We kicked off the series of Industrial-Academic Forums with the topic of Operational Risk this past Friday and Saturday. Since I knew very little about the subject, the Forum was an opportunity to learn quite a lot. But aside from my personal experience, I heard from most participants that this was a very well organized, timely and productive forum.
For lack of expertise, instead of reviewing the talks individually, I will list the things that I learn from the workshop as a whole, in no particular order, except perhaps the number of times they were mentioned in the talks.
- because banks don't make money out of operational risk (as opposed to market and credit risk, each related to core commercial activities, with a lot of money to be made by beating the competition in the corresponding markets), research in the area has been mostly driven by regulation rather than market pressure;
- as a consequence, most of the effort focus on how to calculate the exact quantity specified by Basel II, namely VaR 99.9% of operational risk losses, which are primarily modeled using LDA (loss distribution analysis);
- LDA consists of modeling the losses in each business line as compound Poisson processes (therefore modeling the frequency and the severity of the losses separately) and then bringing business line together under some dependence assumption (say copulas). The severity of losses is modeled as a heavy tail distribution, with log-normal (10,2) being everyone's darling, closely followed by Pareto and something called "g and h" (google it !);
- clever numerical methods and analytic approximations compete to produce the final shape of the loss distribution and calculate its VaR 99.9%.
- it is really really hard to estimate the parameters of such heavy-tailed distributions, and there is not nearly enough data to accurately distinguishe them. As a consequence, regulators (and just about everyone else) have their work cut out for them in trying to validate the models used by different banks. Much care is needed to ensure a "level playing field" in the process;
- VaR is a particularly boneheaded choice of risk measure in this case, especially when tying to measure the "diversification effect" (the parameter "delta" in Basel II), which can jump from positive to negative (i.e adverse diversification) by slightly changing the marginal distribution of losses, without even touching their dependence structure;
- it would be really nice to have a way to model operational risk from first principles, like a structural model in credit risk, rather than simply trying to fit (highly unstable) statistical models to (largely unavaiable) data;
- at the end of the day, research in operational risk should lead to better risk management rather than measurament. As one participant put it: "operational risk groups should be seen more like a think-tanks instead of data centre";
As I said, a wonderful learning experience.