If there were a Word of the Year award in finance it most certainly would go to volatility. It seems like nearly every article you read makes some reference to it. Is volatility gone for good or is it just in hibernation? Did central bankers forever squash volatility, repress volatility for some time, transform volatility? Did structural changes in the macroeconomic landscape create a “new normal” with respect to volatility? Is volatility building beneath the surface of the markets planning its vengeful return? Is volatility volatile? Volatility, volatility, volatility! With all this discussion of volatility, one thing seems certain: there’s a bubble in volatility, as in the word itself.
For a while, any discussion regarding volatility appeared academic in nature. Measures of volatility stood at historically low levels and no developments suggesting the contrary seemed to have any impact. People were making money shorting it (whatever that meant) while those expressing concern were relegated to the fringe of the investment industry, seeking comfort among themselves and their shared misery. It was a Goldilocks scenario.
Then, in February, all of that changed. Volatility returned like a pack of Viking raiders descending upon an English monastery after weeks at sea. And much like those feared heathens of yesteryear, it seemed to come without warning, have its way with markets for a few days, and then slipped back out under the cover of darkness. It was so swift some might be asking, “what in the world just happened? And where are my gold and goats?!” I guess people forget what happens to Goldilocks at the end of the fable.
Given all the ink spilled in the financial press on volatility, as well as the true importance of the concept, I thought it appropriate that we Integrating Investors tackle the topic. In this article I’d like to discuss what volatility is and how it impacts investing. In Part 2 we’ll examine what (I believe) the current volatility landscape looks like and what it might mean for the general direction of investment markets. Strap in, because it’s going to get complicated, fast.
Before starting off, I would like to state the following: I am by no means an expert on volatility. I do not study it, explicitly trade it, seek it out in any way, poke it with a stick, etc. For anyone looking for one, Chris Cole at Artemis Capital Management is your man. That said, I believe the approach we’ll be taking here yields a differentiated – if not nuanced – understanding of volatility that may help us better grapple with the topic. Also, please remember that this is a blog post and not an academic paper, so treat my commentary as such (i.e. as a casual examination).
But first thing’s first, giving credit where it is due. As I mentioned, I owe my understanding of the topic in a large part to Mr. Cole. He was the first person I heard connect volatility to the actual, real world. My introduction came via a podcast interview nearly a year ago, and then by reading his brilliantly penned research report entitled Volatility and the Allegory of the Prisoner’s Dilemma. I can’t recommend these resources enough; they are “must listen/reads” for anyone interested in volatility.
If you ask most people for a description of volatility (in a financial context), you’re likely to end up with some variation of the following answers: “it’s the VIX, has something to do with option prices, is a fear gauge or measure of uncertainty in the markets”, etc. An even worse response is some complicated math formula or [sigh] “a measure of one’s risk.” While all these answers are in fact in the ballpark of being correct, none actually get to a precise definition. They dance around the issue and instead cite conceptual tools or a contextualization of what volatility actually is for the purposes of investing. Not bad, really. It’s a tricky concept to nail down given its abstractness. We can do better though.
Before getting to the topic du jour, let’s review a couple of investing basics first. This may seem silly, but please bear with me. An investment is merely a pool capital that is exchanged for another one sometime in the future. An investment return reflects the incremental amount of capital you ultimately receive in excess of your initial capital stock committed. Thus, we can see why my favorite Warren Buffett quote – “the price you pay determines your rate of return” – is profoundly true. As investors, our only concern, fundamentally, is price.
The current price of a financial asset embeds lots of information with respect to what market participants expect to happen in the future. In other words, it carries some kind of return expectation; either explicitly like a bond yield, implicitly like an equity multiple, or some combination like a real estate cap rate or dividend paying stock. Prices change when investors’ expectations change. This takes the form of transactions (i.e. the actual buying and selling of assets). No transaction, no change in price, irrespective of what any model suggests “fair” or “intrinsic” value to be. Price is observable, not theoretical.
Put differently, prices change when capital is mobilized (as conducted via transactions). From this perspective, we can see that price changes merely reflect changes in capital flows.
This brings us to volatility. The Merriam-Webster dictionary defines volatility as “a tendency to change quickly and unpredictably.” What do we investors care about potentially changing “quickly and unpredictably”? That’s right, price. If prices change when transactions occur, and transactions occur when investors are motivated to rejigger their capital allocations, then we can see that volatility – that “tendency” – is essentially an expectation gap, almost analogous to electrical voltage. It’s a measure of how much the market’s view of an investment’s price could potentially change. Thus, volatility is a descriptor of potential capital flows. Simple, right?
Volatility increases when “the collective we” change our minds about the future. Maybe we were mistaken about an assumption, maybe the facts changed. Whatever the reason, capital mobilizes to reflect the change in view. This changes prices and hence closes that expectation gap between what we thought would happen prior and what we do now. Note that it’s not really possible to know what this gap is ex ante.
