Risk Update: July 2021 – Transitory gets the Orwell treatment.

“Not merely the validity of experience, but the very existence of external reality was tacitly denied by their philosophy. The heresy of heresies was common sense.” George Orwell’s 1984.

Once again, we feel obliged to throw in a quote from Orwell’s 1984. We might go so far as to make the claim that Orwell, way back in 1949 when he wrote 1984, did a better job of forecasting what a July 2021 Federal Reserve press conference would sound like, than the Federal Reserve did in forecasting changes in their chosen price index from just one month previous.

In classic Orwellian tradition, Chair Powell, who has been at the forefront of the steadfast claims that recent price rises are “transitory”, was seemingly cornered into redefining the meaning of the word. A simple googling of the word transitory produces a very simple, two-word, definition: “not permanent”. Chair Powell, however, gave these explanations:

“The concept of transitory is really this. It is that the increases will happen. We’re not saying they will reverse. That’s not what transitory means. What it really means is temporary, but then you got to understand that it doesn’t mean that the increases will be taken back.”

So, in other words, permanent?

In case you were wondering, googling the word temporary gets you this definition: “lasting for only a limited period of time, not permanent”.

“It’s a beautiful thing, the destruction of words”. George Orwell’s 1984.

You do not have to dig too deeply through the transcript of Chair Powell’s July 28th press conference to find any number of gems.


“Indicators of long-term inflation expectations appear broadly consistent with our longer-run inflation goal of 2 percent. If we saw signs that the path of inflation or longer-term inflation expectations were moving materially and persistently beyond levels consistent with our goal, we’d be prepared to adjust the stance of policy.”

Figure 1: US CPI Index. CAGR 30yr 2.33%, 20yr 2.16%, 5yr 2.54%

Source: Bloomberg

“As you know, with maximum employment—unlike with price stability, where we can target a number of 2 percent on average—with maximum employment, there isn’t a single number that we can target.”

Figure 2: US CPI 5.4% (white), PCE Core 3.54% (red), UIG Prices 3.56% (green) Y-o-Y% Change

Source: Bloomberg

“So inflation is running well above our 2 percent objective and has been for a few months and is expected to run up certainly above our objective for a few months before we believe it’ll, it’ll move back down toward our objective.”

“If we were to see inflation moving up to levels persistently that were significantly materially above our goal and particularly if inflation expectations were to move up, we would use our tools to guide inflation back down to 2 per cent,”

Figure 3: University of Michigan Inflation Expectations: 1yr Expected Change

Source: Bloomberg

“If you look at the most recent inflation report, what you see is that it came in significantly higher than expected. But essentially all of the overshoot can be tied to a handful of categories. It isn’t the kind of inflation that’s spread broadly across the economy.”

Figure 4: Bureau of Labor Statistics (source) CPI by Category

Only the two items, highlighted with the red arrows, are below the magical 2% target, everything else is above 2%. It is what an unbiased commentator might refer to as “spread broadly across the economy”.

Of note might be the Shelter sub-category that has risen Y-o-Y at a 2.8% clip, while various measures of home prices are rising at annual rates in the 18% plus region. If what is going on in the real world of home price inflation starts to feed through to the “Shelter” and “Owner’s Equivalent Rent” components of the various chosen measures of inflation, we may get yet further evolutions of the meanings of transitory and temporary.

Figure 5: US CPI Shelter (white) vs Shiller Home Price Index (blue)

Source: Bloomberg

In amongst all the gibberish and misrepresentation of what is or is not price stability, the Fed did announce something of relevance. That being, as Chair Powell put it:

“…the establishment of two standing repo facilities, a domestic one for primary dealers and additional banks, and another for foreign and international monetary authorities. These facilities will serve as backstops in money markets to support the effective implementation of monetary policy and smooth market function.”

This is a culmination of work that has been going on since the Fed’s interventions in the repo market in September 2019. We have tracked the ongoing developments of this major expansion of Fed sponsored Moral Hazard In a number of past Monthly Updates:


In line with the Fed’s announcement, the G30 Working Group on Treasury Market Liquidity released their report “US Treasury Markets. Steps Towards Increased Resilience”. The working group was chaired by Timothy Geithner and included such other distinguished former senior Titanic officers as Bill Dudley, Mervyn King and Larry Summers.

