Economist Channel

Interest rates in the cash market and the spot exchange rate can be taken as given – these markets are much larger than those for FX derivatives. Hence, it is primarily shifts in the demand for FX swaps or currency swaps that drive forward exchange rates away from CIP and result in a non-zero basis 

[the paper] focuses on one key source of pressure on the basis, namely net foreign currency hedging demand that is largely insensitive to the size of the basis...  

A first, structural source of demand for foreign currency hedges arises from
banks’ business models. For a long time, banks have been the main players running
currency mismatches on their balance sheets (managed mainly via swaps)... 

The second source of demand arises from the strategic hedging decisions of
institutional investors, such as insurance companies and pension funds.. 

The third source of demand arises from non-financial firms’ debt issuance across
currencies as they seek to borrow opportunistically in markets where credit spreads
are narrower...

Recently, for instance, many US firms needing dollars have been issuing in euros to take advantage of very attractive spreads in that currency and have then swapped the proceeds into dollars ..

This is a more satisfying story to me than the story that the exchange rate and interest rates are under pressure from people doing the carry trade -- borrowing cheap and lending dear unhedged. The carry trade only exists because some other flow is pushing rates in the opposite direction, and then my head starts to spin on all these "demands" for specific securities. An underlying demand for FX hedging that makes a bit more sense.


The dollar, bank leverage and the deviation from covered interest parity by Stefan Avdjiev, Wenxin Du, Catherine Koch and Hyun Song Shin has a gorgeous fact: (picture below)

Deviations from CIP turn on the strength of the dollar; when the dollar strengthens, the deviation from CIP becomes larger.

The interpretation is a little less clear to me, but I didn't spend enough time with the paper

the value of the dollar plays the role of a barometer of risk-taking capacity in capital markets.... 

To the extent that CIP deviations turn on the constraints on bank leverage, our results suggest that the strength of the dollar is a key determinant of bank leverage.

Why do the CIP deviations narrow when the domestic currency strengthens against the dollar? Underpinning this relationship is the role of bank leverage and cross-border
bank lending in dollars. Indeed, we will show the existence of a “triangle” that coherently ties together (i) the value of the dollar (ii) the cross-currency basis and (iii) cross-border border bank lending. In this triangle, a depreciation of the dollar is associated with greater borrowing in dollars by non-residents.

I can see many less institutional stories associated with such a big fact.
On the second question, why don't arbitrageurs do a better job, Darrell Duffie The covered interest parity conundrum  explains balance sheet constraints and debt overhang well. If an arbitrage opportunity transfers more wealth to creditors, by making debt safer, than it makes for equity holders, the equity holders might not do it.

A CIP basis trade may require an expansion of abank’s balance sheet, which, in turn, implies an increase in the amount of capital required by regulation. Adding capital can benefit a bank’s creditors at the expense of its shareholders. [Example follows]  

That is, by making the balance sheet safer through increased capital, with essentially no increase in risk, the trade has shifted some of the value of the bank’s assets away from its equity owners to its creditors. The equity owners have given up 12bp to creditors, so the bank should only be willing to do the trade if the magnitude of the CIP basis is at least 12bp, net of transactions costs. 

...even a true arbitrage is not necessarily attractive to bank shareholders. 

A commonly stated rule of thumb is that a balance-sheet-expanding trade is justified if it generates a profit in excess of the bank’s return on equity (ROE) multiplied by the amount of capital required for the trade. This is not correct. If it were, then a riskless arbitrage would have the same required profit per unit of capital as a risky real estate loan. That makes no sense.

Also, as last time,

That regulatory capital constraints play a big role in the CIP basis is buttressed by the fact that

the magnitude of the basis in some currencies, especially yen, increases sharply at quarterends,
when non-US banks are checked for capital adequacy.

Segmented Money Markets and Covered Interest Parity Arbitrage Dagfinn Rime, Andreas Schrimpf,  ad Olav Syrsatd also looks more at the costs of arbitrage

[ First,] measures of CIP deviations based on Overnight-Index-Swaps (OIS) rates, General Collateral (GC) repo and Inter-Bank Offer Rates (IBOR) do not adequately account for the cost of trading faced by a typical arbitrageur in this market. In particular, such common measures of CIP deviations do not adequately account for risk factors related to the usage of balance sheet.

Second, we find that once arbitrageurs’ marginal funding costs are adequately reflected via the choice of interbank money market rates, any alleged profits from CIP arbitrage entirely vanish (or are only detectable in short-lived stress episodes).

Third, we find that arbitrage opportunities in international money and FX swaps markets are economically viable for only a confined set of market participants.

