We absolutely must open this month’s Update with a wonderful bit of prose submitted to the Financial Times from our favourite poet laureate of the economics fraternity, Bill White.
“The Fed says it will no longer react to anticipated higher inflation but only to actual higher inflation. Yet they are failing to react to actual higher inflation because they anticipate it will decline. Perhaps the real framework is anything that justifies not tightening?”
Bill White; Financial Times, June 28, 2021.
The esteemed Mr White, no doubt, recognizes what central bankers likely know in their gut, it is a debt trap. The system cannot function on anything other than the most accommodative monetary stance in history, regardless of what the traditional dashboard of economic indicators are flashing! Any effort to reign in an overheating economy, through removal of extreme monetary stimulus, would likely trigger a bursting of asset and debt bubbles the world over.
The efforts to mask this dilemma appear on an increasingly regular basis. Readers of economic commentary need not search hard to find comments from central bankers and their representatives that obfuscate the relationship between their policies and the destabilizing wealth effects resulting from them. As we have discussed before, it is seemingly an exercise in Orwellian “newspeak and doublethink”.
One can find examples of this from Singapore:
“Market processes are allocating an increasing share of national income to income from property and other financial assets and a reducing share to income from work.” “This is a development that we should be deeply concerned about.” MAS Managing Director Ravi Menon.
“We are not going to use monetary policy to deal with housing prices”. RBA Governor Philip Lowe.
“The Governing Council does not conduct systematic policies of either “leaning against the wind” (whereby monetary policy is systematically tightened when systemic risk builds up) or of “cleaning” (whereby monetary policy is systematically loosened when systemic risk materialises).”
To the USA:
“Participants observed that economic activity was expanding at a historically rapid pace.”
“Several participants saw benefits to reducing the pace of these purchases more quickly or earlier than Treasury purchases in light of valuation pressures in housing markets.” Minutes of June FOMC Meeting.
As Bill so succinctly states “perhaps the real framework is anything that justifies not tightening”.
Likely the most common metrics for indicating monetary policy stance by the Federal Reserve would be the Fed Funds Rate and the expansion of their balance sheet through their various Quantitative Easing (QE) asset purchases.
Figure 1: Fed Funds Rate and Federal Reserve Balance Sheet
A useful way of looking at the combined impact of these two is what is known as the Wu Xia Shadow Rate
Put simply, it tries to represent the effective equivalent Fed Funds Rate adjusted for the impact of QE, thus allowing policy to pierce the zero bound. The below graph clearly indicates that current settings are near historical extremes of “looseness”.
Figure 2: Wu Xia Shadow Rate (red) vs Fed Fund Rate (white)
The Fed claims to be managing to their Congressionally imposed “dual mandate” of price stability and employment. We will not get into the various evolutions of how they have redefined and prioritized this mandate, regularly making it more and more subjective, but rather stick to just an effort to take a standardized view of how policy vs circumstance has behaved over time.
There are a variety of pricing measures (far too often referred to as “inflation” but better termed as price indices or chosen measures of inflation) that we can look at. Probably the most common measure is the Consumer Price Index (CPI), while the Fed explicitly chooses to focus on the Personal Consumption Expenditure Deflator Core Price Index (PCE). The Fed’s choice of price index may or may not have something to do with the PCE consistently showing the lowest rate of increase of all the commonly available indices. Obviously, taking these as a measure of relative “hotness” of the economy, we can say we are at 30-year extremes of heat.
Figure 3: CPI (white) and PCE (red) YoY%
On the employment side, the most common metric is probably the Unemployment Rate.
Figure 4: US Unemployment Rate
In the market community the focus tends to be on the Nonfarm Payrolls, ie total employed people. Recently, given oddities around willingness to take on jobs by unemployed workers, there has been some focus around the Jobs Openings Total (JOLT) numbers. The gap between the current level of Nonfarm Payrolls and where things stood prior to the economic impact of pandemic related policies is generally the number one excuse by policymakers for continuing with the extreme measures put in place in March/April 2020. The JOLT numbers surging to historical highs is often used to indicate that demand for workers, presumably what accommodative monetary policies are meant to target, is no longer a problem.
