Markets

Mirror Mirror on the Wall: Asset Prices and Wall Street

Before I became a geometer and after I studied economics, I worked as a pricing actuary for a reinsurance firm. Insurance companies aggressively market their products and in the process accumulate more risk than they can stomach. They offload this risk to heavily-capitalized reinsurance firms whose entire business is to bear such tail risks and for which they get compensated in the form of ceded premium. The job of the pricing actuary is easy: Compute the expected loss and add on a risk premium for the value-at-risk. Value-at-risk is the largest loss you would have to bear, say, once in a hundred years. Reinsurance pricing is relatively straightforward because the underlying shocks are exogenous and independent of each other. Because you are insuring only against acts of God, the probabilities are relatively stable. It’s all quite tame.

Contrast that to the untamed gyrations of the market. In sharp contrast to the reinsurance industry, shocks to asset prices are endogenous and highly correlated. It is dramatically harder to price risky assets than bundles of insurance policies. Not coincidently it is also much more interesting.

For about a year now, my professional research has focused on asset pricing and macrofinance. I’ve written about financial cycles before. In this post, I’ll summarize my findings on asset pricing for the layperson. All the technical details can be found in my recent paper. I’m a strong believer in the notion that unless you can explain your ideas in plain English, either you don’t understand them yourself or you are peddling snake oil. So in what follows, I’ll try to explain in a clear and straightforward manner precisely what I have figured out.

My intellectual wanderings have convinced me that every single discipline is organized around a single powerful idea—a master key that unlocks the field. The master key that makes asset prices intelligible is systematic risk.

Modern finance began when the focus moved away from stocks to portfolios. The fundamental insight of modern finance is that investors are not compensated for holding idiosyncratic risk; they are compensated for holding only systematic risk. Idiosyncratic risk is the risk that a particular asset will lose value. Such risks are easily diversifiable. Simply by holding a portfolio with a large enough number of assets, an investor can reduce the threat posed by any particular asset to her balance sheet virtually down to zero. If there was any compensation for holding idiosyncratic risk, it would be immediately bid away by diversified investors for whom the risk is as good as nonexistent.

The defining feature of systematic risk is that it is hard to diversify away. For instance, if the market as a whole were to decline, you would feel the pain no matter how diversified a portfolio of stocks you hold. The Capital Asset Pricing Model says that that’s all there is to it: The only systematic risk is market risk. Things are not so simple, of course. The Capital Asset Pricing Model provides a rather poor explanation of asset prices.

More generally, an asset pricing model tells you what constitutes systematic risk. It is quite literally a list of risk factors. The sensitivity of a portfolio’s returns to a risk factor is called the portfolio’s factor beta. The expected return on a portfolio (in excess of the risk-free rate) is then simply the sum of the betas multiplied by the risk premiums on the factors. Your portfolio’s factor beta is your exposure to that risk for which your compensation is the risk premium on that factor. You earn exactly the risk premium on a factor if your portfolio’s beta for that factor is 1 and all other factor betas of your portfolio are 0.

The workhorse asset pricing model is that of Kenneth French and Eugene Fama from the 1990s. They have two risk factors besides market risk. The first is the difference between returns on stocks with low market capitalization and stocks with high market capitalization. That’s the size factor. The second is the difference between returns on stocks with high relative value and stocks with low relative value; where relative value is given by ratio of the book value of the firm (what they show on their accounts) and the market value of the firm. That’s the value factor.

This 3-factor model does well in explaining stock prices, as does Carhart’s 4-factor model; also from the 1990s. Carhart added a fourth factor, momentum, to the Fama-French 3-factor model. He builds on on the observation that stocks that perform well in a given month also do well in the following month. The momentum factor is simply the difference in the return on stocks with high prior returns and stocks with low prior returns.

These two workhorse models have been so successful that they have percolated down from academic journals to personal finance. If you have a bit of money in the bank or in your 401K, you have probably talked to an investment advisor. (The usual advice is to be aggressive if you have a long investment horizon, and play safe otherwise.) They often talk about high beta stocks (by which they mean high market beta), size stocks, value stocks, and momentum stocks. That’s all irrelevant. What matters are your portfolio’s factor betas, not the factor betas of the stocks! You should think of your portfolio not as a collection of stocks but as a bundle of factors.

