Back on March 19, when the world (and not just the financial world) was busy falling apart, I penned a piece called “Now We Know: The Babadook Is Real“.
The title referenced a critically acclaimed psychological thriller (“The Babadook“) which, were it not for an unnecessarily overwrought finale, would have achieved something close to cinematic perfection.
The film (ostensibly a supernatural drama) is wholly unnerving, mostly due to a lingering sense that the main character (a single mother) is not in fact haunted by a malevolent spirit, but rather simply going crazy. The tension around that suspicion is palpable from the beginning. It makes the viewer fearful for the child, and becomes almost unbearable by the end.
For years, those who warned about the perils of a liquidity-volatility-flows “doom loop” dynamic embedded in equity markets were generally seen as suffering from similar psychosis.
It wasn’t that anyone necessarily questioned the thesis — nobody doubted that the dynamics were real. It was more that most market participants were dubious that the stars would ever align in such a way that all of the dominoes would fall at once. The Jenga tower might wobble from time to time, but nobody believed it would topple.
To be sure, there were plenty of episodes which served as a warning. The events of February 2018 (when the VIX ETP complex imploded), were a case in point. With each passing episode, it became clearer that the day of reckoning was coming. It was just a matter of when.
“When” turned out to be March 2020, when market participants off all stripes were suddenly forced to come to terms with the fact that the pernicious, self-feeding loop between systematic flows, volatility, and market depth is not just some ghost story that only “seems” real episodically during fleeting bouts of technical tumult.
Rather, this post-crisis dynamic wherein everyone is, in one way or another, short vol. and thereby operating under the same risk management regime (i.e., more leverage is mechanically deployed as markets trend higher/vol. grinds lower, setting up forced de-leveraging into a ‘VaR-shock’), is not only real, but capable of collapsing markets, from equities to rates to FX and back again.
In March, that manifested in some of the most chaotic, untradeable markets ever witnessed. The panic culminated with mass de-leveraging from the risk parity “kraken”, which you can think of as the final domino.
In a sweeping new global equity volatility outlook piece, SocGen’s Vincent Cassot, Jitesh Kumar, and Gaurav Tiwari recap the setup that made it all possible, on the way to delivering a series of incisive observations, all rendered in quality prose, even as I continue to believe the bank’s gray-on-red chart color scheme could use a refresh.
The trio capture the gist of the “doom loop” dynamic with the following two points, delivered early on in their exposition:
- Selling volatility to generate yield suppresses at-the-money volatility and suppresses the vol risk premium. On the other hand, intermittent and path-dependent de-risking by volatility control/risk parity/trend following funds (as volatility rises) leads to sharp sell-offs, which keeps skew and convexity in demand and elevated.
- The increased participation of algorithmic traders has made liquidity a function of volatility itself, thereby bifurcating the liquidity environment depending on volatility. Liquidity is enhanced when volatility is low (thereby suppressing volatility further) but goes missing when volatility increases (thereby increasing volatility further). Skew and convexity therefore need to rise to compensate for the overall pressure on at-the-money volatility.
They go on to write that the effect of central banks’ unprecedented efforts to target financial conditions in a world where economic uncertainty is the most elevated in at least a century, creates a bimodal distribution.
“This is why the word ‘barbell’ has been so popular in the investment dictionary”, they note, adding that “a bimodal distribution, by definition, increases the skew and convexity of returns while suppressing the distribution probability around ‘at the money’ – overall reinforcing the same trend from other market micro-structure changes”.
The perceptive among you will note that one could have said the same thing about the interaction of the central bank “put” and elevated economic/geopolitical uncertainty at virtually any time over the post-financial crisis period.
Cassot readily admits as much. “The trends in skew and convexity on the S&P 500 have been upward even during the quiet periods of 2014-15 and 2017 when volatility was trending lower”, he writes.
The bank goes on to observe that while there have been several years during which daily moves of 1% or more were more common than they are in 2020 (1999, 2000, 2001, 2003, 2008 and 2009), you have to go all the way back to the 1930s to find a year during which the percentage of days with 3% (or more) moves was higher than this year.
SocGen adds the following color:
March 2020 saw the highest volatility level (99% over the month) for the corresponding change in spot (a drawdown of 12.5%). It is remarkable that the largest realized monthly downside skew (in March) was followed by the second-highest realized monthly upside skew (in April). Overall, skew has also been volatile.
The full piece is some three-dozen pages long, and the above excerpts and concepts speak to what I consider to be some of the most important structural issues market participants face in the modern world.
Regular readers are apprised that I am far more interested in describing, analyzing, and documenting these structural shifts and how they interact with macro shocks (which, anecdotally anyway, are increasing in frequency), than I am in venturing into the specifics of any particular strategy.
And yet, what’s notable about these dynamics is that they represent the intersection of theory and strategy.
Selling vol. (in all its various manifestations) is a strategy, but its popularity is enhanced in a world where everyone is engaged in a desperate quest to generate yield. That desperation comes courtesy of PhD economists who, thanks to the financial crisis, the European debt crisis, and now the pandemic, have carte blanche to use the market as a guinea pig for conducting “live” tests of theories which, at least in their extreme forms, were previously confined to textbooks.
Throw in combustible geopolitics (e.g., the rise of populism, de-globalization, etc.) and you’re left with an exceptionally fascinating interplay that’s just as enthralling as it is perilous for those who get caught on the wrong side.
With that, I’ll leave you with one last excerpt from Cassot, Kumar, and Tiwari (note again that their entire outlook piece is quite extensive and the above is in no way meant to represent a “summary” version, as the original contains myriad trade recommendations and countless additional observations that are more relevant for market participants active in the volatility space than they are for general audiences).
Overall, the brave new world of volatility needs investors to be nimbler and more computationally sophisticated. Time horizons for trade ideas have to be shortened as the world continues to move from an ‘analytical solutions’ framework to one with only ‘numerical solutions’. While a lot of market-making capacity in options has long moved to algorithmic traders, we have recently heard anecdotal evidence of market makers giving up on pricing models altogether and turning to machine learning to hedge liquid derivative markets. Given that market participants with faster computational power hold an advantage over other investors, being on the right side of liquidity has slowly become one of the most important aspects of trading/investing (after all, alternate risk transfer has been a popular trend in recent years for a reason). Chris Cole likes to say that “Risk cannot be destroyed, it can only be shifted through time and redistributed in form”. Central banks have done their best to shift volatility through time (Chrono-kinesis) and investor behaviour (hunt for yield) has carried out the redistribution by flattening the second moment (volatility) at the cost of increasing the third and fourth moments (skew and convexity respectively). But if volatility has both been flattened and redistributed, we need to re-evaluate our understanding of this ubiquitous parameter and readapt all processes that depend on it in one form or another – including risk management (value at risk), performance management (Sharpe ratios) and diversification (correlations).