Sep 07

Friday’s Movie: Operational Risk, Part 1 (2007 FRM)

by David Harper, CFA, FRM, CIPM

FRM |

opRiskMovieIntro

For members, we just uploaded a colorful 1 hour 15 minute review of operational risk. This is Part 1; it reviews learning outcomes (LO 56.1 thru 59.8). Part 2 will publish on Tuesday. (Then our Basel II tutorial on Sept 21st).

Definitions of Operational Risk

Unlike credit and market risk, the definition of operational risk is fuzzy around the edges. As illustrated by the chart below, you can think of operational risk as "almost everything that is not market or credit risk." However, some risks are excluded. Therefore: credit + market + operational risk does not equal all risks. Specifically, following the Bank for International Settlement's definition, operational risk does not include strategic risk (an amorphous risk, if ever there was one!) and does not include reputational risk (which is controversial. Some think this reputational risk should be included):

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Basel II

We have an upcoming tutorial devoted to Basel II, but as the Accord does impose a charge for operational risk, we introduce its three approaches here:

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They are:

  • Basic indicator approach (BIA): Please notice this is a classic top-down approach with attendant advantages (the essence of simplicity, gross income multiplied by 15%) and disadvantages (what does this have to do with risk? All banks, all business lines treated the same?)
  • Standardized approach (SA): A mere extension. Instead of the bank's gross income, it is business line gross income multiplied by a business line specific factor; e.g., retail banking multiplier at 12% is perceived to be less risky than commercial banking at 15%. In short, the standardized approach is a weighted average multiplier of gross income, weighted by business line.
  • Advanced measurement approach (AMA):  Many qualifying criteria (including four 'must have' elements: internal data, external data, scenario analysis, and controls/tools. I am abbreviating here, see notes for details). Within the AMA, three (sub-classes of) approaches: internal measurement (IMA), loss distribution, and scorecard. Note the IMA formula, you have already seen its cousin in credit risk. Gamma (supervisor determined) multiplied by expected loss (EL) where EL = EI x PE x LGE. Note this is the same/similar to credit risk, but we are dealing with operational losses instead of credit losses where EL = EAD x PD x LGD.

Chapter 3 of the Kalyvas' text, Integrating Market, Credit and Operational Risk (see link to one the world's more expensive books below), has an excellent summary of these approaches.

 

Top-down versus Bottom-up

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You do need to study the details, but a few key takeaways about top-down:

  • If you need an example, refer to Basel's basic indicator approach (BIA). That's top down.
  • Top down is easier and largely inferior (e.g., poor diagnostic)
  • It is prone naturally to the error of over-aggregation of risks

 

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Regarding bottom-up, selected highlights:

  • Generally superior (e.g., predictive) but difficult (e.g., complex, data intensive)
  • Some of the bottom up approaches are necessarily subjective
  • It is prone to under-aggregate risks as it builds-up from the bottom and may underestimate relationships
  • If you want to get to root causes, you need bottom-up
  • If you want to distinguish high-frequency, low-severity (HFLS) from low-frequency, high-severity (LFHS), you need bottom-up

Model risk

Finally, we review model risk, largely based on Kevin Dowd's chapter. Model risk may occupy a tiny corner of the FRM cirriculum, but at the current 'subprime moment' who would deny the centrality of model risk (something tells me it will expand in next year's cirriculum!).

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The Dowd chapter on model risk is worth reading for its wisdom on the limitations of models. Juxtapose the math-precision of the tail-adjusted normal (TAN) distribution with Dowd's caveat emptor: your model is always making assumptions. It's good to keep this in mind lest we get seduced by the fine elegance of a statistical distribution.

One of the learning outcomes in this section is about catastrophe bond ("cat bond"). In case you missed it, Michael Lewis wrote a fun piece about a cat bond trader, John Seo, in a recent (August 26th) New York Times Magazine. A physics Ph.D. and cat bond trader has permission to criticize the math:

And if there has been a theme of modern Wall Street, it's that young men with Ph.D.'s who approach money as science can cause more trouble than a hurricane. John Seo is oddly sympathetic to the complaint. He thinks that much of the academic literature about finance is nonsense, for instance. ''These academics couldn't understand the fact that they couldn't beat the markets,'' he says. ''So they just said it was efficient. And, 'Oh, by the way, here's a ton of math you don't understand.' '' He notes that smart risk-takers with no gift for theory often end up with smart solutions to taking extreme financial risk -- answers that often violate the academic theories. (''The markets are usually way ahead of the math.'') He prides himself on his ability to square book smarts with horse sense. As one of his former bosses puts it, ''John was known as the man who could price anything, and his pricing felt right to people who didn't understand his math.' - Source: In Nature's Casino - New York Times (subscription required)

Source on Basel II

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