The big problem with size, value, and momentum, is that it is not at all clear why they sport positive risk premiums. In other words, we do not have a theory to explain the empirical performance of these risk factors. They are, in fact, anomalies begging for explanation.

In recent years, a powerful new theory of asset prices has emerged from the wreckage of the financial crisis. It is this theory that attracted me away from my research on the geometry of black holes.

At the heart of the theory are giant Wall Street banks, referred to in the jargon as broker-dealers. These big banking firms are some of the largest financial institutions in the world. JPMorgan, for instance, has $2.5 trillion in assets.

As the financial crisis gathered pace in the fall of 2007, Tobias Adrian at the New York Fed (now at the IMF) and Hyun Song Shin at Princeton University (now at the Bank of International Settlements) started paying attention to broker-dealer leverage. What they found was striking.

Leverage is naturally countercyclical. When asset prices rise, equity rises faster than assets since liabilities are usually more or less fixed. Leverage therefore falls when assets are booming. Conversely, leverage rises when asset prices fall. This holds in the aggregate for households, non-financial companies, commercial banks, and pretty much every one else—except broker-dealers. Dealer leverage is procyclical. This is because dealers aggressively manage their balance sheets. When perceived risk is low, they increase their leverage and expand their balance sheets. When perceived risk is high, they deleverage and shrink.

In the years since that first breakthrough, the balance sheets of broker-dealers have been tied to the great mortgage credit boom, the shadow banking system, the transmission channel of monetary policy, the global transmission of US monetary policycross-border transmission of credit conditions, the yield curve and the business cycle (or more properly the business-financial cycle), and of course, asset prices.

This is quite simply the most profound revision of our picture of the global monetary, financial and economic system in decades. More on that another day. Let’s stick to the topic at hand.

What is absolutely clear is that an intermediary risk factor belongs in the pricing kernel (the vector of systematic risks). There is no disagreement that such a factor must be based on broker-dealer balance sheets (as opposed to the much broader set of financial intermediaries).

The big disagreement is on precisely what is the right measure to use as the risk factor. There are three competing groups of academics here. The first is the original group around Tobias Adrian, who argue that leverage is the right factor, that the risk posed to investors’ portfolios is that dealers could deleverage and therefore drive down asset prices. The second group, based around Zhiguo He at Chicago University, argue that the capital ratio (the reciprocal of leverage) of the holding companies that own broker-dealer firms is the right factor. This is because dealers can access internal capital markets inside their parent firms, and therefore don’t have to shed assets in bad times as long as they can ask their parents for money.

Both of these models are based on the observation that dealers are the marginal investors in asset markets. In effect, they replace the representative average investor who had hitherto played the starring role in asset pricing theory with broker-dealers. Basically, times are good when the marginal investor has high risk appetite (the marginal value of her wealth is low) and they are bad when she has low risk appetite (the marginal value of her wealth is high). Assets that do well in bad times ought to offer lower compensation to the investor than assets that do badly. The marginal value of her wealth therefore belongs in the pricing kernel.

The third group is a circle of one centered around yours truly. I argue that except for the interdealer markets—which are important as funding markets but not as markets for risky assets—both non-dealer risk arbitrageurs (basically all other big fish in the market) and dealers are simultaneously marginal investors. For the business of broker-dealers is to make markets. That is, dealers quote a two-sided market and absorb the resulting order flow on their own books. Importantly, dealers provide leverage to risk arbitrageurs by letting them trade on margin. Balance sheet capacity is the risk-bearing capacity of the dealers with system-wide implications. It goes up with both dealer equity and dealer leverage. When balance sheet capacity is plentiful, risk arbitrageurs can easily take risky leveraged positions to bid away excess returns. Conversely, when balance sheet capacity is scare, risk arbitrageurs cannot obtain all the leverage they want and therefore find it harder to bid away excess returns.