An environment characterized by high volatility means that investors have a wide range of views – as indicated by their capital at risk (not by those held in their heads). A diversity of expectations are being expressed in the investment markets in such a scenario. Stated in statistical terms, there’s a wide confidence interval surrounding the consensus narrative. An environment characterized by low volatility indicates the opposite; that investors feel comfortable with how they’re positioned. They are making few allocation changes.
Thus, we can see that volatility is a coincident indicator. It tells us what kind of environment we’re currently in. Volatility qua volatility is not a useful forecasting tool, though it can have great utility in a risk management context, similarly to the margin of safety concept in value investing. If volatility is low, one can position to profit from a future spike, and vice versa.
There’s nothing overly profound in any the above, yet I suspect that few have gone through the trouble of getting down to the roots of volatility’s meaning before attempting to use it, either explicitly in investing or in discussions. Why the bother, you might ask? Because we care about price (and only price), and specifically, how it might change in the future. A description of how price might change is volatility. Get it? Got it. Good!
Now that we’ve (painstakingly) examined what volatility actually is, let us move on to its relationship to investing. I would argue that all of us employ some estimation of volatility in our decision making process. This is most commonly done implicitly by coming to some general view of one’s targeted financial market, the economy, an industry, etc. However in modern times, volatility is also employed explicitly. This brings us back to the relationship between volatility and uncertainty.
It’s worth restating that volatility cannot be known in advance since it’s the gap between what we expect to happen and will in the future. The historical record of volatility is what the professionals refer to as, quite succinctly, historical volatility. However, because we investors deal with the future – what might prices be – we all are subject to what is called implied volatility, and whether we know it or not.
Implied volatility is merely the consensus guess of the market’s current state of volatility. It is determined by analyzing how investors are currently positioned and comparing that to historical outcomes. It is a tool that we can use for sure, but it is no crystal ball.
When investor sentiment is fairly consistent – as reflected by their actual positioning – it can be said that implied volatility is low. In other words, there is little variation among the expectations of market participants; the confidence interval is narrow. We humans, being social animals, take comfort in company and become emboldened in expressing our views. This can generate an overconfidence bias and lead to various behaviors such as the ignoring of contrary evidence, employing higher usages of leverage, etc., which can manifest in higher valuations. The opposite holds true with respect to periods containing high implied volatility.
Volatility plays prominently in modern financial theory as well. Practitioners have extensively studied the topic, expanding our knowledge and contributing to an entirely new vocabulary of mathematical relationships. We’ve seen an explosion in the proliferation of Greek letters throughout the financial lexicon, exotic new terms such as “risk parity“, nonlinear chart patterns, squiggly lines, and the like. The result is that implied volatility has become an attribute and incorporated directly into the risk and investment management processes.
Thus, volatility – or rather the tools we employ to try to gauge implied volatility, such as the VIX, the MOVE index, trailing averages of historical volatility and the like – is no longer just a description of price, but has become an inputto price. Certain strategies will allocate capital according to a calculated view of volatility. Typically, a rise in volatility triggers a reduction in capital at risk (and vice versa), such that a self-reflective loop can form. The volatility-induced selling has the effect of increasing implied volatility, which begets more selling, etc. Mr. Cole identified this in his latest report, which in fact played out according to script in the recent liquidation of the XIV ETF.
While the above might sound overly complicated and scary (the “algos” are coming for our money!), there is actually nothing unique to these “quant” strategies. They are merely explicitly mimicking perhaps the most common of all investor behaviors since the dawn of speculation – buying high and selling low! Investors have been buying feverishly into manias and selling hysterically into panics well before the advent of the byte. John Maynard Keynes infamously described this as animal spirits. After all, it is we humans who program the bots! That said, the dispassionate fashion in which these automated strategies act is certainly a valid concern (see Black Monday for an example).
The irony of the investment markets is that past is prologue. We investors are extremely good at projecting out in a linear fashion and quite poor at anticipating changes in trends. This appears to simply be human nature. Thus our expectations as it relates to volatility – either our implicitly held views or the implied volatility measures used by “quants” – is inextricably tied to the past. Our forecasts are serially correlated and yet history suggests that events unfold with a cyclical cadence. Hence, past is prologue.
I’d like to leave our discussion here and continue with an analysis of the volatility environment in Part 2.
A version of this article first appeared on the Integrating Investor.
Seth Levine is a professional, institutional investor focused on selecting high yield bond positions for a financial services company. He is also the creator of The Integrating Investor where he blogs about macroeconomic and investment strategy related themes. Seth holds a Bachelor of Science degree in Mechanical Engineering from Cornell University and is a CFA charterholder. You can learn more about Seth at www.integratinginvestor.com and follow him on Twitter at @SethLevine2. Please note that any opinions and views he expresses are solely his own and do not reflect those of his current or former employers.