Over the years I have tended to rail against people who claim to be risk managers but in truth are only risk measurers. This causes great problems, as whilst examining past data (data are always in the past) is very important, it poses a trap if we fail to follow through to the true risk management piece. As humans we love “solutions” so a nicely crafted statistical model with loads of data can give a false sense of control and security. The risk management piece should be to carefully question the assumptions in any model, be open to a wide range of outcomes and scenarios, and to be willing to re-examine past mistakes in the model.

The re-examination part is important, with the exception of very predictable past data sets (for example, tide tables or sunrises) pretty much everything in the future, in varying degrees, will be different to the past. Models tend to point us towards a static snapshot rather than being flexible enough to account for dynamic changes. The risk management role is to take all of this into account and as the old maxim goes “questions are often more important than answers”.

All well and good you may say, but we are still focusing mainly on the past, driving via the rear view mirror rather than peering ahead through the windscreen. This brings us to the third element that is often ignored or given too little time, the importance of risk identification. The very best risk managers are on top of the first two parts just mentioned and devote a significant amount of time and resources to the identification piece. There are a variety of techniques, scenario planning being one of the most common. Additionally, many try “horizon scanning” or searching for “faint signals” all of which makes sense.

We are all however up against a particularly difficult issue – that of Emergence. How do things start to appear or indeed seem to suddenly smash us right in the face from nowhere? Needless to say there is no nice neat answer, let alone the constantly sought “solution”. Emergence is a key component in Complexity Science and in broad terms can be defined as when an entity is observed to have properties its parts do not have on their own, properties or behaviours that emerge only when the parts interact in a wider whole.  This can be from the relatively simple, of say traffic jams or birds flocking, through to trying understand what is a living being or system. This may all seem arcane for the hard pressed risk manager, but we can draw some broad conclusions and guidelines.

In this third stage of risk identification, we must remain aware that financial markets are dynamic and frequently unpredictable. Your model can trap you into generating a series of constrained snapshots that do not allow for sudden or unexpected changes. Be aware of unlikely or unusual relationships emerging in markets – the killer one is often new patterns of liquidity and volatility. that seem well outside the norms.

A key component of Emergence is that it doesn’t obey nice linear patterns, so that using normal tools is often of no use or even downright misleading. Power laws seem to frequently drive such complicated relationships and are often referred to as scale free networks. A feature of such is the likelihood of clustering in these networks and the potential for the cascading of risks, previously uncorrelated risks that start to work closely together. The classic financial example is that when markets crash previously uncorrelated asset classes suddenly become joined at the hip. A good example of this was the initial fall in in the price of gold at the time of the LTCM crisis in 1998.

The whole topic of risk identification and in particular Emergence is now a vital part of risk management. Here are some books that look at the issues and explain Complexity and Emergence in straightforward terms:

Critical Mass – How one thing leads to another
By Philip Ball 2005

The Origin Of Wealth: Evolution, Complexity, and the Radical Remaking of Economics
By Eric Beinhocker 2007

More Than You Know: Finding Financial Wisdom In Unconventional Places
By Michael Mauboussin 2009

Escape from Model Land: How Mathematical Models Can Lead Us Astray and What We Can Do About It
by Erica Thompson 2022