What this implies is that even if dealers were not marginal investors, their balance sheet capacity but not their leverage, still ought to belong in the pricing kernel. And if dealer leverage is tamed as it has by financial repression since the crisis, fluctuations in balance sheet capacity would still whipsaw asset markets. Balance sheet capacity is like the weather; it affects everyone. Of course, what matters is not the absolute size but the relative size of balance sheet capacity. I therefore define my intermediary risk factor to be the ratio of the total assets of the broker-dealer sector to the total assets of the household sector.

The first thing I show, of course, is that my intermediary risk factor is priced in the cross-section of expected stock excess returns. That is to say: Stocks with high intermediary factor betas have higher expected excess returns than stocks with low intermediary betas. Remarkably, a 2-factor model with my intermediary factor and market as risk factors explains half the cross-sectional variation in expected excess returns and sports a mean absolute pricing error of only 0.3 percent. The 4-factor Carhart model with market, size, value and momentum as risk factors, can explain a greater portion of the cross-sectional variation but it has a much higher mean absolute pricing error of 1.9 percent. (The mean absolute pricing error is a much more important measure than the percentage of variation explained.) In fact, I have shown that no benchmark multifactor model is competitive with my parsimonious intermediary model.

CSR

What I do next is to extract the time-variation of the premiums on the risk factors using a dynamic pricing model. First, behold the intermediary risk premium (see chart). What I love about this chart is the sheer intelligibility of the fluctuations. You can literally see the financial booms of the late-1990s and the mid-2000s when the premium gets extraordinarily compressed. The intermediary premium contains macroeconomic information: It predicts US recessions (the dark bands) and is manifestly correlated with the business-financial cycle. Indeed, I show in the paper that it is both contemporaneously correlated with, and predicts 1 quarter ahead, US GDP growth.

IntRiskPremium.png

There is clearly an important cyclical component in the intermediary risk premium. I isolate it using a bandpass filter that assigns fluctuations to the frequency at which they appear. The visuals are compelling. The lows of the cyclical component of the intermediary premium line up nearly perfectly with US recessions.

medfreq.png

None of the benchmark premiums share these properties. In fact, their confidence intervals almost always straddle the X axis, meaning that they are not even statistically distinguishable from zero.

Carhart.png

Here’s the money shot. The intermediary premium dwarfs the premiums on the benchmark factors. It appears to be at least thrice as great in amplitude as the benchmark premiums.

dwarf.png

Lastly, I show that a portfolio that tracks my intermediary risk factor has dramatically higher returns than benchmark factor portfolios. Over the past fifty years, the market portfolio has returned 6% above the risk-free rate. Size and value portfolios have done worse. The momentum portfolio has done better. It has returned 8% above the risk-free rate. Meanwhile, the intermediary portfolio has returned 14% above the risk-free rate. Yet, its volatility is lower than either the market portfolio or the momentum portfolio! The Sharpe ratio (the ratio of a portfolio’s mean excess return to its volatility) of the intermediary portfolio is in a class of its own. It is twice as high as that of the momentum portfolio, thrice as high as that of market and value, and almost four times as high as that of the size portfolio. If there was ever going to be a compelling reason for investment professionals to start paying attention to balance sheet capacity, this is it.

Market Size Value Mom Intermediary factor
Mean excess return (annual) 6.5% 3.2% 4.4% 8.4% 14.0%
Mean excess return (qtrly) 1.6% 0.8% 1.1% 2.0% 3.3%
Volatility (qtrly) 8.4% 5.6% 5.7% 7.6% 6.0%
Sharpe ratio 18.8% 14.4% 19.2% 27.0% 56.0%

The implications of my work for macrofinance and investment strategies are interesting. But what is really interesting is what this tells us about the nature of the modern financial and economic system.

You are welcome to read and comment on my research paper here

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Markets

Where Is My Trump Instability Premium?

