Making Sense of Immanent Failure
In the recent paper from the Said School, “Beyond the Financial Crisis” the authors use the phrase “inability to make sense of immanent failure” to describe one of the aspects that lead up to the financial crisis.
That matches up well with Jared Diamond’s ideas about Why Civilizations Fail.
And perfectly describes the otherwise baffling Chuck Prince quote about dancing.
I imagine that it is a problem that is more common with people who believe that they have really done their homework. They have looked under every rock and they do not see the rock falling out of the sky. It is not that they are failures. In most times their extreme diligence will pay off handsomely. There is just one sort of time period when they will not benefit appropriately from their careful work.
That is when there is a REGIME CHANGE. Also called a SURPRISE. All of the tried and true signals are green. But the intersection is uncharacteristicly clogged.
A major task for risk managers is to look for those regime changes – those times when the risk models no longer fit and at that point to CHANGE MODELS. That is different from recalibrating the same old model. That means applying the Baysian thinking not just to the parameters of the model but to the model selection as well.
It is not a failure when a new model must be chosen. It is a normal and natural state of affairs. Changing models is what I will call “Rational Adaptability”.
The reason why it will not work to simply recalibrate the old model is that the model with combined calibration for several regimes would be too broad to give appropriate guidance in different regimes.
You ride a car on highways, a boat on water and a plane on air. Multi vehicles exist but they are never as efficient in any environment as the specialized vehicle.
So the risk manager needs to make sense of immanent failure and practice rational adaptability.
Get out of the car when you are wet up to the doors and get into a boat!Explore posts in the same categories: Assumptions, Emerging Risks, Modeling
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