Figure 5: Total Nonfarm Payrolls (yellow) vs JOLT (white)
A simple way to standardize a view of the relative economic heat is to use the good old Taylor Rule, which is just a simple formulaic representation of price and employment measures adjusted to a hypothetical Fed Funds rule-based rate. Without making any particular assumptions about the appropriate parameters of a given Taylor Rule setting (i.e. PCE vs CPI or NAIRU levels, etc.) it allows us to show a historical tracking of an objective level of the economic circumstances.
Figure 6: US Taylor Rule (yellow) vs Fed Funds Rate
Finally, we can use the Wu Xia Shadow Rate as our proxy of policy setting and a hypothetical Taylor Rule as our proxy of economic heat.
Figure 7: US Taylor Rule (yellow) vs Wu Xia Shadow Rate (red) – 30yr view
Figure 8: US Taylor Rule (yellow) vs Wu Xia Shadow Rate (red) – 50yr view
This comparison might allow one to claim that current historically loose policy has not responded to the recovery of economic circumstances to what could, by this view, be considered economic circumstances on the hot side of historical outcomes. We can see this even more clearly looking at the historical spread between the two metrics.
Figure 9: US Taylor Rule (yellow) minus Wu Xia Shadow Rate (red) – 30yr view
Figure 10: US Taylor Rule (yellow) minus Wu Xia Shadow Rate (red) – 50yr view
We can carry on and squeeze the individual data series, as well as the spread between the two, into normal distributions based upon the two time periods. This allows us to throw some statistical parameters around them.
Figure 11: Wu Xia Shadow Rate 30yr and 50yr time series Normal Distribution
In the 30yr time series the current Shadow Rate is in the 4th%tile of historical outcomes, while in the 50yr series it is in the 2nd%tile.
Figure 12: US Taylor Rule 30yr and 50yr time series Normal Distribution
Meanwhile, on the 30yr time series, the hypothetical Taylor Rule is in the 99th%tile, while on the 50yr series it is merely in the 71st%tile of economic heat.
Figure 13: US Tayor Rule minus Wu Xia Shadow Rate 30yr and 50yr time series Normal Distribution
It doesn’t take much mathematical intuition to guess that on the spread basis both time series land us squarely in the 100th%tile. Thus, using these standardized metrics, the divergence of policy from circumstances is at all-time historical extremes. We can create similar views using housing prices and equity values, things not considered (by themselves) as part of the price stability mandate of central banks, but rather “financial stability” issues, and see similar stories.
Figure 14: Case Shiller US Home Price Index YoY% (white) vs Wu Xia Shadow Rate (red)
Figure 15: SPX Index YoY% (blue) vs Wu Xia Shadow Rate (orange)
We’ve focused on the US Federal Reserve, given its role as keeper of the global reserve currency, but this really is a global problem with direct or indirect implications for every other central bank in the world. The “Debt Trap” is truly a global phenomenon this time around. Debt Trap, we believe, is the answer to the question we are posing, “Why is policy so out of sync with circumstances?”. As we have all heard so many times, “they can’t let rates go up”.
Policy is set to continue to add as much additional juice to the economy as can possibly be provided, yet the supposed indicators they follow are running as hot as has been seen in decades. We are all meant to believe that these policies, which we were told were tailored to generate resurgence in their price stability measures during the worst of times, will now have no further impact, even the inverse effect, on those measures during booming times. Is the Fed admitting that their policies in fact do not impact the metrics that they have long claimed to be targeting? Is the Fed admitting that they have lied to us all along? Or is the Fed admitting that their policies have created so much fragility, so much moral hazard, so much dependence on the forever support of stimulus, that anything less than full-on will not be able to sustain it? Probably some amount of all-of-the-above.