All evidence suggests that President Donald J. Trump is not ready to put down the bludgeon. On Monday, Trump signed an executive order to pull out of the Trans-Pacific Partnership, that the United States had signed but not ratified. He then announced his intention to renegotiate Nafta. And he all but declared a trade war against China. Given the architecture of global supply chains, a trade war with China would in effect be a trade war against all US trade partners; or at least those in the Western Pacific. A major disruption of global supply chains is a significant risk factor for global markets. US firms have come to rely rather heavily on offshore production and are themselves at risk.

Yet, the bull run in US equities shows no signs of letting up. The S&P 500 hit another all-time high today. Even before the election, stocks were clearly overvalued. What is going on here?

For months before the election, markets rose when Clinton’s fortunes improved and fell when Trump’s likelihood of reaching the White House increased. Wolfers and Zitzewitz estimated that a Trump victory would reduce the value of global equities by 10-15 percent and significantly increase expected stock market volatility.

clintonmarkets

Source: Wolfers and Zitzewitz (2016)

On election night, markets initially reacted in line with the prediction. But then a strange thing happened. Markets reversed course within hours and the great Trump rally began.

election

Source: New York Times

The Trump trade is being justified by the promised tax-cuts, infrastructure program and pro-business agenda. But these were common knowledge well before the election. Why did markets change their mind?

I had a very interesting conversation about this with the historian Adam Tooze. He said he was not surprised. In his view, financial markets are reflexive in that market participants’ subjective beliefs determine market outcomes which in turn shape participants’ beliefs and so on. In Soros’ formulation,

The participants’ views influence the course of events, and the course of events influences the participants’ views. The influence is continuous and circular; that is what turns it into a feedback loop.

As I understood it, Tooze has a thicker notion of reflexivity in mind. Specifically, market participants, strategists and commentators construct narratives to make sense of market developments. These narratives gain currency though a complex intersubjective process that is only vaguely comprehensible. They dominate the discourse for a while and at some point that cannot be predicted in advance, they relinquish their hold on the collective imagination in favor of another narrative.

This is pretty much as far as it gets from modern asset pricing. The central insight of modern asset pricing theory is that investors are compensated for bearing systematic risk and not idiosyncratic risk (which can be diversified away). An asset pricing theory in the modern sense tells us what constitutes systematic risk. A theory is entirely pinned down by specifying a vector of systematic risk factors called the pricing kernel.

For simplicity, assume that we have a single risk factor in the pricing kernel, m. Expected excess return of an asset will then be the product of the asset’s beta (the covariance of the returns on the asset with m) and the price of risk (determined by market-wide risk aversion). In the standard Capital Asset Pricing Model, for example, the price of risk is assumed to be constant and m equals the return on the market portfolio, so that the expected excess returns on a stock or a portfolio of stocks is proportional to its market beta. In contemporary intermediary asset pricing models on the other hand, the systematic risk factor is shocks to the leverage of US securities broker-dealers (Wall Street).

We should, of course, expect political risk to be priced in. Especially in times of heightened expected system-wide political instability—say due to the risk of a near-term trade war between the world’s two largest economies—expected excess returns on risky assets should be high. That is say, asset prices should be lower than otherwise warranted. Where, then, is my Trump instability premium?

I am near-certain that Tooze is onto something when he posits that participants’ emplotment of market developments reflexively drive market movements. But such narrative-driven fluctuations are bound to reverse sooner or later. When the Trump trade finally reverses, we are bound to see a large risk-off as the pendulum swings the other way and the market reprices to give me back my instability premium.

***

Bonus: Banks are leveraged bets on the economy. Banks stocks therefore tend to overperform the market in upswings and underperform them in downswings (their beta is greater than 1). But that’s actually only a small part of the story. The big part of the story is that because banks borrow short and lend long, the profitability of their marginal loan depends on the term spread. And the term spread has widened dramatically as part of the Trump reflation trade. And then, of course, you have the reassurance of adult supervision in the White House.

banks tspread

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A Whopper From the President

Wages have risen faster in real terms during this business cycle than in any since the 1970s,” according to the president. That doesn’t sound credible to anyone aware of the tepid pace of wage growth. As I’ll show, he is not even close.

