Markets

Are Shocks to Housing Priced in the Cross-Section of Stock Returns?

In the previous post I argued that the risk premium on property is due to the fact that the marginal investor in housing is your average homeowner who finds it extraordinarily hard to diversify away the risk posed by her single-family home to her balance sheet. If I am right, this means that housing wealth is a systematic risk factor that ought to be priced in the cross-section of expected stock excess returns (ie, returns in excess of the risk-free rate).

Assume that the marginal investor in the stock market is your average homeowner. Since it is so hard for her to diversify away the risk posed by fluctuations in property values, she should value stocks that do well when property markets tank. Conversely, stocks whose returns covary with returns on property should be less valuable to her. Given our assumption that your average homeowner is the marginal investor in equities, expected returns on stocks whose returns covary strongly with property returns should be higher than expected returns on stocks whose returns covary weakly (or better yet, negatively) with property returns. This is what it means for shocks to housing to be priced in the cross-section of expected stock returns.

We want our risk factor to capture broad-based fluctuations in housing wealth. Ideally, we would use a quarterly time-series for total returns (including both rent and capital gains) on housing wealth owned directly by US households. I am unaware of the existence of such a dataset—if you know where I can find the data, please get in touch.

We can also instrument fluctuations in housing wealth by using a property price index. Here we use the US property price index reported by the Bank of International Settlements. For return data we use 250 test assets from Kenneth French’s website. (The same dataset I used in my paper, “The Risk Premium on Balance Sheet Capacity.”)

We’re now going to jump straight into the results. For our econometric strategy please see the appendix at the bottom.

Figure 1 displays a scatterplot of the cross-section of expected stock returns. Along the X axis we have the betas (the sensitivity of the portfolio’s return to property returns) for our 250 test assets, and along the Y axis we have the mean excess returns of the portfolios over the period 1975-2016.

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Figure 1. Housing and the cross-section of expected stock excess returns.

Guys, this is not bad at all. Our single factor explains 20 percent of the cross-sectional variation in expected excess returns. By comparison, the celebrated Capital Asset Pricing Model, for instance, is a complete washout.

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Figure 2. The CAPM fails catastrophically in explaining the cross-section of expected excess returns.

It is very hard for single factor models to exhibit such performance. Table 1 displays the results from the second pass. We see that the mean absolute pricing error is large because the zero-beta rate does not vanish. Indeed, at 1.8 percent per quarter it is simply not credible. But the risk premium on property returns is non-trivial and significant at the 5 percent level.

Table 1. Property returns and the cross-section

Estimate Std Error p-Value
Zero-beta rate 0.018 0.006 0.002
Property return 0.007 0.004 0.049
R^2 0.195
Adj-R^2 0.192
MAPE 0.022

I have a lot of professional stake in the failure of this model actually. I have argued that stock returns are explained by fluctuations in the risk-bearing capacity of the market-based financial intermediary sector. In other words, the central thrust of my work is to say that we ought to pay less attention to the small-fry and considerably greater attention to the risk appetite of the big fish, for that is what drives market-wide risk appetite. Fortunately for my thesis, property shocks do well, but not nearly as well as balance sheet capacity.

Figure 3 displays yet another scatterplot for the cross-section. On the X axis we have the factor betas (the sensitivities of the portfolios to balance sheet capacity) and on the Y axis we have, as usual, mean excess returns over 1975-2016.

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Figure 3. Balance sheet capacity explains the cross-section of stock returns.

In Table 2 and Figure 3 we’re only looking at a single-factor model with balance sheet capacity as the sole systematic risk factor. That’s a parsimonious theory that says: exposure to fluctuations in the risk-bearing capacity of broker-dealers explains the cross-section of asset returns. The empirical evidence is pretty compelling that this is the right theory. We see that balance sheet capacity singlehandedly explains 44 percent of the cross-sectional variation in expected stock excess returns. What is also manifest is the vanishing of the zero-beta rate; and the attendant vanishing of the mean absolute pricing error. Other single factor models cannot even dream of competing with balance sheet capacity in terms of pricing error. Indeed, I have shown in my paper that even the pricing errors of standard multifactor benchmarks, Fama and French’s 3-factor model and Carhart’s 4-factor model, are significantly bigger than our single factor model’s 48 basis points. We can thus have good confidence that the evidence does not reject our parsimonious asset pricing model.

