So, what is a Black Swan?
A Black Swan is an event. An event is something that has happened, is happening, or may happen in the future. Within this context, a Black Swan is a special kind of event. It has the following three properties: it is an unpredictable outlier that has a decisive (negative or positive) impact, and its occurrence is explained after it has occurred. In this discussion, we will cover only the destruction caused by negative Black Swans (leaving the possible benefits of positive Black Swans for later).
An event is unpredictable if it cannot be calculated deterministically nor modelled stochastically (probabilistically). An outlier is an observation (event) that lies outside the overall pattern of a probability distribution. An event is considered decisive if the impact of the event is equal to or greater than the combined impact of all other events within the confidence interval of the probability distribution (being used to model such events). In technical terms, Black Swan events are tail (outlier) events in probability distributions that display kurtosis greater than three (leptokurtic); kurtosis being the measure of the frequency and size of outlier events.
Black Swans have another interesting quality: they, almost single-handedly, dictate what is going to happen in (non-physical) systems, in our lives and in the world in general. All the non-Black Swan events are thus, relatively, immaterial. Practically speaking, our lives will be decided by a few major unpredictable events, not by the (cumulative effect) of thousands of other insignificant events; whom you marry, a life-threatening accident, a sovereign default etc. will have a far higher impact on your life than the combination of day to day routine events, over a lifetime.
Similarly, the success/failure of a stock trader will be decided by the few single-day stock market crashes; not by the combined impact of the thousands of other days of stock trading. Stock price movements regularly (in fact, overwhelmingly) show kurtosis > 3 (the kurtosis for a Normal distribution); similar patterns are observed in all price movements —commodities, interest rates, exchange rates, to name a few. As an example, the impact of one single day —19 October 1987— represented 80% of the kurtosis of the past forty years in the US stock market. This event was, of course, the Black Monday Black Swan, hiding in the left tail. This was a 19.7 sigma event. It should happen approximately once in every 1.453 × 1086 years; somewhere between one vigintillion and one centillion years. To put this into perspective, scientists estimate the Big Bang occurred 13.8 billion years ago. Needless to say, as per the Normal distribution models, Black Monday should have been an impossibility; however, it happened.
The above should worry everyone (if not scare them, all together). Our stock markets, our lives, and the future of the world, in general, is decided by events that we cannot predict. Even worse, since we are bent upon explaining each of these Black Swans after the fact, our systems do not improve nor mature in the correct manner to manage future occurrences. We keep solving for the previous Black Swan, assuming the next one will be similar. If things continue like this, sooner or later, a Black Swan of unbearably high intensity could permanently knock us out at a personal level, at an economic level, or even at a global level; as an example, a massive global real estate bubble that cannot even be managed by multiple Fed interventions, a nuclear war, a plague, to name a few.
Some history: the term Black Swan was popularised, and more importantly quantified mathematically, by Nassim Nicholas Taleb in his 2007 best-selling book with the catchy title, The Black Swan. The timing of the book could not have been better; while the financial crisis of 2007–2008 proved to be a negative Black Swan for the rest of the world, it proved to be a positive Black Swan for the book. Taleb built on the research of his mentor, the late Benoit Mandelbrot and Nobel laureate Daniel Kahneman, and changed the field of quantitative finance, econometrics and risk management forever. He did so by comprehensively discrediting, once and for all, the two foundational assumptions of these fields - the Gaussian (Normal) distribution and statistical independence.
An inability to internalize the idea of Black Swans being the definitive event(s) in financial risk management is the reason the world keeps getting hit by one financial crisis after another, every decade. No one sees it coming, and afterwards, everyone runs over each other trying to explain the causes of the catastrophic event by viewing it in the rear-view mirror. This includes the global financial regulators, whose speciality is (supposed to be) in financial risk management.
If, as a group, even with a large amount of human and technology resources available to them, regulators did not see it coming last time (nor the many times before), what is to guarantee they will see it coming the next time? The next time, it may come from a different direction, completely. If an asteroid is heading towards earth, in full view of astronomers, and all of them conclude it will not hit the earth and/or they completely underestimate the potential destruction caused by the asteroid hitting the earth, then where does the problem lie (if the asteroid does hit the earth, eventually)?
It would either be an indication of the limited abilities of the astronomers and/or the limited accuracy of the models and techniques being used by them for risk management of celestial collisions. Considering the vast amount of experience of global regulators, one must assume they have the skills and experience to tackle the problems; but have just not realized what the problem happens to be. Abstract (and vague) qualitative discussions around debt, moral hazard and regulatory policy seem to be the flavor of the month, after the 2008 crisis. Such discussions, though useful, completely miss the key point.
To avoid future financial disasters, comprehensive risk management accords are rolled out, periodically, with banks spending hundreds of millions of dollars in measuring and managing credit, market and operational risks. The Basel I accord, initially issued in 1988, concentrated on minimum capital requirements. The Basel II accord, issued in 2004, concentrated on minimum capital requirements plus regulatory oversight. Yet the 2008 crisis occurred anyway. The Basel III accord, agreed in 2010 (to fill the gaps in the previous two Basel accords, after a thorough analysis of the 2008 Black Swan), has re-emphasized capital requirements and regulatory oversight; along with moving liquidity risk to a position of higher importance. Will this be enough to avoid the next financial crisis, or is it a case of solving the last problem instead of preparing for the next one?
Needless to say, a next financial Black Swan will hit, and it will not be inline with the credit, market, operational and liquidity risk models as described in the existing Basel accords. Once again, no one will see it coming. Because the real problem lies in Model risk. There isn't a single financial risk model being used by any regulator that incorporates Black Swan risk (the one and only risk that matters). In fact, the term Black Swan risk does not exist in the regulatory vocabulary. None of the key global regulators have mentioned the term Black Swan in their after the 2008 crisis memoirs. If this risk is not acknowledged (nor understood), it cannot be modelled. If the model doesn't exist, the risk is not mitigated.
Unfortunately, Black Swans, being unpredictable, cannot, by definition, be modelled. Luckily, the causes of their origin can be understood and mitigated. Hence, the next financial crisis cannot be avoided until a change in thought process occurs. The reason is simple: the current steps being taken by regulators and regulatory bodies do not address the Black Swan problem. The regulators seem to be unable (and/or unwilling) to address the problem of induction (one of the philosophical bases of Black Swans). It is imperative that regulators should regulate financial systems towards preparedness for managing Black Swan risk, moving it all the way to the top of the financial risk list; above credit, market, operational, liquidity, and the remaining laundry list of risks defined in the Basel accords. A good place to start will be for regulators to understand the qualitative concepts around Black Swans, along with the statistical errors in existing financial risk models; incorporating Black Swan risk as a key risk in the next set of Basel accords, subsequently.