Last Uncertainty Wednesday provided a recap on our adventures with sample means and what those implied about the difficulties of inference. Now we will look at another equally fascinating complication: inferring volatility. As the title of this initial post gives away, we will see that it is easy to make large inference errors when we are dealing with situations in which volatility is somehow suppressed. It turns out such situations are all around us all the time. Let’s work our way into this one step at a time.
First of all, what is volatility? Here is a nice definition, courtesy of Wiktionary: “A quantification of the degree of uncertainty [about the future price of a commodity, share, or other financial product.]” I put the second half in brackets because while volatility is commonly used for financial assets, it could be about something else such as the level of employment in the economy. We have encountered several quantifications of the degree of uncertainty along the way, most notably entropy and variance.
What then might suppressed volatility be? Well if we are fragile, then increased volatility hurts us. So we tend to dislike volatility and look for ways of reducing it. Important aside: if we are “antifragile” then we benefit from increased volatility. The tricky part is that often the measures we take to reduce volatility wind up simply suppressing it. By that I mean it looks, for a while, as if volatility had been reduced but then it comes roaring back. The ways in which attempts to reduce volatility can backfire are among Nassim Taleb’s favorite topics.
The securitization of mortgages provides a great example of suppressed volatility. The basic idea is simple: throw a bunch of mortgages into a pool. Then carve the pool up into tranches of different volatility. Some with presumably very low volatility that looks like triple AAA rated bonds and others with high volatility like equity. It should be easy to infer from this description that total volatility has not been reduced it has just been parceled out.
So why am I calling this an example of suppressed volatility? Well, securitization of mortgages worked fantastically well for several decades. But as it did, people started to mistake the lower volatility of the bond tranches with lower volatility of real estate overall. And that meant more and more money started piling into real estate and as that happened banks got greedy. They underwrote more and more bad mortgage risks, making the pools increasingly risky. And yet for a while, because of securitization, it continued to look as if the the bond tranches had low volatility.
So what started out as a legitimate way of allocating volatility across different investors, turned into a case of massively increased and suppressed volatility that exploded in the 2007 financial crisis which has become known as the Great Recession.
Next Wednesday we will start to develop a simple model that lets us study suppressed volatility and see why it is so hard to detect. In general the take away will be that we should always be questioning anything that looks like a magical reduction in volatility. Most of the time it will be a case of suppressed volatility instead. In that regard the current super low volatility in financial markets, which has become known as the volatility paradox, should we worrisome for investors.