We date expansions as beginning in the first quarter after an NBER recession and ending in the last quarter before the next recession. We then calculate real wages as the ratio of total wages and salaries (BEA) to total hours worked (BLS), deflated by the headline inflation index (FRED). Figure 1 shows the overall gains in real wage per hour during the expansions under the four two-term presidents since the 1970s. We see that real wages grew 11% during the Clinton expansion, whereas they have only grown at 4.2% in the Obama expansion. Indeed, Obama’s performance is even slightly worse than Bush’s 4.5%.

wages1

Figure 1.

But the president did not say wages have grown the most in his expansion; he said they have grown the fastest. Since the expansions are of different lengths—ranging from 24 quarters under Bush to 40 quarters under Clinton—perhaps the president has a point about the pace of gains in real wages?

Not even close. Figure 2 shows the annualized growth rate of wages per hour in the four expansions. We see that although the gap is narrower by this metric, the Clinton expansion still yielded significantly larger real wage gains than the Obama expansion (1.05% vs. 0.59%). And instead of being statistically tied, Bush pulls away from Obama. His expansion saw an annual increase of 0.73% in real wages per hour. Meanwhile, Reagan lags behind at 0.41%.

wages2

Figure 2.

The White House itself publishes annual estimates of real wages that are included in the Economic Report of the President. Unlike the BEA’s numbers which are for all employees, these are for blue-collar workers only (“production and nonsupervisory workers”). And because the numbers are annual, we have to make a choice of which years to include. We date our expansions from the first year after the end of an NBER recession and the last year before the next NBER recession. For example, the Clinton expansion is taken to be 1991-2000 since the first recession ended in the last quarter of 1990 and the next one began in the first quarter of 2001. We then calculate the annualized gain in real wages per hour for blue collar workers from the data provided by the White House. Figure 3 displays the results.

wages3

Figure 3.

We see that according to the president’s own numbers, blue-collar workers did nearly twice as well under Clinton than under Obama, even as the working class did twice as well under Obama than under Bush. During the Clinton expansion, blue collar wages per hour grew at the pace of 0.73% per annum, versus 0.4% per annum during the Obama expansion. Meanwhile, blue-collar workers got shafted under Ronald Reagan. Their real wages per hour fell by 2.8% between ’83 and ’89.

The bad news is that this has been the worst expansion for the middle class since Reagan. The good news is that the Clintons will be back in the White House soon.

………

Correction: An earlier version of Figure 2 displayed quarterly growth rates instead of annualized growth rates for real wages per hour in the four expansions.

Appendix. Quarterly growth in real wages since the 1970s.

wages4

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An Illustrated Guide to the US Financial Cycle

Claudio Borio of the Bank of International Settlements is one of the most interesting and original economists of the day. A key innovation of his is the concept of the financial cycle. The idea is that the excess elasticity of the financial sector has dramatic consequences for real activity. Specifically, the supply of credit to the real economy is much more elastic than macroeconomic models have hitherto assumed or would be justified by macroeconomic fundamentals. In good times credit is plentiful and even very dicey borrowers can obtain credit quite cheaply. In difficult times even worthy borrowers find it hard to secure credit.

In order to empirically capture this boom-and-bust cycle, Borio and others developed a measure that uses filtering techniques. The idea is to isolate medium frequency movements in key indicators: credit-to-GDP ratio, total credit to the private sector, and property prices. Borio showed that the comovement of these indicators captures national financial cycles for a number of countries.

Technically: Borio uses a bandpass filter to isolate cycles with length ranging from 8 to 30 years in these three variables and averages them to obtain the financial cycle. Figure 1 displays Borio’s financial cycle for the United States.

fc1

Figure 1. Source: Claudio Borio

I recomputed Borio’s financial cycle with more recent data. Figure 2 displays the US financial cycle from 1976-2015. We see that the financial cycle has turned since Borio calculated it.

fc2

Figure 2.