Table 2. The Primacy of Balance Sheet Capacity

Estimate Std Error p-Value
Zero-beta rate 0.002 0.010 0.440
Balance sheet capacity 0.095 0.038 0.007
R^2 0.442
Adj-R^2 0.440
MAPE 0.005

I know what you are thinking. If these things are priced in, there must be a way to make money off it. How do I get some of that juicy risk premium? Aren’t they non-traded factors? Yes, they are. But you can still harvest the risk premium on non-traded factors, eg by constructing factor mimicking portfolios. Briefly, you project your factor onto a bunch of traded portfolios and use the coefficients as weights to construct a portfolio that tracks your non-traded factor.

Figure 4 displays the risk-adjusted performance of portfolios that track benchmark risk factors and the two risk factors discussed in this essay. We report Sharpe ratios (the ratio of a portfolio’s mean excess return to the volatility of the portfolio return) rescaled by the volatility of the market portfolio for ease of interpretation.

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Figure 4. Risk-adjusted performance of traded portfolios for size, market, value, momentum, property, and balance sheet capacity.

The results are consistent with our previous findings. The stock portfolio that tracks property outperforms standard benchmarks convincingly. In turn, the portfolio that tracks balance sheet capacity outperforms the portfolio that tracks property. But let’s be very clear about what Figure 4 does not say. There is no free lunch. More precisely, there is no risk-free arbitrage.

The existence of these two risk premiums imply instead that there is risk arbitrage. That is, you can obtain superior risk-adjusted returns than the market portfolio by systematically harvesting these risk premiums. The existence of the two risk premiums is due to structural features. Specifically, the property premium exists because non-rich homeowners must be compensated for their exposure to housing; while the risk premium on balance sheet capacity exists because of structural features of the market-based financial intermediary sector—features that I explain in detail in the introduction of my paper. Since we can expect these structural features to persist, we should therefore not expect these risk premiums to vanish (or perhaps even attenuate much) upon discovery.


Appendix. Cross-Sectional Asset Pricing

We can check whether any given risk factor is priced in the cross-section of excess returns using standard 2-pass regressions where you first project excess returns \left(R_{i,t}\right) onto the risk factor \left(f_t\right) in the time series to obtain factor betas \left(\beta_i\right) for assets i=1,\dots,N,

R_{i,t}=\alpha+\beta_i f_{t}+\varepsilon_{i,t}, \qquad t=1,\dots,T,

and then project mean excess returns \left(\bar R_i\right) onto the betas in the cross-section to obtain the price of risk \lambda,

\bar R_{i}=\gamma^{0}+\lambda\hat\beta_{i}+e_{i}, \qquad i=1,\dots,N.

The scalar \gamma^{0} is called the zero-beta rate. If there is no arbitrage, the zero-beta rate must vanish. If the zero-beta rate is statistically and economically different from zero, then that is a failure of the model. That’s why the mean absolute pricing error is a better metric for the failure of an asset pricing model than adjusted-R^2. It’s given by,

\text{MAPE}:=|\gamma^{0}|+\sum_{i=1}^{N}\omega_{i}|\hat e_{i}|,

where \omega_{i} are weights that we will discuss presently.

If you try this at home, you need to know that (1) ordinary least squares (OLS) is inefficient in the sense that the estimator no longer has the lowest variance among all linear unbiased estimators; (2) OLS standard errors are an order of magnitude too low (and the estimated coefficients are attenuated, though still consistent) because their computation assumes that the betas are known, whereas we are in fact estimating them with considerable noise in the first pass.

The solution to (1) is well-known. Simply use weighted least squares (WLS) where the weights are inversely proportional to the mean squared errors of the time-series regressions,

\omega_i \propto \left[\frac1{T}\sum_{t=1}^{T}\hat\varepsilon^2_{i,t}\right]^{-1},\qquad \sum_{i=1}^{N}\omega_{i}=1.

The solution to (2) is to use errors-in-variable (EIV) corrected standard errors. In our work, we always use WLS for the second pass and report EIV-corrected standard errors wherever appropriate.

 

 

 

 

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Markets, World Affairs

The Never-Ending Greek Debt Slavery Saga Revisited

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Obedience is not enough. Unless he is suffering, how can you be sure that he is obeying your will and not his own?

George Orwell, 1984

‘To say [Varoufakis’ Adults in a Room] is the best memoir of the Eurozone crisis,’ writes Adam Tooze, ‘is an understatement.’ I would second that had I read others. Memoirs are simply not my genre. But the former Greek finance minister’s testament is a different matter altogether.