Finally, we show that any potential CIP arbitrage opportunities based on non-bank money market funding sources are significantly reduced if we base the analysis of arbitrage profits on the US dollar commercial paper rate for lower-rated banks.

One of the big questions as I see it is whether "balance sheet constraints" -- not enough capital -- or "funding constraints" -- can't borrow at rates to make it attractive -- are crucial for limiting arbitrage. This seems to say a bit of both. (Their notion of funding cost seems to include the shadow value of capital and other risk constraints.)  It is nice verification that only big banks can do this trade.

Keep in mind some of these papers focus on different issues. For example, the end of quarter spike is hard to explain by direct funding costs, unless funding costs also spike at quarter ends, which I don't think they do.

The third question is, why don't non-arbitrageurs, simple long-only investors, go buy from the cheaper source, thereby pushing prices back in line?  None of the papers address this question, but maybe because the answer is so obvious. We are still talking about small differentials. If you can get 50 basis points -- 0.5% -- more on your money by investing abroad with a FX hedge, that might not seem worth the bother for a checking account, at least in the short run, and at least in a dynamic way. A money market fund, which would have to have floating values, advertising it follows this strategy might have a hard time competing with a high yield fund. Yes, you and I know that one is an arbitrage (less counterparty and funding risks) and the other has some credit risk (less the chance of bailouts), but that might take a while to convince investors. A 50 bp arbitrage opportunity is a huge profit to someone who can lever it many times, but not a huge difference to long-only investors. I still get 0.01% on my checking account, even though Chase turns it around and gets 0.75% interest on excess reserves with the money. That's a bigger spread.

That fact also raises the question just how important this is. That you can get 50 bp more or less in various short term credit instruments is interesting, but how much social cost is there?

The February Issue of the Journal of Finance made it to the top of my stack, and it has a lot of good articles. The first two especially caught my attention, Who Are the Value and Growth Investors? by Sebastien Bertermeier, Larent Calvet, and Paolo Sodini, and Asset Pricing Without Garbage by Tim Kroencke. A review, followed by more philosophical thoughts.

I  Bertermeier, Calvet, and Sodini. 

Background: Value stocks (low price to book value) outperform growth stocks (high price to book value). Value stocks all move together -- if they fall, they all fall togther -- so this is a "factor risk" not an arbitrage opportunity. But who would not want to take advantage of the value factor? This is an enduring puzzle.


Fama and French offered one of the best paragraphs in finance as a suggestion:

One possible explanation is linked to human capital, an important asset for most investors. Consider an investor with specialized human capital tied to a growth firm (or industry or technology). A negative shock to the firm's prospects probably does not reduce the value of the investor's human capital; it may just mean that employment in the firm will expand less rapidly. In contrast, a negative shock to a distressed [value] firm more likely implies a negative shock to the value of specialized human capital since employment in the firm is more likely to contract. Thus, workers with specialized human capital in distressed firms have an incentive to avoid holding their firms' [value] stocks. If variation in distress is correlated across firms, workers in distressed firms have an incentive to avoid the stocks of all distressed firms. The result can be a state-variable risk premium in the expected returns of distressed stocks.

But nobody has seen these investors, who shun value stocks despite their high average return, because value stocks are correlated with those investors' human capital. Value funds tend not to have many customers who come in, learn about the value/growth premium and factor and say "thanks, I'd like to short value" (Lots want to buy hot growth stocks, but hedging is probably not directly on their minds, and it takes a pretty strong "as if" argument to ignore that)

Enter Bertermeier, Calvet, and Sodini.

we examine value and growth investments in a highly detailed administrative panel that contains the disaggregated holdings and socioeconomic characteristics of all Swedish residents between 1999 and 2007. 

Value investors are substantially older, are more likely to be female, have higher financial and real estate wealth, and have lower leverage, income risk, and human capital than the average growth investor. By contrast, men, entrepreneurs, and educated investors are more likely to invest in growth stocks.

 over the life cycle, households climb the “value ladder,” that is, gradu- ally shift from growth to value investing as their investment horizons shorten and their balance sheets and human capital evolve.

...we find that a single macroeconomic factor—per-capita national income growth— explains on average 88% of the time-series variation of per-capita income in any given two-digit SIC industry. Households employed in sectors with high exposure to the macroeconomic factor tend to select portfolios of stocks and funds with low value loadings. ... Furthermore, we show that cross-sectoral differences in loadings are more pronounced for young households than for mature households, consistent with the intuition that human capital risk is primarily borne by the young. As a result, the value ladder is empirically steeper in more cyclical industries.

...More financially secure households should generally be better able to tolerate investment risk .. Consistent with these predictions, we document that households with high financial wealth, low debt, and low background risk tend to invest their financial wealth aggressively in risky assets and select risky portfolios with a value tilt.

The numbers seem big to me. For example, Figure 2:



-0.3 to 0.3 loadings on HML are quite large. Most value mutual funds don't get that big. (HML is lolg value and short growth)

Overall,

The patterns we uncover appear remarkably consistent with the portfolio implications of risk-based theories. 

To be fair, the authors offer behavioral interpretations as well,

we find that sentiment-based explanations of the value premium also help explain the portfolio evidence. Overconfidence, which is more prevalent among men than women (Barber and Odean (2001)), is consistent with the growth tilt of male investors. [JC, yes, but that's pretty weak. Men and women also have different human capital paths on average.] As attention theory predicts (Barber and Odean (2008)), a majority of direct stockholders hold a small number of popular stocks. Furthermore, some of the portfolio evidence can be explained by complementary risk-based and psychological stories. For instance, the growth tilt of entrepreneurs can be attributed both to exposure to private business risk (Heaton and Lucas (2000), Moskowitz and Vissing-Jørgensen (2002)) and to marked overconfidence in own decision-making skills (Busenitz and Barney (1997))

But I'm interested that all of these are "alternative explanations" of things that also have portfolio interpretations, not puzzling facts that have no portfolio interpretation, which is the usual bread and butter of behavioral finance. (It looks a lot like defense against referees to me!)

II Kroencke:

Background: The main question of asset pricing is, why do some assets reliably earn higher returns, on average, than others? The answer is, compensation for risk. Our benchmark model says this: People in Fall 2008 were really unhappy that just as their jobs and businesses were in trouble, and just as they were cutting back on consumption expenditures, their stock portfolios fell too. How nice it would have been if stocks rose on the occasion, and so could buffer other misfortune. In turn, that means people will, ahead of time, shy away from stocks that are likely to fall more in bad times, lowering their prices and raising their average returns. In sum, our baseline model is

Expected return - risk free rate = (risk aversion coefficient) x (covariance of return with consumption growth)

This model does work, qualitatively. Stocks covary with consumption growth more than bonds. However, the measured covariances are small, so the risk aversion coefficient you need to get this to work is absurdly high -- 50 or more. Such people don't get out of bed in the morning for fear of anvils falling from the sky.

For a long time, -- since this model emerged in the early 1980s -- we've recognized that some of the trouble may lie with measured consumption growth. Kroencke has a good review of the many attempts to get around this project. Two stand out worth mentioning here. Alexi Savov wrote the beautiful Asset Pricing with garbage. More consumption means more garbage, and data on garbage are in some ways (more below) cleaner than data on consumption. The standard model works a lot better using garbage to measure consumption.

Another long time favorite of mine is Ravi Jagannathan and Yong Wang's "Lazy investors..." paper, which is great except for the title in my opinion. They used fourth quarter to fourth quarter consumption growth rather than the usual monthly consumption growth. Surely asset prices are not driven by who goes up and down at lunch time. Similarly, it only takes a moment's thought to realize that monthly consumption numbers are poorly measured for this purpose. An especially nice feature of Jagannathan and Wang don't really make progress on the equity premium. But covariances with fourth quarter to fourth quarter consumption growth explain the value premium nicely, a tougher puzzle really (see above!)

As Korencke puts it

using fourth-quarter to fourth-quarter consumption is a straightforward way to mitigate time-aggregation and to bring the data closer to point consumption growth as well  

Now, Kroencke. Your first instinct might be "measurement error," but that isn't necessarily a problem

Observable consumption is subject to measurement error, which is uncorrelated with stock market returns. From an asset pricing perspective, observable consumption growth would be eligible to measure the consumption risk of stock returns, that is, should produce unbiased estimates of consumption covariances. 

Let me unpack that. Suppose consumption growth has a measurement error uncorrelated with anything. Then

covariance(return, measured consumption growth) = covariance[return, (true consumption growth + measurement error)] 

but if measurement error is uncorrelated with everything, it's also uncorrelated with returns, and

covariance(return, measured consumption growth) = covariance[return, true consumption growth] 

However, NIPA statisticians do not attempt to provide a consumption series to measure stock market consumption risk. Instead, they try to estimate the level of consumption as precisely as possible. As a result, they optimally filter observable consumption to generate their series of reported NIPA consumption. 

This is a beautiful and deep insight. The problem is not "error." The problem (pervasive in finance) is that the data are collected for another purpose.

Example: Suppose you are a government statistician, and you are asked to provide numbers on consumption, whose levels are as accurate as possible. You have consumption on Monday = $200, and consumption on Wednesday = $210. You don't have data for Tuesday. What do you report? $205 of course! That's the best guess you have of the level of consumption.

But asset pricing demands the growth rate of consumption. And asset pricing is very sensitive about timing.  If we shift all consumption measures one period forward or backward in time, the level measurement is not far off. But that destroys the correlation of consumption growth rates with anything else.

This is a pervasive problem in finance. Venture capital, private equity, university endowments or any other institution holding illiquid assets does rightly the same thing. Real estate values have the same problem. Suppose you see a true market value $200 on Monday and $210 on Wednesday. What do you report for Tuesday? Well, $205 of course. That is the best guess of the level of the asset on Tuesday. But a time series of such guesses grossly understates the volatility of the assets, makes returns artificially serially correlated (if you fill in from Monday through Friday, it will seem like a positive return every day), and destroys their correlation or betas with other assets. Beware using numbers for unintended purposes. Beware the Sharpe ratios of illiquid assets.

On top, filtering is intensified by the well-known bias stemming from time-aggregation

NIPA consumption is the total consumption over the month (at best). If you correlate that consumption with asset returns from last day of previous month to last day of this month, you're making a mistake.

Kroencke "unfilters" consumption data. He uses a nice model of how the BEA filters the data, a more complex version of my Monday-Wednesday example, to make a good guess of what the data looked like before filtering, i.e. what underlying growth rates really are. (You can't do this in my example, but suppose my example was, you observe Monday $200, Wednesday $210, and you have data for some components but are missing others for Tuesday. To measure growth rates and correlations, you would not use the Monday and Wednesday data as much, and would rely more on the partial observations for Tuesday)

The results?

unfiltered NIPA consumption is able to explain the equity premium together with constant relative risk aversion (CRRA) preferences with a coefficient of relative risk aversion between 19 and 23 in the postwar period (1960–2014),  

unfiltered NIPA consumption can explain a substantial fraction of the average returns of decile portfolios sorted by size, book-to-market, and investment growth.

Alas, Kroencke didn't make any nice average return vs. covariance plots for the blog, so you'll have to go read the tables.

There is another, rather dramatic point that surfaces early and its impact explained toward the end.  Unfiltered consumption data look a lot more like a random walk. 
This is the "variance ratio" graph. A random walk has a flat line. An upward sloping line means positive serial correlation -- high growth this year is likely to be followed by high growth next year. A downward sloping line means negative serial correlation. The variance ratio is particularly good at detecting long-run unstructured mean reversion.

You knew that filtering would lead to spurious positive serial correlation in consumption growth. How much? All of it!

The random walk in consumption (going back to Bob Hall's beautiful paper) is a very nice intuitive result. If you know you're going to be better off in the future, go out to dinner now. Consumption should be like stock prices.

It matters particularly now, in the context of the "long run risks" model, for a very prominent example
Banal Kiku and Yaron Long run risks. That whole model depends on the idea that long run risks are larger than short run risks, which they infer from the positive serial correlation of consumption growth. If consumption is a random walk, long run risks collapse to power utility. (More in a recent review.)

(To be fair, this criticism addresses the univariate properties of consumption. It is possible for a series to be a random walk in its univariate representation, but forecastable by other variables. Stock returns themselves are a great example, a nearly perfect univariate uncorrelated process, but forecastable by price-dividend ratios. So, the next round of "long run risks" may well find long run consumption growth forecastability from other variables, like P/E ratios.)

Thoughts

Why do I like these papers so much? I guess in part, they confirm my priors. One has to be honest. But that is a terrible reason to like and blog about papers. The blogosphere is full of "studies show that" whatever point one wants to flog today.

I like them because I think they're well done, and make the case convincingly.

Most of all, I like them because they show how after long and patient work, involving taking data really seriously, phenomena that seem like "puzzles," needing to be addressed by new and inventive theories, really are not puzzles, and explainable by simple economics.

This is "normal science" at its best. Looking back on the history of science, over and over, observations seemed not to fit good theories, and resulted in hundreds of new and creative "explanations." Once in a great while those puzzles result in dramatically new theories, which we celebrate. But far more often, after decades, and centuries at times, dogged persistent work showed how indeed things work as you think they might, and the original simple theory was right after all. The "rejections" of the consumption based model started around 1980. It took a long time to see the glass is not completely empty.

Everyone wants to be the "paradigm shifter," and the journals have about 10 new theories in every issue. Of which 9.99 are soon forgotten.

Part of my psychological makeup, part of what attracted me to economics all along, are the far more frequent cases in which dogged work shows how supply and demand indeed explain all sorts of puzzles.

I like "normal science." And I think we should celebrate it more.