US housing has always been a leading indicator of economic activity. Housing-finance is the primary channel through which the excess elasticity of the financial sector propagates to real activity. In what follows, we will see that a single metric of housing-finance, namely mortgage credit-to-gdp, captures the comovement of the components of the US financial cycle quite well. Figure 3 displays raw and detrended US mortgage credit-to-GDP. We can see the extraordinary boom in the run-up to the Great Financial Crisis. Figure 4 displays filtered US mortgage credit-to-GDP from 1951-2016 (using the same bandpass filter).

boom

Figure 3.

hfc

Figure 4.

The US housing-finance cycle has become increasingly coupled to credit-to-GDP (Figure 5). It has long been coupled to property prices (Figure 6) and has become increasingly synchronized with the raw credit cycle (Figure 7).

fc8

Figure 5.

fc4

Figure 6.

fc5

Figure 7.

Figure 8 displays the comovement of the US financial cycle and the US housing-finance cycle as measured by mortgage credit-to-gdp. We can observe three closed financial cycles that can be identified either by the three peaks or the four troughs. Mortgage credit-to-GDP (the US housing-finance cycle) barely rose in the first. Then there was a discernible but mild boom in mortgage lending during the late-1980s financial boom. But in the financial boom of the 2000s the two were phase-locked; so to speak. Note the increasing amplitude of both the cycles and the rigidity of the comovement in the last cycle. The past twenty years have witnessed a coupling of the two cycles.*

fchfc

Figure 8. The US financial cycle and the US housing-finance cycle.

What explains the coupling of the financial and housing-finance cycles? One word: Securitization. Basically, the extraordinary amplitude of the financial cycle in the lead up to the Great Financial Crisis was the result of shadow lending. Figure 9, 10, and 11 show the contributions of banks and credit unions, US housing-finance agencies (“Agency MBS”), and shadow banks (“Private-label MBS”) respectively. Shadow lending accounted for 90% of the increase in mortgage credit-to-GDP during the housing-finance boom of 2003-2007.

Shadow banks here refers to finance companies, ABS issuers, and mortgage real-estate investment trusts (M-REITS), which are essentially artificial firms created by Wall Street to warehouse the raw material (mortgages) used to manufacture financial assets. Thus, securitization brought expanding dealer balance sheet capacity to the housing market and thereby amplified the US housing-finance cycle.

banks

Figure 10.

agency

Figure 11.

shadow

Figure 12.

An interesting question for future research is whether housing-finance cycles are synchronous with financial cycles more generally. That is, is this an American peculiarity or is it true of other countries as well? Another open important question is how Borio’s financial cycle relates to Rey’s global financial cycle which is defined in terms of the comovement of global asset prices.


*We know from Rognlie’s work that the growing share of capital income in total income is explained almost entirely by capital gains on real-estate. That’s a third closely-related cycle.

 

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A Monetary Explanation of the Commodities Rout

Commodity prices have fallen dramatically over the past two years. Figure 1 shows the IMF’s commodity price indices for fuels, metals and agricultual raw materials.

IndicesFigure 1: IMF Commodity Price Indices for Fuels, Metals, and Agricultural Raw Materials.

A popular explanation for the commodities rout is the slowdown in China, and more generally, the slowdown in the global economy. A second explanation, favored by Liberty Street Economics, is the strength of the dollar. Since commodities are priced in dollars, a stronger dollar requires commodity prices to fall in order for markets to clear. The dollar has strengthened is large part because the Fed has embarked on a tightening cycle even as the European and Japanese central banks are easing. Another implication of US monetary tightening is a slowdown in the creation of international dollar credit, in turn implying weaker demand for primary commodities.[1] Figure 2 shows the trade-weighted US dollar Index (Dollar Strength) and US dollar credit to non-bank non-residents (Global Dollar Liquidity), compiled by the Bank of International Settlements.

DollarIndCrFigure 2: Dollar Strength and Global Dollar Liquidity.

Commodities are also financial assets, so that their prices are affected by the market price of risk. Etula (2013) showed that the risk-bearing capacity of US broker-dealers—Wall Street banks—is an important determinant of commodity returns.[2] This is because commodity derivatives are largely traded over-the-counter (OTC); that is, in markets where dealers serve as market-makers. Greater dealer risk appetite implies lower expected commodity returns while increased dealer risk aversion implies higher expected commodity returns. Figure 3 shows Etula’s measure of broker-dealer risk appetite (Effective Risk Aversion).