Although far from disinterested, Varoufakis is a reliable reporter from the trenches. His critique of the Troika is truly devastating. Simply put: They knew. They knew that their plan was guaranteed to fail. They knew that Greece was bankrupt and would never be able to pay back all the money that it owed. They knew that without debt relief in one form or another, there was simply no path back to sustainability. They knew that austerity was devastating the Greek economy and worsening its debt burden. They knew that pouring good money after bad was a non-solution. They, Varoufakis insists, did not even want their money back.

Yet, utilizing an impressive arsenal of Kafkaesque red tape they obstructed all potential solutions, and using all available means of diplomatic and financial coercion, arm-twisted successive Greek governments to submit to never-ending debt slavery. Why? Adam Tooze explains it best:

The main function of disciplining Greece, Varoufakis tells us, was to serve as a warning to the French of the price of fiscal indiscipline. In other words its purpose was to perpetuate and widen discipline. But that in turn was not so much an economic as a political problem. Berlin wanted to avoid the terrifyingly difficult distributional politics of even larger scale exercises in cross border bail outs and “transfers”. Holding the line in Greece was a way of containing what could have become a spiraling political disaster for the CDU and their coalition partners.

We will return to the strategic rationale for putting Greece in debtors’ prison. But first, How did we get here?

Greece was a victim of global macro forces well beyond its control. In 2001, Greece gave up monetary independence and adopted the euro. This meant that regaining lost competitiveness and correcting macro imbalances would require a real devaluation (ie, wages would have to fall in nominal terms); thus requiring an extraordinarily painful ‘structural adjustment’ programme in IMF-speak.

Bond markets responded to the advent of the euro by compressing sovereigns spreads; meaning that Greece could borrow at virtually the same interest rate as Germany. (See Figure 1.) Greece was thus able to borrow large sums from bondholders—debt that was only revealed to be unsustainable when sovereign spreads widened with the onset of the eurozone crisis.

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Figure 1. The spread between Greek and German sovereign bond yields.

At the same time, northern banks dramatically expanded their lending to Greece. Greek debt to foreign banks grew from €135 billion as of 2004Q1—surely up from a much lower level since 2001—to €217 billion in 2008Q1. (See Figure 2.)

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Figure 2. Foreign banks’ credit to Greece.

More generally, Shin (2012) has shown that a banking glut in Europe was the principal driver of the financial boom in the European periphery as well as the United States. The idea here is straightforward: Fluctuations in the risk-bearing capacity of global banks drive fluctuations in the supply of credit. But let me offer a more precise thesis.

The credit boom preceding the financial crisis in the US and peripheral Europe was the great sucking sound of the wholesale market for collateralized funding. The extraordinary expansion of mortgage credit to, say, US households was due to demand for raw material (ie, mortgages) required for the manufacture of private-label mortgage-backed securities. (There was a persistent ‘shortage of safe assets’ in the global financial system.) In 2003-07, Pozsar (2015) shows, Wall Street was hard at work feeding the machine it had constructed to intermediate between cash pools (central banks, corporate and state treasuries, money-market mutual funds, et cetera) demanding ultra-safe assets in the money markets and portfolio managers demanding risk assets for their relatively high yields in capital markets. No wonder that Mehrling et al. (2013) describe shadow banking as ‘money market funding of capital market lending’.

Note that the centrality of the wholesale funding market does not undermine the role of the dealers’ risk-bearing capacity as the main state variable (or explanatory variable). To the contrary, funding markets exist in only as much as dealers make them. Physically, the interdealer funding market—the supercore of the dealer ecosystem and hence the global financial system—is a network of phone and Internet connections between traders at global banks. The point is that Shin (2012)’s finding—that fluctuations in balance sheet capacity drive fluctuations in credit supply—is only being fleshed out here; not superseded. [Of course, the risk-bearing capacity of the sell side ought to be measured relative to the financial size of the buy side. See Farooqui (2017) for the primacy of the relative scale of balance sheet capacity in pricing the cross-section of stock returns.]

The preceding paragraphs may seem like a digression. They are anything but. For the ‘excess elasticity’ of global finance was the fundamental reason why Hollande and Merkel had to impose debt slavery on Greece. The denouement of the financial boom unleashed by the unprecedented expansion of European balance sheet capacity came when excessive bank leverage met mounting losses on subprime loans. Germany and France could not acknowledge the scale of the bailout required by the banks to their audiences at home. They had to be bailed out without recourse to more public funds. While American policymakers used the AIG bailout to secretly bail out Goldman Sachs and JP Morgan, the Europeans used the Greek bailout to secretly bail out French, German and Dutch banks.

