Jon Danielsson and Robert Macrae on how to think about risk models.
Uncertainties in risk models
The authors point to several reasons why risk models are uncertain:
- The model estimation period is too short
- There are structural breaks during the estimation period
- Data snooping and model optimisation occur
- Portfolios are optimised, maximising errors
- It is often necessary to forecast extreme risks
Uses of risk models
They also point to the main uses of risk models:
- Routine understanding of risk by banks and supervisors
- Routine management and control of risk by banks and trading desks
- Analysis of systemic and regulatory risk
- Management and control of systemic risk by supervisors
I think the most important sentence in the piece is:
This suggests that models used to constrain risk should be substantially simpler than models used to understand risk.
A summary of their take on systemic risk is:
Ultimately, the difficulty of the systemic risk problem suggests that supervisors working on systemic risk should be wary of statistical models of extreme market outcomes.
Finally, quite a nice analogy:
Medieval mapmakers often noted the risk of an unknown kind by the notation “here be dragons”. Attempts at controlling extreme risk should come with a similar warning. Just like the sailors of yesteryear, financial institutions will go into unknown territories and, just like the map makers of that era, modern risk modellers have very little to say.