Over the past year, we’ve spent an enormous amount of time discussing the “hierarchy of vulnerability” and, relatedly, volatility sequencing across assets.
One of the defining features of the volatility landscape is the extent to which rates vol. has remained the outlier, staying anchored despite periodic bouts of turmoil in equities, credit and crude (for instance).
The last time we broached this subject was – checks notes – two weeks ago exactly, when we (again) illustrated how rates vol. has stayed “well-behaved” using the following two-pane visual, which shows vol. ratios for equities vs. rates – on top – and crude vs. rates – on the bottom.
The spikes are indicative of periods when volatility in equities (top pane) and crude (bottom pane) has surged while 3m10y has remained subdued.
What accounts for that? Why is rates vol. a persistent outlier? Well, central bank accommodation and forward guidance, obviously.
As BofAML wrote earlier this month, “no other measure best captures the cumulative effect of QE and the effect of forward guidance [as] the former accumulates assets and the latter anchors uncertainty about the future path of funding costs and the potential risk of re-pricing across the fixed income world.”
Read more on rates vol as an outlier
Well, in a sweeping 61-page note dated Tuesday, SocGen takes a look at differences in cross-asset volatility with an eye towards explaining the sequencing of spikes in a historical context. Here’s the setup:
Credit spreads continued to widen throughout 2018, with US investment grade spreads having doubled from the lows. Crude oil volatility reached 60% for the first time since 2016 when spot prices had briefly fallen below $30/b. However, some assets do not seem to have received the memo yet – rates volatility is within touching distance of its all-time lows, while gold and fx volatilities are low and unexciting. So why is volatility exploding in some assets while conveying a sense of calm in others? Is this behaviour normal? Has this happened before, or is this time different?
To call SocGen’s note “ambitious” would be to grossly understate the case. The bank goes to great lengths to expand on the mechanics behind cross-asset volatility linkages, and trying to do the whole study justice in a short post that’s amenable to a quick read is impossible. So, we’ll just hit the high points from the opening section.
The bank uses the Markov switching model and, unsurprisingly, finds that “the sensitivity of volatility to various phases of the business or monetary policy cycles” is the “common factor” that binds. In other words, inflation and rates are critical variables when it comes to explaining volatility regime change. Here’s a visual:
What you want to note about that chart is that US monetary policy uncertainty has been very subdued post-crisis, underscores the effectiveness of forward guidance and QE in setting expectations and thereby underpinning the low vol. regime.
Of course it’s not that simple. As SocGen goes on to note, “there are plenty of differences in the reaction function of these asset class volatilities to the same input parameters [and] these differences generally manifest themselves in a temporal asynchronisation in regime changes across the business cycles.”
That sounds complicated, but it’s just another way of saying what we said above about the sequencing of volatility spikes. As it turns out, that sequencing usually sees equity volatility spike first.
SocGen reiterates that rates vol. usually lags, with the post-crisis environment being particularly conspicuous in that regard.
“Bond yields, on the other hand, do not see a pick-up in volatility until much later”, the bank writes, adding that the conspicuous absence of rates volatility in the current cycle “gives us a sense of the extraordinary impact the unconventional monetary policy conducted by central banks (including forward guidance) has had on sovereign bonds.”
Do note that the Lehman crisis was an exception, which is intuitive/self-evident. “The current ratio between VIX and 3M10Y rates gamma is significantly higher than in the early 2008, after all, the unfolding of the subprime crisis was the rates-driven event”, Deutsche Bank wrote last Friday (the following is the same chart as that shown in the top pane of the first set of visuals above, only extended to capture a longer time frame).
Ultimately, SocGen delivers the following “basic rule of thumb” with regard to sequencing:
If we plot the average sequencing of high volatility regimes across the last three business cycles for all the above assets, we find that the sequence of entry is Equities->Commodities->FX- >Rates. If we are indeed moving towards a recession in the US, we could soon start to see a pick-up in FX and eventually in rates.
Maybe, but remember, monetary policymakers have been extraordinarily successful at anchoring rates volatility. Indeed, there’s a strong argument to be made that a spike in rates vol. is simply a non-starter. As BofAML wrote earlier this month, “there is too much debt out there to ‘mark’ vol higher.”
SocGen takes things (much) further and while I’m not going to opine on the actual value of their effort to divine the start date of the next recession based on current levels of cross-asset volatility, what I will say definitively is that it’s interesting/headline-worthy, and thereby well worth mentioning.
“We note the current probability of being in a high volatility regime for each asset class, calculate what stage of the cycle we saw these probabilities in the previous two cycles, and then calculate the implied date of recession considering the length of the current business cycle”, the bank explains, before presenting the results in the following table:
The takeaway: if you “listen” to what WTI and equities are saying, a recession is coming next spring at the earliest, and next summer at the latest. On average, the implied date of recession is July 6, 2021 (exactly – mark your calendar).
In the final section of what amounts to the intro of SocGen’s tome, the bank endeavors to establish the linkages between cross-asset volatilities. Most of those linkages are intuitive. Below, find what the bank describes as “a cheat sheet of all factors that affect the volatility of different assets… based on our observations and the results obtained from [our study].”
Again, the full note is expansive and capturing all the nuance/doing it justice here is not possible. But we wanted to present the highlights and excerpts above as part of our ongoing efforts to expand the discussion around rates vol. as the outlier in a world where central banks are keen on ensuring that the odds of a spike remain exceptionally low. This is an especially critical discussion now that forward guidance has taken the baton from rate cuts and QE when it comes to keeping rates vol. subdued.
And on that note, we’ll leave you with the following chart which shows how monetary policymakers in the US and Europe have succeeded in keeping monetary policy uncertainty suppressed, even in the face of surging political “implied vol” (as proxied by the global economic policy uncertainty index).