The [big] three French banks’ loans to the Italian, Spanish and Portuguese governments alone came to 34 per cent of France’s total economy – €627 billion to be exact. For good measure, these banks had in previous years also lent up to €102 billion to the Greek state.…

Why did Deutsche Bank, Finanzbank and the other Frankfurt-based towers of financial incompetence need more? Because the €406 billion cheque they had received from Mrs Merkel in 2009 was barely enough to cover their trades in US-based toxic derivatives. It was certainly not enough to cover what they had lent to the governments of Italy, Ireland, Portugal, Spain and Greece – a total of €477 billion, of which a hefty €102 billion had been lent to Athens. [The €102 billion in this quote is quite likely a typo—that’s the French banks’ exposure to Greece. As Varoufakis tells us later, the German banks’ exposure was €119 billion.] 

So, of every €1000 handed over to Athens to be passed on to the French and German banks, Germany would guarantee €270, France €200, with the remaining €530 guaranteed by the smaller and poorer countries. This was the beauty of the Greek bailout, at least for France and Germany: it dumped most of the burden of bailing out the French and German banks onto taxpayers from nations even poorer than Greece, such as Portugal and Slovakia. 

This disturbing transformation of the banking crisis in the northern core into a sovereign debt crisis on the periphery was accomplished in the very first phase of the eurozone crisis; well before Varoufakis arrived on the scene.

As soon as the bailout loans gushed into the Greek finance ministry, ‘Operation Offload’ began: the process of immediately siphoning the money off back to the French and German banks. By October 2011, the German banks’ exposure to Greek public debt had been reduced by a whopping €27.8 billion to €91.4 billion. Five months later, by March 2012, it was down to less than €795 million. Meanwhile the French banks were offloading even faster: by September 2011 they had unburdened themselves of €63.6 billion of Greek government bonds, before totally eliminating them from their books in December 2012. The operation was thus completed within less than two years. This was what the Greek bailout had been all about.

Thereafter, the European strategy was to enact a morality play to cover up the crime; complete with bloodletting—aka austerity—and moral sermons blaming the victim. The Troika’s treatment of Greece was tantamount to economic warfare. By the time Varoufakis got in the cockpit, the Europeans had developed the full apparatus of control. The reason he found the Troika to be Kafkaesque, is that the insiders were committed to controlling the narrative. They could not negotiate honestly with Varoufakis both because they feared the markets and because they would then be admitting guilt. While some individuals involved in the ‘institutions’ even admitted the crime to Varoufakis, institutionally the Troika was designed to bury the dirty little secret.

This is, of course, not to disagree with Adam Tooze about the role played by political constraints in Berlin, Paris, and indeed, Washington. Indeed, political constraints are precisely what drove the bank bailouts underground and started the Greek Debt-Slavery Saga.

VAROUFAKIS SAYS he had a financial deterrent to get Draghi to back off from financial strangulation and give him breathing room.

[The €33 billion] Greek debt to the ECB were legally momentous: any haircut of that sum or delay in its repayment would open Draghi and the ECB up to legal challenges from the Bundesbank and the German Constitutional Court, undermining the credibility of its overall debt-purchasing programme and causing a rift with Chancellor Merkel, who would never take on both the Bundesbank and the German Constitutional Court at the same time. Facing their combined might, Draghi was sure to find his freedom drastically curtailed, thus undermining the markets’ faith in his hitherto magical promise to do ‘whatever it takes’ to save the euro – the only thing preventing the currency’s collapse. 

‘Mario Draghi is about to unleash a major debt purchasing programme in March 2015, without which the euro is toast,’ I said. ‘The last thing he needs is anything that will impede this.’…I had no doubt that if a Syriza government signalled early on its intention to retaliate by haircutting the Greek SMP bonds held by the ECB in this way, it would deter the ECB from closing down the banks.

Calling the deterrent “potentially very powerful,” Tooze reports that

…the faction within the Tsipras cabinet that wanted to avoid a break was too strong. Varoufakis was never allowed to make the critical threat at the right moment. Greece was driven to a humiliating compromise without ever having deployed its deterrent.

Game-theoretically speaking, whether Varoufakis’ deterrent was effective cannot be answered without knowledge of the preferences of other players. Even if Draghi himself could be deterred, as indeed seems likely, that was only going to grant Greece a short term lease on life. There is no reason to believe that it would’ve forced the Troika to negotiate in earnest. A Greek threat to activate the deterrent could just as easily have yielded a Schäuble solution with the Troika turning the screws to push Greece out of the eurozone in the service of discipline. We cannot answer that without knowing just how much Merkel feared Grexit.