It is not a particularly good read, but one of those things that has to be done to believe they have actually done what they have done. The gist is, the Fed and other central banks had to intervene across all markets, particularly the US Treasury markets, to maintain market functioning: “market functioning could only be restored by massive purchases of Treasuries by the Federal Reserve”. They manage, throughout their lengthy explanations of what the underlying problems are, to miss the true issue of what we would term “Uncapitalized Tails”. Their principal recommended solution is the rollout of precisely what the Fed has done, the Standing Repo Facility (SRF). Interestingly, having never mentioned system leverage in their list of causes and never touched on it in their 10 Recommendations, they do give it a nod in a comment that their recommendations, if implemented, would not eliminate the future need for the Fed to save the market again in the future.

“They will not eliminate the likelihood of large-scale Fed purchases of Treasury securities or Federal Reserve support for other parts of the US financial system in the most extreme circumstances. They do not address the many other challenges that come from the extent of leverage and maturity mismatches in parts of the nonbank financial system in the United States.”

They go on to touch upon the rather obvious moral hazard implications of the SRF saying “An important concern with respect to creation of an SRF is the potential for it to create moral hazard that would increase systemic risk by encouraging entities with access to the facility to become more highly leveraged.” They then, in a wonderful twist of logic, manage to justify the increased moral hazard of the SRF by claiming that it would reduce the need for stressed selling of Treasuries in an event, therefore reducing the need for Fed direct intervention through Treasury purchases, which would contribute even more to moral hazard than the SRF. Try thinking that one through while balancing on one leg! All in, levered carry NBFIs could not have gotten a better early Christmas present and the rest of us need to consider the implications of the extreme market support measures becoming non-transitory features of the market. Where were these guys when LTCM needed them!

What has it all wrought? Just ever great fragility.

Bank of America’s Michael Hartnett recently shared some lovely visualizations of fragility.

Figure 6: Financial Assets to Global GDP

Source: BofA Global Investment Strategy, BIS, IMF

Figure 7: US Private Sector Financial Assets to GDP

Source: BofA Global Investment Strategy, Haver; note private sector financial assets includes currency, deposits, equity shares, and other security and does not include real estate.

Those two pictures do a pretty good job of answering the question of “where did all the inflation go”! They also make it imminently clear what is behind issues related to wealth segregation. Another thing that jumps out is that, for the better part of 12 years and increasingly so of late, the pain has been to the upside. What has hurt investment portfolios has been lack of exposure to the upside, over allocating to investments that have not participated in the inflation of financial assets. What we like to call “defensive midfielders”.

We can simplify it even further and straightforwardly ask the question, is the below line subject to Stein’s Law?

Figure 8: US All Sectors Debt

Source: St Louis Federal Reserve

One argument that could be made is that an increasing amount of this debt is being held by central banks that are not subject to the type of risk management prudence that ordinary investors would face, whether they be fiduciaries or principals. Therefore, potentially central banks could endlessly support the continued exponential growth of debt but, we would argue, just because they have no skin-in-the-game from a capital perspective, it doesn’t mean they don’t have some perceptions of risk. If pressed, they might describe that as their “third mandate”, that being financial stability.

Last month we used the Wu Xia Shadow Rate as a proxy for Fed policy setting and the Taylor Rule as a proxy for economic circumstances, https://convex-strategies.com/2021/07/23/risk-update-june-2021/.

Figure 9: US Taylor Rule (yellow) vs Wu Xia Shadow Rate (red) – 50yr view

Source: Bloomberg

Our friends at SocGen, coincidently, created a very similar view using a calculation of Real Policy Rate and the Output Gap (%GDP).

Figure 10: US Real Policy Rate vs Output Gap (%GDP)

Source: SG Cross Asset Research/Derivatives

As we did with our divergence between policy setting and economic circumstance, they too pointed out that these sorts of extremes have not been seen since 1973. Any mention of 1973 gets us thinking about Arthur Burns (think of him as the original transitory prices guy https://convex-strategies.com/2021/04/16/risk-update-march-2021/ ). We can isolate Chair Burns’ tenure and see how a good old fashioned 60/40 Balanced Portfolio would have performed post 1973. The circa 30% drawdown from Jan ’73 to Oct ’74 would not seem to indicate that “hot” economies, with an overly accommodative policy setting, are overly conducive to this type of portfolio.