There was a recent article, reminding us of just what undergirds the functioning premise of Fed (and other central banks) policy, referencing the official transcript from a meeting in 1996 where Janet Yellen quite clearly lays it out. It is certainly possible to read Ms. Yellen’s points as intentionally and knowingly debasing the purchasing power of wage earners. Another participant in the meeting, Jerry Jordan, the then Cleveland Fed President, speaking on behalf of real people (i.e. those without an Econ PHD) and common sense states it clearly:
“The difference between 13 cents and 7 cents is the difference between a 2 percent rate of inflation and a 3 percent rate of inflation over 100 years. I think most people would view that as a silly alternative. They would say, why not zero inflation.”
History and common sense tell us that inflation, broadly, and most pointedly food price inflation, is destabilizing. The below chart of the UN Food Price Index makes clear the peaks that preceded the Asian Crisis (1996), the Great Financial Crisis (2007/08) and the European Credit Crisis (2011). It is hard to imagine that the current peak is somehow not relevant to the ongoing rise of civil unrest and socio-political instability we are seeing at various points of the globe at the moment. Can the Fed, and their legion of follower central banks, continue to pour fuel on the fire?
Figure 16: UN World Food Price Index
Any that have visited us in our offices in Singapore will be aware that we have forever had Stein’s Law written at the top of our white board. “If something cannot go on forever, it will stop”. We find this statement popping up ever so frequently of late as regards the current phenomenon we are discussing. Clearly, central banks cannot forever maintain policy settings at one extreme as economic circumstances push to the opposite extreme. Something has to give. As we discussed last month, it is the classic fat-tailed distribution: high expectations around the mean and really wide variance.
The investment solution to this dilemma remains the same. Structure your portfolio for what you do not know (the wide variance), not what you think you do know (the probabilistic mean). Compounding is driven in the wings. It is just math. Participate in the unexpected upside, e.g. central banks carry on and continue to drive asset values to historic levels without blowing things up. Cut off the unexpected downside, e.g. things blow up. Be convex!
We used this simple example (Figure 17) in a recent conference to try to help a bunch of forecasters of ‘x’ better understand the importance of f(x). To make a point about the value of protection through the longest running bull cycle in history, we started the time series as of March 2009.
Figure 17: CBOE S&P Put Protect Index (blue) vs CBOE Put Write Index (red)
The blue dots/line are the CBOE Put Protect Index (PPUT Index) while the red dots/line are the CBOE Put Write Index (PUT Index). PPUT owns S&P and systematically buy 1month puts for protection. PUT systematically sells 1month puts. They are, in a sense, mirror images of each other. One pays risk premium, and one earns risk premium. One is convex, one is concave. Over time, the convex one outperforms. That outperformance, not surprisingly, takes place in the wings. This outperformance has nothing to do with alpha, timing or algos, it is literally just math. The PPUT Index has a CAGR over this period of circa 13.3% on a downside volatility of 6.1%. The PUT Index has a CAGR over the period of 9.9% on a downside volatility of 8.1%. If we equalize the two on downside volatility of circa 6.1%, the PUT Index CAGR drops to 7.6% over the period.
Figure 18: CBOE S&P Put Protect Index (blue) vs CBOE Put Write Index (red): Risk Equalized
This is a great visualization to challenge proponents of “risk premia harvesting”, or those academic papers that claim volatility is overpriced. More often than not, they are, shall we say politely, comparing apples and oranges. Likewise, anybody that talks about the “cost” of owning hedges/vol/convexity on a standalone basis, is simply not looking at the problem correctly. There must be a factoring of the portfolio impact through time and the ability to adjust upside risks due to superior downside protection. Not everyone misses penalties in a shootout, you need a goalkeeper.
It turns out, by a certain perspective, that there might actually be “alpha” in the long vol space, or maybe volatility is actually structurally underpriced. We can fiddle with the weightings of a version of our “Always Good Weather” theoretical portfolio, using the CBOE Long Vol Index of Long Volatility Hedge Fund Managers and splitting the weightings in the compounding asset between SPXT and XNDX (Nasdaq Total Return) and come up with something with the same downside volatility of circa 6.1%. This gives us something with a stunning bull market CAGR of circa 18.7% assuming yearly rebalancing.
