Breaking down the silo

I often hear from risk analysts that we need to break down the risk silo and stop measuring risk in unique disciplines but such a statement without thinking begs the question: If the silo is so evil, why did we invent the structure in the first place?

In this quick posting we look at risk silos, why they exist, the problems with them and how to make them work.

Bureaucracy Banking

Banking today is viewed upon by the customers it is designed to serve as sick. 

The causal factors for the Global Financial Crisis have been debated by many a soul and some blame our economic pain on feeble regulation, others on poor credit risk origination practices or asset bubble growth in a long only market. There is actually a whole array of factors that break banking greater than these three reasons alone I can assure you, but emphasis aside, nearly everyone I speak with will put the banking sector squarely and central to the debate of our economic woes.

What has gone wrong with banking then?

Correlate your risk factors

When modelling enterprise risk outcomes, analysts need to consider the correlation between variables in their algorithms. If they don't, the potential loss estimates they generate from these calculations are likely to be extremely erroneous.

A recent linked-in discussion on the dependency, correlation, causality and mutuality of multiple risk factors has opened up an interesting debate on the subject and stimulated this blog post. Additionally, after speaking with several risk analysts on the subject of factor dependency, there also appears to be a genuine interest in putting to word how to model an aggregate level of risk which is sensitive to correlation.

In this article we review a very straight forward method for measuring correlation in risk variables and for propagating a final outcome.  We also show why the process under CAPM is flawed.