Figure 11: Compounded Performance Line of a 60/40 Balanced Portfolio

Source: Bloomberg, Convex Strategies

We can play around with this and add how a hypothetical Risk Parity portfolio might have performed. We will give this one a bit of the old investment banking leeway and use a leverage assumption more attuned with current modern day market levels, ie. we use a leverage factor of 4.3x. We have also extended the time horizon into 1980 to show some of the impact from Chair Miller’s stint in the chair.

Figure 12: 60/40 Balanced (blue) and Hypothetical S&P Risk Parity 10Vol (gold) 1973-1980

Source: Bloomberg, Convex Strategies

Point being, leverage and reliance on negative correlation between stocks and bonds, may not be a great idea post a period of unprecedented economic heat paired with the mistake of overly accommodative policy. Post the 1973 extremes, our hypothetical juiced Risk Parity portfolio gets a nearly 55% drawdown. Tough to stomach.

Tail Risk guru, Mark Spitznagel, penned a recent op-ed in the Financial Times yet again pointing out the obvious inefficacy of traditional risk mitigating strategies such as fixed income and hedge fund strategies. We think his arrows analogy slightly misses the mark, but his overall point about omission is spot on.


As all are aware, a big part of the solution to this lack of efficacy has been to add leverage to the supposedly risk mitigating strategies, maybe most renowned in various forms of the above-mentioned Risk Parity type of “vol optimizing” strategies. As Mr. Spitznagel notes “An overallocation to bonds and other risk mitigation strategies is the principal reason why public pensions remain underfunded today — an average funding ratio in the US of around 75 per cent — despite the greatest stock market bull run in history.” This is exacerbated by leverage and comes down to our rote mantra – “it’s just math”. As we so often discuss, it comes down to convexity and the issue of ergodic vs non-ergodic.

We can quickly game this up with our Coin Toss Monte Carlo simulator by simply playing the game where a winning toss earns 25% return on capital, while a losing toss costs 20% destruction of capital. With a fair coin, the ensemble average (ergodic-based) expected return of this game is +2.5%, but of course on a compounded time-average basis (non-ergodic reality) we know that the median return of participants is simply breakeven. The average terminal capital in this particular run (remember the average can fluctuate dramatically as it is a very fat tailed distribution with only the lucky few big winners driving the parameter) works out to $100 initial capital growing to $312 (not far off the compounding expectation of the 2.5% per toss expected return), while the median works out to terminal capital of $100, ie. 0% returns over the 40 tosses.

Figure 13: Coin Toss Simulation: +25% vs -20%. 200 Players. 40 Tosses

Source: Convex Strategies

Not surprisingly, particularly in the fiduciary world that advocates as though the world is ergodic and targets arithmetic return objectives, there is a propensity to look at the 2.5% expected return and propose applying “leverage” to it. Very simply, we can 2x the position with leverage, doubling both the upside and downside to a +50% versus a -40%, and see that our expected return doubles to 5%! But as you are all aware, this is not a win! Running the simulation again with +50% and -40%, we see that the median terminal capital has now dropped to just $12. Almost every player is losing money, and many are finding the absorbing barrier (ie. insolvency). Again, the average terminal value is $562 in this run, similar to before and not far off from the compounded expectation at the per toss expected return of 5%. However, you can clearly see that virtually all of that is contributed by the few very lucky winners – call it the Jeff Bezos effect.

Figure 14: Coin Toss Simulation: +50% vs -40%. 200 Players. 40 Tosses

Source: Convex Strategies

In the above, we have assumed no cost for the additional levered turn of playing the game, and it still takes you to a median terminal value of $12. If we charge something for the leverage, in the below simulation we assume a 2% per toss cost on the additional turn of leverage, logically the results get even more dire. Now the median terminal capital is a mere $3, nearly everybody is going broke, with the exception of the lucky very few (looks like roughly 2 players) that manage to bring the average terminal capital up to $236 (and their fiduciary managers that get a piece of the average upside, but don’t share in the far more common median downside, as well as the providers of the leverage). A compounded return, on average, of circa 2.17% per toss.

Figure 15: Coin Toss Simulation: +50% vs -40%. 200 Players. 40 Tosses. 2% Leverage Cost

Source: Convex Strategies

Regular readers will know where this is going. Instead of spending money to lever poor portfolio strategies, invest the money to add convexity to your portfolio. Going back to the original risk portfolio of +25% vs -20% and investing in convexity/insurance, the same 2% per toss that the previous example spent on leverage, to cut off losses after three consecutive losing tosses, generates this sort of simulation. This one has a median terminal capital of $145, average terminal capital of $273, and nobody going bankrupt. This, we would argue, is the probable path to superior compounded returns. Figure out how to add protection on the downside, then go and take more upside risk.