A distinct possibility is that the deterrence strategy was leaked to the Germans by someone in the Greek war cabinet—infiltrated as it was by the Troika—and that Merkel let it be known to Tsipras that the activation of the threat, or perhaps even its deployment against Draghi, would harden the Troika’s stance. What I mean to suggest is this: Tsipras is not a scoundrel. Why did he capitulate if the deterrent was in fact effective? Did he know something about the preferences of Greece’s jailers that Varoufakis did not? Could it be that Varoufakis’ dirty bomb was a recipe for tactical victory but strategic defeat? Is that why Tsipras capitulated instead of deploying the deterrent?

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Markets

The Tendency of Commodity Prices to Fall Over the Long Run

The Prebisch-Singer hypothesis states that real commodity prices have a tendency to fall over the long run. Harvey et al. have shown that the historical evidence from four centuries of commodity price data is consistent with the hypothesis. The most convincing explanation of the phenomena is that, with important exceptions, the income elasticity of primary commodities is less than 1. This means that incomes grow faster than demand for primary commodities, so that prices must fall to clear markets. This mechanism is subject to ecological constraints since if the ecological constraint bites, prices must rise to clear markets. So far, fundamental ecological limits have not trumped the tendency of commodity prices to fall over the long run. The tendency introduces a dynamic, systematic bias in favor of the core and against the periphery of the world economy since, by construction, nations or regions specializing in primary goods belong to the latter. In what follows, I’ll illustrate the tendency for a large number of commodity classes since 1850. All data is from David S. Jacks’s website.

Overall, the picture is that non-fuel commodity prices have fallen. Figure 1 shows the unweighted average of 37 non-fuel commodities. While there is a medium term cycle, the trend is clearly negative.

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Figure 1. Non-fuel real commodity prices (1850-2010).

The main exception is energy prices, which have shown a tendency to rise over the long run. See Figure 2.

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Figure 2. Real energy prices (1850-2010).

We should not expect the real price of gold (or other precious metals) to fall over the long run because it was, and still approximately is, the numeraire. Indeed, there is no long term trend in precious metal prices. See Figure 3.

 

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Figure 3. Precious metal prices (1850-2010).

Figure 4 shows the price of beef and other animal products. We see that even though pork and hide prices have fallen over the long run, the price of beef (and lamb) have increased significantly. This is because the income elasticity of beef is greater than 1 since it is not yet a necessity for the bulk of global households. Indeed, as mass affluence spreads around the world, we should expect the demand for beef to grow faster than global income.

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Figure 4. Animal product prices (1850-2010).

Let’s move to proper commodities starting with grains. Growing wheat, rice and corn is the mainstay of the world’s farmers. The real price of their product has been falling systematically over the past 150 years. See Figure 5.

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Figure 5. Grain prices (1850-2010).

I know what you are thinking: These farmers ought to plant cash crops such as cotton. Not so fast. The prices of cash crops have also fallen just as much if not more than grains. Figure 6 shows non-food soft commodities.

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Figure 6. Non-food soft commodity prices (1850-2010).

Figure 7 shows the systematic decline in the price of “drug foods” that played such an important role in the early modern world economy.

 

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Figure 7. Drug food prices (1850-2010).

What about metals and minerals? Are commodities that are dug out lucky as a class? The evidence does not support that conclusion. Figure 8 shows metal prices. The data is noisier but the common trend component is clear.

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Figure 8. Metal prices (1850-2010).

Figure 9 shows the prices of minerals such as iron ore and sulphur. Here the trend is even more manifest.

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Figure 9. Mineral prices (1850-2010).

I hope to have convinced you of the tendency of commodity prices to fall over the long run. The finding raises the stakes for the politics of global inequality and the international division of labor. But trend analysis is merely the first step. For a full analysis of the role of commodities in the global condition, we have to look at how both the terms of trade between the town and the countryside (defined in terms of price levels) and the volatility of commodity prices affects commodity producers and informs their politics.

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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 12% above the risk-free rate. Yet, it has the lowest volatility! 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 Momentum Intermediary
Mean excess return (annual) 6.5% 3.2% 4.4% 8.4% 12.1%
Mean excess return (qtrly) 1.6% 0.8% 1.1% 2.1% 2.9%
Volatility 8.4% 5.6% 5.7% 7.6% 5.1%
Skewness -48.1% 23.5% 57.9% -64.5% -44.9%
Sharpe ratio 18.8% 14.4% 19.1% 27.0% 57.3%

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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Markets

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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