Figure 19: Always Good Weather (44/44/12) (blue) vs CBOE Put Protect Index (red)
As we always say, lever the hedge. Just rerunning the same again with 2x on the Long Vol, so with a 24% allocation, puts the CAGR now at 19% and reduces the downside volatility to 5.1%. Less risk, more return. “It’s alpha Jim, but not as we know it!”….with apologies to The Firm and ‘Star Trekkin’.
Figure 20: Always Good Weather (44/44/24) (blue) vs CBOE Put Protect Index (red)
As we have mentioned before, on the absolute return side, it would seem that there is generally negative alpha when compared to the simplistic rolling of short S&P puts. If we adjust to the lower 3.1% downside volatility of the HFRX Index we get an allocation of just 40% to the Put Write Index. The below pictures make clear just how similar the risk and returns of these two strategies are. Not diversification. On these balanced risk levels, the PUT Index has a CAGR of 4.2% versus the HFRX of a mere 2.7%. Not alpha.
Figure 21: CBOE Put Write Index (red) vs HFRX Global Hedge Fund Index (blue)
How do we construct portfolios that perform in the wings, that will protect us from the excesses of central bank policies? We could package together things that are convex in the wings and that are truly diversifying. We can go through the steps with our Scattergram tool. As a nicely convex in the up-tail compounding asset, we will use the Thompson Reuters Private Equity Buyout Index (TRPEI Index) and for the nicely convex in the down-tail we will use our standard CBOE Long Volatility Index. Adjusting again to roughly a 6% downside volatility, and going to our longer data series commencing January 2005, on the TRPEI leaves us with about a 45% allocation on a standalone basis, with the balance 55% in cash. Plotting that alongside the Long Vol hedge as two separate strategies allows the obvious convexity in the wings and true diversification to stand out.
Figure 22: TRPEI Index (blue) vs CBOE Long Vol (red)
Both strategies are clearly convex, and they are negatively correlated in the wings. They are perfect portfolio complements. We can package them together, our Barbell Portfolio strategy, and solve the weightings to again get us to something in the region of a 6% downside volatility. That works out to 55% allocation to the TRPEI and 45% to Long Vol. As the comparison we can throw in a Balanced Portfolio, similarly weighted to achieve an equivalent downside volatility of 6%, which leaves us with a 50/50 portfolio of S&P and US Treasuries. The CAGRs are 11.4% for the Barbell versus only 7.4% for the Balanced.
Figure 23: Barbell TRPEI 55% + CBOE Long Vol 45% (blue) vs SPXT 50% + US Treasury 50% (red)
You all know what comes next – lever the hedge! So, assuming we can make the allocation 2x on the Long Vol, we can go to something like 70/60 on the Barbell. This keeps us at the same 6% downside volatility overall.
Figure 24: Barbell TRPEI 70% + CBOE Long Vol 60% (blue) vs SPXT 50% + US Treasury 50% (red)
For the Barbell Portfolio, this increases the CAGR to 14.6%. The terminal compounded capital is fast approaching triple that of the equivalent risk Balanced Portfolio in just over 15 years. This is the magic of convexity. This is how to deal with the unstable equilibrium and massive tail entropy created by extreme central bank policy. This is what fiduciaries managing long term capital (endowments, pensions, Sovereign Wealth Funds, Family Offices) need to be doing. As we have shown before, so much of what is being done in the name of diversification is just delusion.
Figure 25: Barbell Portfolio TRPEI 70% + CBOE Long Vol 60% (blue) vs Endowment Index (red)
Comparing the PE+Long Vol Barbell to the Endowment Index shows just more of the same. Only in this case we didn’t bother to risk adjust the Endowment Index. At the 100% allocation, the Endowment Index comes with nearly double the downside volatility at 11.6%, and more than double the maximum peak-to-trough drawdown at 44.9% versus 18% for the Barbell here.
As we often state, when it comes to building convexity into a portfolio, “Where is the cost?”.
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