Figure 16: Coin Toss Simulation: +25% vs -20%. 200 Players. 40 Tosses. 2% Insurance Cost

Source: Convex Strategies

In the real world, when we compare portfolios with embedded convexity to those without, as we have so often discussed, we can clearly see that it is the performance in the wings that differentiates the compounding. Going back to 2009, the beginning of the current epic bull market in financial assets of all types, we can compare our hypothetical Always Good Weather (AGW) portfolio (40% SPXT/40% XNDX/40% CBOE Long Vol) with a standard Risk Parity portfolio (S&P Risk Parity Target 10%Vol).

In the Scattergram View (the x-axis shows the S&P monthly returns and the y-axis is the given portfolio returns in that month) we see very clearly that the AGW separates itself in the extremes. Note the red arrows that show performance in the two most extreme months of S&P performance in the series, March 2020 (down) and April 2020 (up). We have added to the right the skewed normal distributions of the respective strategy returns. The convex AGW strategy has skewed upside returns. The levered, relying on historical correlations, benefiting from a near perfect historical period of outcomes Risk Parity strategy, has skewed downside returns.

Figure 17: Always Good Weather (40/40/40) (blue) vs S&P Risk Parity 10Vol (red): Scattergram View

Source: Convex Strategies

Rolling those into their respective compounding lines, shows the same again. Over the bull market period, AGW and Risk Parity show CAGRs of circa 17.8% and 11.9%, respectively. The convex AGW portfolio separates from the non-convex Risk Parity during times of volatility. In calendar year 2020, the respective returns for AGW and Risk Parity were 37.8% and 11.5%. Simply put, AGW exhibits better protection to the downside tail and more participation to the upside. As we like to say: less risk, more return.

Figure 18: Always Good Weather (40/40/40) vs S&P Risk Parity 10Vol: Compounded View. Yearly Rebalance.

Source: Convex Strategies

Another view of the same issue is the ratio of the yearly NAVs of the two investment strategies. The convex AGW is on the verge of reaching 2x the compounded capital value of the Risk Parity strategy. Again, worth noting, this is over the best imaginable period for the Risk Parity strategy with equity and bond valuations both surging to all-time high valuations. Yet, that levered pursuit of returns, aided by highly cooperative central bank driven correlations, still leaves purveyors significantly behind a hypothetical convex alternative. What are they to do from here should they want to catch up? Take more risk? Add more leverage? More of the same at ever more extreme valuations is unlikely to improve performance in the wings. Non-convex strategies will perpetually underperform in the wings and continually fall behind when big numbers materialize.

Figure 19: Always Good Weather (40/40/40) vs S&P Risk Parity 10Vol: Ratio of NAV from March 2009

Source: Convex Strategies

Bit of a Catch-22. Risk Parity needs to make big numbers to catch up, moderate outperformance in average months will not fill the gap, but it always performs relatively its worst in the big numbers.

The challenges for investment portfolios are far from being a secret. The best and the brightest are well aware. For example, GIC, in their annual announcement on 20yr returns, comment on their expectations for even lower returns over the next 5-10 years. The Group Chief Investment Officer commented “That risk of higher bond yields for already-elevated equity and risk asset valuations is a facet of the medium term that will imply probably low and rather volatile returns.” The environment they describe does not sound overly conducive to the mass world of balanced portfolio managers, risk parity advisors, annual arithmetic return chasers. It does not sound like an environment where targeting performance at the mean of the return distribution is likely to make up for past years and decades of underperformance.


We suspect the answer, as usual, comes back to convexity. The first critical step is determining that the appropriate investment objective should revolve around geometrically compounded returns. That fundamental decision changes the math around the entire optimization process and will drive a rethink about what risk and portfolio management truly are. Next step, contemplate how good goalkeeping would allow you to change the attacking strategy of your current line-up, e.g. fewer defensive midfielders, more goal scorers. Finally, figure out what makes for good goalkeeping. Hint: if you are using a goalscoring metric, you might want to try a different approach.

Read our Disclaimer by clicking here