ERAFigure 3: Effective Risk Aversion (detrended).

In order to understand the contributions of these different factors, I estimated linear models for quarterly changes in Fuel and Non-Fuel commodity price indices compiled by the IMF over 2000-2015.[3] I found that a parsimonious model with only three variables (Effective Risk Aversion, Dollar Strength and Global Dollar Liquidity) explains 28 percent of the variation in Fuels and 51 percent of the variation in Non-Fuels.[4] I also tried other reasonable predictors for which quarterly data is available outside paywalls. None had significant explanatory power. In particular, OECD, Chinese and EM growth rates were insignificant for both indices even at the 10 percent level.

FuelFigure 4: Contributions to YoY% changes in the Fuel Price Index.

Figure 4 shows the decomposition of year-on-year percentage changes in the Fuel Price Index. We see that Dollar Strength and Global Dollar Liquidity have been major factors pulling down energy prices. Still, there is a big residual that presumably contains the large-scale effects of geopolitical and oversupply factors. This is certainly the case with energy prices. In 2012-2015, US shale gas production increased by 10 billion cubic feet per day. In the same period, US oil production rose by 3 million barrels a day. In addition, the Saudis essentially declared a price war on their Russian and Iranian rivals as well as on American oil firms. I have no supply-side predictors in the model, meaning that if commodity prices fell due to negative supply shocks then that variation would not be captured by the model. Indeed, it would very sketchy if it did!

NonFuelFigure 5: Contributions to YoY% changes in the Non-Fuel Price Index.

Figure 5 shows the decomposition of year-on-year percentage changes in the Non-Fuel Commodities Price Index. The model performs much better for non-fuel commodities, where it is able to explain half the price variation in 2000-2015. The residuals here are much smaller, showing that a supply glut has been less of a factor for non-fuel commodities than for energy. Dollar Strength and Global Dollar Liquidity each explain roughly a quarter of the price decline in non-fuel commodities over the past two years. On the other hand, the impact of Effective Risk Aversion has been largely positive over the past two years.

The raw correlation between the fuel and non-fuel price indices in the period under consideration is 71 percent, while that of the fitted values is 98 percent.[5] But the correlation between the two residuals is still 57 percent, meaning that while some of the comovement of two series is accounted for by the monetary factors in our model, much of it still begs explanation. The obvious explanation that comes to mind is market expectations of future demand growth.[6] Since commodity prices are forward-looking, market expectations of future demand growth for commodities is likely the dominant factor driving their residual covariation. And that takes us back to China.

———

[1] Non-US banks, especially European banks, create international dollar credit by lending dollars to non-US residents.

[2] Etula, Erkko. “Broker-dealer risk appetite and commodity returns.” Journal of Financial Econometrics 11.3 (2013): 486-521.

[3] I also modeled Metals and Agricultural Raw Material Price Indices with similar results. These results are omitted here for the sake of brevity.

[4] More precisely, the independent variables in the model were one quarter lagged, detrended Effective Risk Aversion, changes in the trade-weighted dollar index, and changes in the natural log of US dollar credit to non-bank non-US residents. All independent variables were normalized to have zero mean and unit variance.

[5] This is Hamilton’s method relayed by Ben Bernanke.

[6] I considered the possibility of a supply-side cycle. If there were a detectable supply-side cycle, then the residuals would oscillate about zero. But subjecting the residuals to the runs test failed to reject the null hypothesis of randomly generated errors. This was also the case with Metals and Agricultural Raw Material Price Indices.

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The Great January Market Sell-Off Is Not Irrational

SPX

Many prominent economists seem to be scratching their heads over the recent bout of market turbulence. The consensus is captured crisply by Capital Economics: “The plunge in global stock markets does not seem to be justified by economic developments.” On the other hand, market participants and other non-academic observers are wondering whether this correction will turn into a bear market before the month is out; if it hasn’t already.

