Tag Archives: black swan

A Riskier World?

This year’s Davos gathering is likely to be dominated by Donald Trump’s presence. I look forward to seeing him barge past other political and industry leaders to get his prime photo opportunity. As US equity markets continue to make all time highs in an unrelentingly fashion, it is scary to see the melt-up market been cheered on by the vivacious talking heads.

Ahead of Davos, the latest World Economic Forum report on global risks was released today. 59% of the contributors to the annual global risks survey point to an increase in risks in 2018, with environmental and cybersecurity risks continuing their trend of growing prominence, as can be seen below.

click to enlarge

Undoubtedly, environmental risks are the biggest generational challenge we face and it is hard to argue with the statement that “we have been pushing our planet to the brink and the damage is becoming increasingly clear“. That said, what is also striking about these assessments (and its important to remember that they are not predictions) is how the economic risks (light blue squares) have, in the opinion of the contributors, receded as top risks in recent years. The report does state that although the “headline economic indicators suggest the world is finally getting back on track after the global crisis that erupted 10 years ago” there is “continuing underlying concerns”.  Amongst these concerns, the report highlights “potentially unsustainable asset prices, with the world now eight years into a bull run; elevated indebtedness, particularly in China; and continuing strains in the global financial system”.

A short article in the report entitled “Cognitive Bias and Risk Management” by Michele Wucker caught my attention. The article included the following:

Risk management starts with identifying and estimating the probability and impact of a given threat. We can then decide whether a risk falls within our tolerance limits and how to react to reduce the risk or at least our exposure to it. Time and again, however, individuals and organizations stumble during this process—for example, failing to respond to obvious but neglected high-impact “grey rhino” risks while scrambling to identify “black swan” events that, by definition, are not predictable.


One of the most pervasive cognitive blinders is the availability bias, which leads decision-makers to rely on examples and evidence that come immediately to mind. This draws people’s attention to emotionally salient events ahead of objectively more likely and impactful events.

I do wonder about cognitive blinders and grey rhinos for the year ahead.

Confounding correlation

Nassim Nicholas Taleb, the dark knight or rather the black swan himself, said that “anything that relies on correlation is charlatanism”.  I am currently reading the excellent “The signal and the noise” by Nate Silver. In Chapter 1 of the book he has a nice piece on CDOs as an example of a “catastrophic failure of prediction” where he points to certain CDO AAA tranches which were rated on an assumption of a 0.12% default rate and which eventually resulted in an actual rate of 28%, an error factor of over 200 times!.

Silver cites a simplified CDO example of 5 risks used by his friend Anil Kashyap in the University of Chicago to demonstrate the difference in default rate if the 5 risks are assumed to be totally independent and dependent.  It got me thinking as to how such a simplified example could illustrate the impact of applied correlation assumptions. Correlation between core variables are critical to many financial models and are commonly used in most credit models and will be a core feature in insurance internal models (which under Solvency II will be used to calculate a firms own regulatory solvency requirements).

So I set up a simple model (all of my models are generally so) of 5 risks and looked at the impact of varying correlation from 100% to 0% (i.e. totally dependent to independent) between each risk. The model assumes a 20% probability of default for each risk and the results, based upon 250,000 simulations, are presented in the graph below. What it does show is that even at a high level of correlation (e.g. 90%) the impact is considerable.

click to enlarge5 risk pool with correlations from 100% to 0%

The graph below shows the default probabilities as a percentage of the totally dependent levels (i.e 20% for each of the 5 risks). In effect it shows the level of diversification that will result from varying correlation from 0% to 100%. It underlines how misestimating correlation can confound model results.

click to enlargeDefault probabilities & correlations