, and error handling under a heavy load, rather than on what would be considered correct behavior under normal circumstances. In particular, the goals of such tests may be to ensure the software doesn't crash in conditions of insufficient computational resources (such as memory or disk space), unusually high concurrency, or denial of service attacks.
Examples:
§ A web server may be stress tested using scripts, bots, and various denial of service tools to observe the performance of a web site during peak loads.
When modifying the operating parameters of a CPU, such as in overclocking, underclocking, overvolting, and undervolting, it may be necessary to verify if the new parameters (usually CPU core voltage and frequency) are suitable for heavy CPU loads. This is done by running a CPU-intensive program (usually Prime95) for a long time, to see if the computer hangs or crashes. CPU stress testing is also referred to as torture testing. Software that is suitable for torture testing should typically run instructions that utilise the entire chip rather than only a few of its units.
§ A cardiac stress test is used most commonly to detect marked imbalances in blood flow to the heart muscle.
§ In obstetrics, both a stress test and a nonstress test are forms of cardiotocography.
See also: bank stress tests
Instead of doing financial projection on a "best estimate" basis, a company may do stress testing where they look at how robust a financial instrument is in certain crashes, a form of scenario analysis. They may test the instrument under, for example, the following stresses:
§ What happens if the market crashes by more than x% this year?
§ What happens if interest rates go up by at least y%?
§ What if half the instruments in the portfolio terminate their contacts in the fifth year?
§ What happens if oil prices rise by 200%?
This type of analysis has become increasingly widespread, and has been taken up by various governmental bodies (such as the FSA in the UK) as a regulatory requirement on certain financial institutions to ensure adequate capital allocation levels to cover potential losses incurred during extreme, but plausible, events. This emphasis on adequate, risk adjusted determination of capital has been further enhanced by modifications to banking regulations such as Basel II. Stress testing models typically allow not only the testing of individual stressors, but also combinations of different events. There is also usually the ability to test the current exposure to a known historical scenario (such as the Russian debt defaultin 1998 or 9/11 attacks) to ensure the liquidity of the institution.
Stress testing reveals how well a portfolio is positioned in the event forecasts prove true. Stress testing also lends insight into a portfolio's vulnerabilities. Though extreme events are never certain, studying their performance implications strengthens understanding.
Stress testing defines a scenario and uses a specific algorithm to determine the expected impact on a portfolio's return should such a scenario occur. There are three types of scenarios:
§ Extreme event: hypothesize the portfolio's return given the recurrence of a historical event. Current positions and risk exposures are combined with the historical factor returns.
§ Risk factor shock: shock any factor in the chosen risk model by a user-specified amount. The factor exposures remain unchanged, while the covariance matrix is used to adjust the factor returns based on their correlation with the shocked factor.
§ External factor shock: instead of a risk factor, shock any index, macro-economic series (e.g., oil prices), or custom series (e.g., exchange rates). Using regression analysis, new factor returns are estimated as a result of the shock.
In an exponentially weighted stress test, historical periods more like the defined scenario receive a more significant weighting in the predicted outcome. The defined decay rate lets the tester manipulate the relative importance of the most similar periods. In the standard stress test, each period is equally weighted.