The Policy Tensor is usually more sympathetic to rigorous economists than seat-of-the-pants market observers and traders. But in this case, the economists are demonstrating their professional blinders. It is entirely possible to for a market correction to be justified without a fundamental slowdown in the domestic macroeconomy. And this is indeed what is going on.

The US stock market is not a claim on US GDP; it is a claim on US corporate profits. Profit growth can slow without a slowdown in output growth. The profits of US firms have been falling largely due to the strength of the US dollar. Continued policy divergence among the hard currency issuing central banks imply further dollar strength, thus lowering expectations of US profits in the medium term.

Moreover, while exports are only 13.5% of US GDP, the rest of the world contributes half the profits of US firms. Similarly, 97% of employment growth is now taking place in the nontradable sector, whereas the tradable sector accounts for half the growth in gross value added. So, one can have a robust and tightening labor market while profits margins get squeezed due to global disinflation and a rising dollar.

Indeed, as I have argued before, the US economy is much less resistant to an imported disinflation than an imported recession. In particular, this means that the profits of US firms are much less sheltered against global disinflation than the US economy as a whole is to recessionary headwinds from abroad. Therefore, the impact of adverse global developments on the US stock market is bound to be much more serious than on the domestic economy.

Finally, there are purely market developments that have implications for asset prices independent of the real economy. In particular, innovations in systematic volatility imply asset price adjustments regardless of what happens to GDP and inflation.

So what does the market know in January that it did not in December?

First, China: Policymakers in Beijing are clearly floundering and their ability to stem the panic is increasingly coming into question. The renminbi is depreciating faster than anticipated. Capital outflows from China are accelerating to such an extent that the $3.3 trillion cushion — down from $4 trillion — no longer seems invincible. An astonishing trillion dollars of capital has already left the mainland. Meanwhile, the industrial sector is doing even worse than expected.

Second, instead of stabilizing, the commodities rout has exacerbated. This has worsened the outlook for commodity exporters. Moreover, the continued price declines indicate a greater slowdown in global trade and output than previously recognized. To put it bluntly, the world is doing much worse than was realized in the benign aftermath of the Fed’s liftoff; a decision that looks increasingly ill-judged.

Third, it is now being realized that the banks are much more exposed to the oil rout than was assumed to be the case. All the big banks have reported substantial losses on their loan books to the sector. It is clear that these losses will mount since the price of crude is likely to be depressed in the medium term. But it is not clear by how much and that is obviously very worrying given the central role played by financial intermediaries in powering economic growth.

Fourth, market participants can be more confident that market turbulence is likely to persist. The end of the repression of systematic volatility by the Fed and the reduced liquidity due to tighter regulation of broker-dealers increases the likelihood of market turbulence. This means that risk premia will rise since investors must be compensated for greater volatility, in turn implying lower asset prices. In other words: Even if asset prices were aligned with fundamentals before and assuming that said fundamentals haven’t changed, asset prices would still need to fall to reflect the repricing of risk.

So, yes, even though expectations for US GDP and inflation have barely changed since the December meeting, the market correction is warranted.

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Markets

Hawks Take Centerstage

ACM term risk premia since 2000

Abstract: The market seems convinced that the Federal Reserve will lift-off in December, 2015. We argue that this expectation needs to be tempered because the economy begs to differ with the hawks. Fatally for the hawks’ case, the Phillips Curve is broken. And since the neutral rate is now exceptionally low and on a downward trend, the Fed’s model risk has increased considerably. The labor market continues to show slack on many indicators including decidedly tepid wage inflation. Moreover, the US economy is not nearly as resistant to an imported deflation as it is to recessionary headwinds from abroad. The baseline scenario continues to be lowflation and stagnation for some time to come. The FOMC is therefore likely to hold fire. And if it does hike in December, it would be running the risk of deflation. A premature exit would harm the recovery that is still underway in the real economy. At the very least, the Fed would be sure to miss its inflation target over the medium term.

Read the research note here (pdf). 

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