Beyond Risk Management:: What Are the Alternatives?
Tuesday, 13 November, 2012 Leave a comment
There really isn’t much point in me adding to this blog from Ontonix. Maybe one of these days the financial sector will overcome its “prediction addiction”. Maybe!!?
When facing large doses of uncertainty risk model-based methods of managing uncertainty don’t work. One thing is to manage an assembly line producing, for example, computer chips, another is to run a company in a globalized, turbulent, chaotic, non-stationary and shock-punctuated economy. Some things can be accomplished via mathematical modelling some cannot. Sure, you can model anything. Nobody can stop you from dreaming up an equation based on which you invest your own savings or those of other individuals. That is not the point. The point is that some models are unable to produce results that would be good enough to justify the effort of building them. Why is that? Those who have a few decades of experience building math models know that:
the most important things in a model are those it doesn’t contain
You just cannot incorporate everything into a model. It would take ages to build and, once you’re done, things will have changed, making your model outdated and less relevant. How do you build a 9/11 or fraudulent behaviour into a risk model? How do you incorporate insider trading or the sudden default of a large bank into a risk calculation of a portfolio? You cannot. It is not about the probability of these events, which, to a certain degree can be estimated. It is all about their consequences. These are extremely difficult to define and quantify. When it comes to modelling risk models are particularly difficult to build. First of all, risk is not easy to define. Risk is a reflection of a (subjective) perception of the level of regret one experiences once a hazardous situation has materialized, such as loss of life, equipment or money. However, the more individuals you ask to define risk the more "definitions" you will get. It is not easy to imagine that if something is not easy to define, it will be even more difficult to model. And once you’ve built your model there will always exist important elements or factors it doesn’t incorporate. And these tend to be dominant when things go wrong. Experience suggests that
in complex systems, critical variables are discovered by accident…
So, what are the alternatives? Start here: