Markets

The British Refrigerated Meat Trade, 1880-1930

There were 8.2m city dwellers in Britain in 1850, dwarfing the 2.6m in the United States, and the 1.6m in Canada, Australia, New Zealand, Argentina, Ireland and Denmark combined. At the very peak of British self-confidence, when everything was going for Britain, the London carnivore was deeply unhappy. He had heard too much already about British innovation, about the so-called industrial revolution going on up north, and about the promised bounty of ghost acres. He just didn’t see it. What he really wanted was prime beef and the choicest lamb. No more animals could be fattened on British soil, even on imported grain. European lands were running out of surplus to ship to Britain on account of the growth of their own appetite. Denmark and Ireland were still reliable but both were as close to carrying capacity as the home counties. So … the ghost acres. The Londoner’s problem at mid-century was that livestock shipped 3000 miles from New York suffered significant erosion of quality and weight loss. Put bluntly, it was shit. It did even worse coming 16000 miles from the antipodes. Even the choicest cuts from imported livestock always sold at a significant negative premium against British prime. In any case, the settlement of the Anglo newlands had only just gotten underway.

Over the next half century, human and slaughter-animal (cattle, sheep and pigs) populations of Belich’s Anglo newlands (the American West, Canada, Australia, and New Zealand) would triple, cleared cropland there would quadruple, and pasture would expand by a factor of seven. But most of the meat bounty was destined not for the plate of our insatiable Londoner; urban populations in the Anglo newlands also expanded by a factor of seven (pointedly more in Oceania than Canada). New York in particular developed a voracious appetite for Mid-Western meat, which would soon leave little left over for the mother trade. Deliverance for our hungry Londoner would come in the form of chilled prime beef from Argentina. But I am getting ahead of the story.

Vitals of the British Meat Trade.
Urban population (million)
Britain US CAN AID
1850 8.2 2.6 0.4 1.1
1900 17.4 17.5 2.6 3.4
Population (million)
1850 27.2 23.6 3.2 9.5
1900 41.2 76.4 10.0 11.7
Pasture (thousands of square km)
1850 10.8 26.7 0.7 12.8
1900 14.2 138.3 53.2 33.9
Cattle (million)
1850 2.9 19.0 3.2 22.9
1900 7.5 59.7 13.7 30.0
Pigs (million)
1850 2.1 20.6 0.9 1.2
1900 2.9 51.1 3.4 3.4
Sheep (million)
1850 21.6 6.8 0.5 3.7
1900 21.5 7.6 20.5 4.1
Cropland (thousands of square km)
1850 5.1 23.8 6.3 3.8
1900 7.0 75.6 22.9 6.4
Source: Clio Infra. CAN=Canada, Australia and New Zealand, AID=Argentina, Ireland and Denmark.

Before the meat could be shipped, the slaughter animal had to be fattened. Above all this required the opening of Belich’s Anglo newlands to dense settlement. That in turn required tens of millions of migrants from the Anglo oldlands. But the land had not only to be cleared. Until midcentury, the American interior could not be densely settled on transport networks confined to water. Cincinnati’s water-borne pork hegemony was thus precarious.

Packing MidWest 1840s

It was rail that opened up the American interior to dense settlement. It was rail that created Chicago. It was rail that solved the concrete problem of feeding the Chicago-New York-London pipeline. This central feeder belt of the British meat trade in the 1880s was dependent on rapid transport further inland. The American railway system was financed by London bondholders. British bond finance was also critical in the Dominions proper, as indeed, Argentina. Some £20m of British capital was invested directly in the Argentine meat-packing companies.

But the population history and the rail network weren’t enough. Even if the capacity to produce that much meat is assured, the technical problem of mechanical refrigeration on transoceanic ships had to be mastered. Straightup freezing worked for mutton and lamb. So frozen New Zealand lamb was accepted as prime by our London carnivore when it arrived in the 1880s. After the turn of the century, New Zealand shipped more than one hundred thousand metric tons of frozen lamb every year to Great Britain. Argentinian and Australian lamb provided an additional one hundred thousand. New Zealand would ship an extraordinary one-fifty thousand in 1922. The years after the world war were marked by the violence of British bloodletting in a bid to return to the Gold Standard. But at least our hungry Londoner could score some prime New Zealand lamb. Even the lamb from Argentina and Australia could be top-notch.

British_lamb_trade.png

The unit on the Y axis is metric tons.

But beef did not take well to freezing. For as Perron (1971) explained:

Frozen meat is kept at a temperature of between 14ºF and 18ºF, but between the temperatures of 31ºF and 25ºF large ice crystals form between the muscle fibres of the meat and this process ruptures some of the small vessels of the flesh. When the meat is thawed this gives it a sweaty, discoloured appearance and it loses a certain amount of moisture, making it less juicy when cooked. This effect is more noticeable in large carcases like beef; having a greater bulk than mutton and lamb they take longer to pass through the critical range of temperature where the large ice crystals are formed and the damage done. But meat can also be chilled, that is, kept at a temperature of 30ºF which is just above its freezing point and this means that the ice crystals do not form in the carcase.

The chilling solution (obviously) was articulated by American meatpackings giants. The big four American meat-packers dominated the British chilled beef trade in the 1880s. Meanwhile, the British lamb trade was a definite Kiwi monopoly. The great sucking sound of the London market—London relied disproportionately among British cities on imported meat—had reoriented Belich’s Anglo newlands. The first big suppliers were Belich’s American northwest and New Zealand.

Northwest

There was a major epidemiological panic arising from the discovery of diseased frozen shipments at the turn of the century (that’s the crash in the graph for Beef imports in 1901). This would prove to be a hiccup in the real story: the rise of chilled beef from Argentina that would more than replace the Americans in the British beef trade. Argentina’s market position by the end of the decade outrivaled that of New Zealand’s in the British lamb trade. Of course, British lamb and mutton predominated in the national market. But by 1914, imported meat accounted for 40 percent of British consumption. More than any other great power in history, Great Britain came to rely on ghost acres for its meat.

MeatTrade.png

In the overall scheme of things, our hungry Londoner was finally satiated at the turn of the century when British imports of refrigerated beef, lamb and mutton stabilized in the ballpark of one billion pounds a year. In 1922, the British refrigerated meat trade as a whole peaked (at least locally) at more than a billion pounds (around half a million short tons).

British_meat_trade.png

In the 1890s, Argentina emerged as a major player in the British meat trade. Argentina was the solution to the problem posed by New York’s growing appetite for Chicago beef (increasingly joined by other American cities). In the 1900s, Argentina displaced the United States in the chilled beef trade and emerged as a near-peer of New Zealand in the lamb trade. Already by 1903, Argentina was supplying more refrigerated meat to Britain than any other nation.

British_meat_trade.png

Selected exporters only.

Britain’s refrigerated meat trade could survive the rise of the American carnivore and the reorientation of the American West to point to New York. But this was far from an automatic process. In the six principal suppliers besides the United States, during the second half of the nineteenth century, some 30 million additional people would help clear 270 thousand additional square kilometers of land for pasture and 89 thousand square kilometers more of cropland on three continents; allowing them to raise 30 million more sheep and 45 million more heads of cattle a year. A vast portion of world ecology was thus transformed to suit the taste of the British carnivore. Indeed, New Zealanders replaced their sheep with breeds more attractive to the London palate. Argentinians did the same with cattle. As did Australia and Canada; even old Ireland and Denmark had to keep adapting to Metropolitan tastes. Only the American West served the other pole of the Angloworld. Everyone else served London.

The British refrigerated meat trade began in 1875. By the turn of the century, the supply of meat to Britain had expanded and diversified well beyond the American North-West. It came into its own and lasted until well into the twentieth century. It was only in the 1950s that the British share of New Zealand lamb exports fell below 50 percent. The timing of the core phase of expansion of the refrigerated meat trade, 1880-1910, suggests that we must file this under the secondary industrial revolution. Britain’s ghost acres came to finally bear in the last quarter of the nineteenth century. The increased availability of prime meat may be directly responsible for the vanishing of the settler premium in Anglo-Saxon stature in the early twentieth century.

Stature (cm)
Britain US Canada Australia
1810 169.7 171.5
1820 169.1 172.2 171.5
1830 166.7 173.5 171.5
1840 166.5 172.2 170.4
1850 165.6 171.1 172.5 170.0
1860 166.6 170.6 172.0 170.6
1870 167.2 171.1 171.2 170.1
1880 168.0 169.5 171.2 171.1
1890 167.4 169.1 170.7 171.3
1900 169.4 170.0 169.9 172.3
1910 170.9 172.1 171.5 172.7
1920 171.0 173.1 173.0 172.8
1930 173.9 173.4 172.7
Source: Clio Infra.

This interpretation would be consistent with the evidence from life expectancy. British life expectancy was falling as late as 1870. And it is only after 1900 that it really picks up. No doubt indoor plumbing, penicillin, urban sanitation, and personal hygiene were all implicated in the transformation of everyday living standards recorded in stature and mortality data. But the growth in per capita meat consumption from 91 lbs in 1880 to 131 lbs in 1909-1913 definitely played its part. What this meant in practice was that compared to 1870 our London carnivore was eating meat twice as often on the eve of the world struggle. And not only was the quantity greater, the quality of chilled beef from Argentina and frozen lamb from New Zealand was finally up to the demanding standards of our discerning Londoner.

life_expectancy.png

 

 

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Markets

Musings on the Microstructure of the Market for Risk

margin-call

Margin Call (2011)

In closing the previous dispatch I offered that we may be missing a theoretical piece of the puzzle. Here I offer some musings on what sort of structure I think we need to get an even better handle on asset prices.

My understanding of the microstructure of the dealer ecosystem suggests to me that we have three kinds of market players in the market for risk: sell-side, buy-side, and noise traders. US securities broker-dealers on the sell-side make markets by trading at quoted prices. They also provide funding for the trades which consumes balance sheet capacity (the risk-bearing capacity of the sell-side relative to the scale of the buy-side which I have argued is the right pricing kernel in intermediary asset pricing). Noise traders are needed to close the model. More on them later.

Balance sheet capacity is a joint function of the relative ease of funding in the wholesale funding market on the one hand and the market clearing price of risk in the over-the-counter derivatives market on the other. When the price of risk is low (ie when asset valuations are high) more funding can be secured against the same collateral than when the price of risk is high (ie asset valuations are low). This generates a dangerous feedback loop between the market price of risk and the ease of funding.

To be sure, default-remote bonds serve as collateral in the rapidly spinning rehypothecation flywheel because the stability of the flywheel requires the absence of default risk. The proximate cause of the GFC was the fateful introduction of private-label RMBS into the flywheel. And it was the great sucking sound of the wholesale funding market that generated the housing finance boom. Once debt burdens triggered a massive wave of defaults and credit risk reached the flywheel it tottered and shrank, but continued to spin rapidly in its shrunken state on public collateral. But the crunch of the wholesale funding market generated a massive seizure in the machine of global credit creation, sending a massive shockwave that propagated worldwide. Only those with autonomous financial systems insulated by thick regulatory firewalls and those too remote to have been penetrated by global finance managed to come out in one piece.

In the aftermath of the GFC, an intrusive enforcement regime of limits on bank leverage, balance sheet surveillance, risk-assessment, and other regs have reduced the elasticity of dealer balance sheets. The sharply reduced risk-bearing capacity of the system is reflected in breakdown of the iron law of covered interest-rate parity, volatility spikes, and the risk on-risk off behavior of asset prices. Due to the upper bound on the leverage of global banks, the ease of funding has become a function of US monetary policy with the result that the strength of the dollar has emerged as a barometer of the price of balance sheets. Indeed, the strength of the dollar is now priced in the cross-section of US stock returns.

With the dealers pinned down, fluctuations in the market price of risk can be expected to driven by developments on the buy side. Investment strategies of large asset managers are variations on a small number of themes. Big institutional investors like pension funds and insurance companies (‘real money investors’ in the finance jargon) are bound by regulation and governed by similar investment philosophies to maintain asset allocations in certain definite proportions, which requires periodic and tactical rebalancing of their portfolios. When strategists speak of rotation in and out of asset classes, it is these real money investors that they usually have in mind. Also on the buy side are less constrained hedge funds who make up for their smaller size ($3 trillion AUM in the aggregate) by their tactical agility and willingness to make lots of leveraged bets funded by the dealers. Somewhat between the two are leveraged bond portfolios like Pimco who are interested in holding positions with ‘equity like returns with bond like volatility’ (Bill Gross:”Holy Cow Batman, these bonds can outperform stocks!“). That’s your buyside.

Then we have the noise traders. We can think of them as low information small retail investors, or plainly speaking, the small fry whose herd behavior is driven by sentiment. They kick asset prices away from fundamentals by randomly bidding asset prices too far up or down, thereby generating positive risk premia that are then harvested by the big fish. More generally, the game is subtly rigged towards the house by structural advantages of the dealers. In particular, privileged access to order flow information puts dealers is a position of tactical advantage. Apart from trading for the house, traders at dealer firms share order flow information (and therefore the information premium) with their networks on the buy side in exchange for a larger volume of trades with their attendant commissions. Moreover, since exposure to fluctuations in balance sheet capacity comes with a juicy risk premium, dealers and their counterparties in the market for risk enjoy higher risk-adjusted returns than the small fry even without exploiting order flow information. Furthermore, traders on the sell-side are not beyond generating handsome profits by pumping asset prices up and down or otherwise loading the dice. Although the real scandal is what is perfectly legal.

 

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Markets

Stock Market Fluctuations Are Driven by Investor Herd Behavior

FT AlphaVille linked to an interesting blog post by Nick Maggiulli on Dollars and Data that examined the long-run stock return predictability in terms of equity allocations. Nick shows that high allocations predict lower ten-year returns. Here’s a replication of the main result.

tenyear

The result must be taken with a pinch of salt. Is it a feature or a bug? The cause for concern is that overlapping regressions generate spurious correlations. There is good reason to be skeptical of the extremely high coefficient estimate (r=-0.897, p<0.001). It likely reflects the medium-term cycle in Equity Allocation. (We use the same metric as Nick and in the original blog post at PhilosophicalEconomics.) Econometrically, regression estimates rely on the assumption that the series is stationary (no detectable temporal patterns like trends and cycles) which is manifestly violated here. See next figure.

EQA.png

What is required for kosher statistical inference is to transform the series so that it is at least roughly stationary. The best way to do that is to difference the series. Here we look at changes in the natural logarithm (ie, compounded rate of return) of the SP500 Index and Equity Allocation. The two series are manifestly stationary and appear to be strongly contemporaneously correlated.

ReturnPredictability.png

Indeed, contemporaneous percentage changes in Equity Allocation strongly predict quarterly returns on the SP500. Our gradient estimate (b=1.25, t-Stat=30.7) implies that 1 percent higher allocation to equities predicts a 1.25 percent quarterly return on the SP500 over and above the unconditional mean of 1.79 percent per quarter. Equity Allocation explains 78 percent of the variation in stock market returns. See next figure.

Predictable.png

The empirical evidence is rather consistent with the idea that fluctuations in the stock market reflect investor herd behavior. Specifically, the stock market goes up when investors rebalance to equities and goes down when investors rotate out of equities to bonds and cash. This is not only an important amplifier of dealer risk appetite and monetary policy shocks but also an important source of fluctuations in its own right. So stocks are getting culled across the board as we speak precisely due to investor rebalancing prompted by higher yields. (In turn, higher yields reflect either the expectation that the Fed will hike faster, a higher term risk premium, or both. The two can be disentangled using the ACM term-structure model as I illustrated not too long ago. [P.S. It’s risk premium; although Matt Klein doesn’t seem to buy the ACM decomposition.)

Tying market fluctuations empirically to investor herd behavior goes some way towards explaining the excess volatility of the stock market that has long puzzled economists. My wager is that stock markets fluctuate dramatically more than reassessments of underlying fundamentals could possibly warrant because of fluctuations driven by investor rebalancing.

The question is whether this is due to the herd behavior of small investors, or whether it is due to the inadvertently-coordinated rebalancing among large asset managers because they face similar mandates. If the former, that leads us to questions of investor sentiment. If the latter, it leads us straight back to market structure. In particular, it draws our attention to the buy side. Instead of paying exclusive attention to dealers and wholesale funding markets, perhaps we should also interrogate the investor behavior of large asset managers as an independent source of fluctuations in the price of risk.

In either case, knowing that rebalancing investor herds drive stock market fluctuations is not very useful since data on equity allocation is only available at the end of the quarter. Or is it not? Can we not think of Equity Allocation (hence implicitly investor herd behavior) as a risk factor for pricing the cross-section of stock excess returns? Indeed we can. Turns out, percentage changes in Equity Allocation are priced in the cross-section of expected excess returns. We illustrate this with 100 Size-Value portfolios from Kenneth French’s library.

CSR.png

What we find is that instead of a linear pricing relationship whereby higher betas imply monotonically higher expected returns in excess of the risk-free rate, the relationship is quadratic. Portfolios whose equity allocation betas is moderately high outperform portfolios with extreme betas in both directions. So an easy way to make money is to hold portfolios that are, depending on your risk appetite, long or overweight moderate beta stocks, and short or underweight extreme beta stocks.

Note that stock portfolios that are more sensitive to tidal investor flows are generally more volatile. See next figure.

volatile.png

The big puzzle that thus emerges is why these frontier assets (stock portfolios that are highly sensitive to investor rebalancing) don’t sport high expected returns. For the fundamental insight of modern asset pricing is that risk premia (expected returns in excess of the risk-free rate) exist because investors require compensation to hold systematic risk (but not idiosyncratic risk since that can be easily diversified away). In other words, assets that pose a greater risk to investors’ balance sheets ought to sport higher returns. We have shown that the tidal effect of inadvertently-coordinated investor rebalancing is a significant and systematic risk factor for all investors. So why isn’t there a monotonic relationship between the sensitivity of portfolio returns to investor rebalancing and the risk premium embedded in the cross-section? Why is the price of risk quadratic and not linear in beta? Clearly, we are missing a theoretical piece of the puzzle.

 

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Markets

What Exactly Happened on Vol Monday?

The return of market volatility on Monday, 5 February 2018, was dramatic. The 20 point jump in VIX, a traded measure of expected stock market volatility—best thought of as the price of insurance against a market crash—was the largest daily increase since the 1987 stock market crash.

27625271_10159911284310287_7464389263576233530_o

Daily returns on the VIX and the S&P 500.

A number of leveraged volatility-linked exchange-traded products (ETPs) were implicated in the market commentary that followed. But a clear cut picture was hard to discern. The following forensic analysis by the Bank of International Settlements’ Quarterly Review clarified the dynamic at play:

Issuers of leveraged volatility ETPs take long positions in VIX futures to magnify returns relative to the VIX – for example, a 2X VIX ETP with $200 million in assets would double the daily gains or losses for its investors by using leverage to build a $400 million notional position in VIX futures. Inverse volatility ETPs take short positions in VIX futures so as to allow investors to bet on lower volatility. To maintain target exposure, issuers of leveraged and inverse ETPs rebalance portfolios on a daily basis by trading VIX-related derivatives, usually in the last hour of the trading day.…Given the historical tendency of volatility increases to be rather sharp, such strategies can amount to “collecting pennies in front of a steamroller”.…

Given the rise in the VIX earlier in the day, market participants could expect leveraged long volatility ETPs to rebalance their holdings by buying more VIX futures at the end of the day to maintain their target daily exposure (eg twice or three times their assets). They also knew that inverse volatility ETPs would have to buy VIX futures to cover the losses on their short position in VIX futures. So, both long and short volatility ETPs had to buy VIX futures. The rebalancing by both types of funds takes place right before 16:15, when they publish their daily net asset value. Hence, because the VIX had already been rising since the previous trading day, market participants knew that both types of ETP would be positioned on the same side of the VIX futures market right after New York equity market close. The scene was set.

There were signs that other market participants began bidding up VIX futures prices at around 15:30 in anticipation of the end-of-day rebalancing by volatility ETPs (Graph A2, left-hand panel). Due to the mechanical nature of the rebalancing, a higher VIX futures price necessitated even greater VIX futures purchases by the ETPs, creating a feedback loop. Transaction data show a spike in trading volume to 115,862 VIX futures contracts, or roughly one quarter of the entire market, and at highly inflated prices, within one minute at 16:08. The value of one of the inverse volatility ETPs, XIV, fell 84% and the product was subsequently terminated.

Screen Shot 2018-03-24 at 1.18.52 PM.png

Source: Bank of International Settlements, Quarterly Review, March 2018.

 

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The Restoration of the Corporate Profit Share

In the exchange with Brenner, we were talking about profit rates which are defined as the ratio of profit to capital stock. In what follows we will document the empirical evidence for another measure of corporate profitability, the profit share, ie the ratio of corporate profits to GDP. This alternate metric is useful because measures of capital stock are highly sensitive to methodology, especially the treatment of depreciation. The profit share is not the answer to, How profitable are US firms? It is the right answer to, What portion of national income ends up in the coffers of US corporations?

pretax_all

Figure 1. Pretax profit of US firms, 1935-2016.

Figure 1 displays the ratio of pretax profit of all US corporations since 1935. We see that the profit share was very low during the great depression, rose mightily after the US entry into World War II, largely stayed in double digits until 1969, and fell dramatically thereafter. The profit share was restored partially in the mid-nineties, and much more robustly by the mid-2000s. Since the GFC, the profit share has been close to its historic peak.

Brenner would argue that financial sector profits are an artifact of asset price booms; that we should be looking at the profits of nonfinancial firms. Figure 2 displays the profit share of US nonfinancial corporations. We see that their profit share has also been restored to the postwar level of around 8 percent of GDP.

pretax_nfc.png

Figure 2. Profit share of US nonfinancial firms.

The complement of Figure 2 is the profit share of the financial sector, here operationalized as FIRE (finance, insurance, and real-estate). Figure 3 displays the financial sector share. Financial profits are at their highest level in the postwar period; about 0.5 percent of GDP, roughly $100bn, higher than the average of the postwar period; largely by central bank design—aimed to strengthen the balance sheets of US financial institutions. Note the dramatic crash during the GFC.

pretax_FIRE.png

Figure 3. Pretax profit share of US FIRE sector.

Figure 4 displays the effective tax rate on US corporations, obtained by dividing the difference between pretax and post-tax profit by the former. We see that the effective corporate tax rate rose dramatically in World War II, stayed above 40% in the fifties, and has been stepping down since. Remarkably, it is now back at levels last seen before the war.

CorpTax

Figure 4. Effective corporate tax rate for US firms.

Unsurprisingly, this has led to a dramatic revival in the post-tax profit share. See Figure 5. After-tax profit share is now about 3-4% of GDP higher than that prevailing in most of the 20th century. US GDP is about $18.5 trillion, so corporations are pulling in around $600-700 billion more than they ever did.

post-tax_all.png

Figure 5. After-tax profit share.

What have the firms done with all this cash? Figure 6 displays the distributed earnings as a percentage of after-tax profits. Whereas in the postwar era, firms retained around 60 percent, in the neoliberal era, they have disgorged around 60 percent of their earnings to investors in the form dividends, and increasingly, share buybacks.

distributed_profit

Figure 6. Distributed profits.

Figure 7 displays the decomposition of US corporate profits into broad sectors. Nonfinancial Nonmanufacturing is the residual category, largely made up of services and extractive industries; FIRE is finance, insurance, and real-estate; ROW is receipts from abroad in gross terms, ie we have not deducted the profits earned by foreign firms. A number of observations are in order.

Pretax

Figure 7. Corporate profit decomposition.

First, the share of manufacturers declined steadily from around 6 percent in the fifties to about 2 percent of GDP by the 1980s. It has since fluctuated around the 2 percent level. Some of the decline in the manufacturing share is definitely real; profitability was never really restored in the great mid-century rust-belt industries. But some of it is only apparent. Due on the one hand to servicification, whereby value-added that had hitherto been embodied in the manufactured product is now provided as a service. And on the other, to Baldwin’s “second unbundling” whereby firms relocate production processes offshore, often within a few hours flying distance. In the former case, the same profit ends up in the nonfinancial nonmanufacturing sector. In the latter case, it shows up as receipts from abroad (the ROW sector).

Second, whether or not due to the supply chain revolution, receipts from abroad now amount to 4 percent of GDP. That comes to more than $600 billion a year. Since the GFC, US firms have booked slightly more than 5 trillion dollars of profit earned overseas. In the same period, manufacturers’ profit came to $2.85tn and that for FIRE to $2.35tn. So the rest of the world is roughly as big as finance, insurance, real-estate and all of US manufacturing put together. This is of signal importance to the political economy of the United States.

Third, services and industries that make up the residual nonfinancial nonmanufacturing sector seems to have become the dominant sector in the economy. Yet, unpacking it one is hard put to find an actual sector of sufficient mass. Figure 8 displays some sectors of interest. We choose sectors with an eye to US political economy. “Oil-based” includes all sectors heavily reliant on fossil fuels—oil and gas exploration, petroleum products, chemicals and plastics; “Tech” includes computer design and manufacture, information processing, media and entertainment; “Finance” includes only securities and credit intermediation; “Trade” includes wholesale trade, retail trade, warehousing and transportation. We sum up the profits of each of these sectors for two three-year late-cycle periods, 2004-2006 and 2012-2014.

selected

Figure 8. After-tax profits of selected sectors.

The dominance of multinationals and finance is manifest. Receipts from abroad alone amounted to slightly more than $2tn in 2012-2014, dwarfing the $155bn in healthcare, $340bn in Tech, $375bn in oil-based, and $616bn in trade. It is more than twice as big as US manufacturing whose profits in 2012-2014 were $910bn. At $1.5 trillion, only finance can compete with the multinationals.

In the frame of the investment theory of party competition, I posited that finance and multinational firms have congruent interests and their political alliance constitutes a hegemonic bloc of investors in the system of 1980; and that this was the source of the stability of the neoliberal consensus. The evidence suggests that we should think of multinational firms as the stronger party in that alliance.

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An Illustrated Guide to the Yield Curve

AT SIXTY TRILLION DOLLARS, the market value of US fixed-income securities is twice as large as the market capitalization of all US corporations and thrice the size of the American economy as measured by GDP. These debt securities consist of some $12tn in corporate bonds, $15tn in mortgages, another $15tn in interest-rate swaps; all of it anchored on the $16tn market for US Treasuries. When one talks about “the” yield curve one means the term structure of US Treasuries.

The yield curve does not actually exist. It is estimated from market prices of on-the-run Treasury securities using something called a quasi-cubic hermite spline function. (Don’t ask.) You don’t want to know how the sausage is made. What you want to learn above all is how to read the damn thing.

yield_curve

Figure 1. US yield curve as of 15 February 2018. (Source: NYFed.)

Figure 1 displays the yield curve as of 15 February 2018. Here’s how you should read this graph. Think of the bonds as paying $1 in n years time (called the maturity or term). What the graph tells you is that the prevailing market prices of bonds are such that buying one today and holding it to maturity to get that $1 yields the displayed return via the simple time-value-of-money formula:

bond price formula

The relationship between the price and yield of a zero-coupon bond.

Table 1 displays the prices of these hypothetical bonds whose yields are displayed in Figure 1.

Bond prices and yields
Maturity 1 2 3 4 5 6 7 8 9 10
Yield 2.00 2.22 2.41 2.55 2.65 2.74 2.80 2.86 2.90 2.94
Price $0.98 $0.96 $0.93 $0.90 $0.88 $0.85 $0.82 $0.80 $0.77 $0.75

These hypothetical, plain vanilla bonds that promise to pay $1 exactly n years in the future are called zero-coupon bonds. They are the building blocks of all bonds; any bond whatsoever can be mathematically represented as a portfolio of zero-coupon bonds. In other words, all the information contained in the messy markets for default-remote bonds can be read off the yield curve for zero-coupon bonds. And that is what is displayed in Figure 1.

The expectations theory of the yield curve says that the yield curve reflects the expected path of the short rate. More precisely, that the yield at time t on a zero-coupon bond of maturity n equals the market expectation at time t of short rate prevailing at time t+n.

expectations theory

 

The expectations theory is wrong. Bond prices are dramatically more volatile than that implied by the theory and they routinely move in opposite direction that that implied by the expected path of policy rates. The basic reason why the theory fails is that default-remote bonds, even the obligations of a global military hegemon, are not in fact risk-free. The most obvious risk is that inflation may erode the real value of the bond. But beyond inflation risk, a bond holder faces duration risk: The risk that interest rates may rise faster than expected thereby reducing the market value of the bond in her portfolio. Only bonds of very short maturity are truly risk-free which is why the risk-free rate is approximated by the 3-month Treasury bill. Figure 2 displays the time-variation in the yield curve and the expected path of the Fed’s policy rate since 2010. Note how the former is much more volatile than the latter and has utterly distinct dynamics.

YC_EP

Figure 2. The yield curve and the expected path of short-term rates.  (Source: NYFed.)

Bond yields have two basic components. They reflect on the one hand the expected path of short-term rates and on the other the compensation for the risk they pose to the bondholder’s balance sheet. The latter component is called the term risk premium. In order to extract the expected path and risk premium from bond yield, we have to use a term structure model. We use estimates from the gold standard of term structure models, the ACM model developed by economists at the NY Fed.

Figure 3 displays the evolution of the term structure of the term risk premium, ie the difference between the two “sheets” displayed in Figure 2.

TRP

Figure 3. Term structure of the term risk premium. (Source: NYFed.)

Comparing Figure 2 and Figure 3, observe that the bulk of the variation in the yield curve since 2010 has been driven by variation in the term risk premium. Figure 4 displays the two components of the yield on the 5-year note.

5year

Figure 4. Components of the yield on the five-year note. (Source: NYFed.)

We can see that, until 2014, most of the variation in the yield on the 5-year note was driven by variation in the risk premium. The big shock in 2013 was, of course, the taper tantrum induced by Bernanke’s announcement that the Fed would slow down its bond buying. Since 2014, the expected rate has gone up but the risk premium has fallen. Note that the term risk premium can, has episodically been, and at present is, negative. The collapse of risk premiums is not confined to the market for US Treasuries. Risk premiums (ie, expected returns in excess of the risk-free rate) get bid away across asset classes by investors’ increasingly desperate search for yield. Put another way, we are in an asset price boom—asset prices are too high. For high quality collateral like US Treasuries, this translates into negative risk premiums.

The expected path of the policy rate reflects the market’s expectation of the Fed’s reaction function on the one hand and the trajectory of core macroeconomic variables on the other. A steeper path implies that the market expects the Fed to tighten faster, say in order to counter inflation. So what has been going on over the past few weeks? Figure 5 zooms in on the components of the 5-year yield in 2018.

Recent

Figure 5. Recent movements in the components of the 5-year yield.

Both expected rates and the risk premium have gone up since the year began. Medium term rate expectations (5-year) went up 13 basis points—a basis point is hundredth of a percentage point—while the term risk premium went up 26 basis points. Rate expectations fell dramatically with the return of market volatility in early February. But that has since been priced out. Figure 6 displays the term structure of rate expectations. We see that rate expectations rose uniformly, fell together, and are now priced back in.

TSExpect

Figure 6. Term structure of rate expectations.

What all this means is that the market expects the Fed to hike faster, perhaps because it expects inflation to surprise on the upside, thereby forcing the Fed to hike faster than hitherto expected. Since there is no reason to believe that inflation has returned, this should get priced out soon enough.


 

expected

Bonus chart: The expected path of policy rates.

 

 

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Markets

Here is Why the Dollar is Weak

The so-called ‘Trump reflation trade’ started unraveling before Christmas Day, 2016. As expectations of inflation eroded and the expected path of the Fed’s policy rate became shallower, the dollar began to weaken. By the summer, all concerned agreed that the whole reflation trade had been priced out and then some. The dollar rebounded. But then…it started falling all over again.

USDWeakness

Figure 1. Euro/dollar spot rate. (Source: Federal Reserve.)

This appeared to be a great mystery and generated considerable talk about whether the US Treasury Secretary was talking down the dollar. The FT‘s well-respected market commentator, John Authers, noted,

Has the US dollar stopped making sense? US rates are rising, and a long-run bull market in Treasury bonds seems to be over. This is not happening elsewhere, so the differential between US and European yields has risen to its highest since the euro came into being in 1999. That should mean a rising US dollar and falling US stocks. But US stocks are shooting for the moon and the dollar is tumbling — down 13 per cent from last year’s high on a trade-weighted basis. One retort is that the US has just passed a big tax cut. Of course that raises earnings this year — so buy stocks and sell Treasuries. But it should also be a reason to buy dollars. And stocks that benefit most from the tax cut are doing no better than anyone else this year. This renders the weak dollar the more mystifying.

Matt Klein, one of the sharpest knives in the FT‘s drawer, offered that there was no mystery, it had to do with growth expectations in Europe/RoW rising relative to the United States. This would be the standard (nonfinancial) macro explanation of dollar weakness. As we shall see, he is not entirely wrong. Although the mechanism is not as straightforward as he implies.

In a followup, Authers later shared a comment from a trader that pointed to a massive carry trade underway whereby you borrow dollars to fund euro forwards. According to this trader, there was supermassive 100 basis point (ie, 1 percent) carry in the trade. This, he/she alleged, was the cause of dollar weakness.

I realized that there is a very easy way to check this. Such a carry would only exist if fwd rates deep in the curve were much higher on the continent than in America. Gavyn Davies had already noted the empirical case for this. We can do much, much better than suggestive visual evidence. Indeed, we will see how this can be nailed down mathematically.

We appeal to what’s called uncovered interest rate parity, which says that the home interest rate equals the foreign interest rate plus the expected rate of depreciation of the home currency. It imposes a consistency condition on the euro/dollar spot exchange rate on the one hand and the yield curves prevailing in America and on the continent on the other. Matt is right about changes in expectations about relative growth rates in the United States and the eurozone. What this means is that the US yield curve has become flatter than the German yield curve and that has opened up the carry that Authers’ trader gushed about.

TermSpreads

Figure 2. Term spreads in the United States (30yr minus 2yr) and Germany (15-30yr minus 2-5yr).

The proof is a straightforward calculation of uncovered interest rate parity. Figure 3 displays the expected future exchange rate implied by the yield curves displayed in Figure 2 via the UIP equation as well as the spot rate.

UIP

Figure 3. Uncovered interest rate parity for the euro/dollar exchange rate.

The interpretation is straightforward. Change is relative growth expectations between the United States and Europe led to relative movements in the yield curves which opened up a huge carry trade opportunity. And that massive carry trade put downward pressure on the spot rate for the dollar. Here’s a graph of the carry implied by the yield curves together with the spot exchange rate.

Carry

Figure 4. Carry implied by the yield curves on the continent and in America.

An interesting question that arises then is whether the carry trade consumed so much dealer balance sheet capacity that it precipitated the risk-off that began last week and culminated on Vol Monday. Perhaps that is why US bank stocks (but not European) did so well on Monday despite the volatility shock. If that is the case, we would’ve nailed two birds with one stone.

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