Streetwise Professor

September 18, 2018

He Blowed Up Real Good. And Inflicted Some Collateral Damage to Boot

I’m on my way back from my annual teaching sojourn in Geneva, plus a day in the Netherlands for a speaking engagement.  While I was taking that European non-quite-vacation, a Norwegian power trader, Einar Aas, suffered a massive loss in cleared spread trades between Nordic and German electricity.  The loss was so large that it blew through Aas’ initial margin and default fund contribution to the clearinghouse (Nasdaq), consumed Nasdaq’s €7 million capital contribution to the default fund, and €107 million of the rest of the default fund–a mere 66 percent of the fund.  The members have been ordered to contribute €100 million to top up the fund.

This was bound to happen. In a way, it was good that it happened in a relatively small market.  But it provides a sobering demonstration of what I’ve said for years: clearing doesn’t eliminate losses, but affects the distribution of losses.  Further, financial institutions that back CCPs–the members–are the ultimate backstops.  Thus, clearing does not eliminate contagion or interconnections in the financial network: it just changes the topology of the network, and the channels by which losses can hit the balance sheets of big players.

Happening in the Nordic/European power markets, this is an interesting curiosity.  If it happens in the interest rate or equity markets, it could be a disaster.

We actually know very little about what happened, beyond the broad details.  We know Aas was long Nordic power and short German power, and that the spread widened due to wet weather in Norway (which depresses the price of hydro and reduces demand) and an increase in European prices due to increases in CO2 prices.  But Nasdaq trades daily, weekly, monthly, quarterly, and annual power products: we don’t know which blew up Aas.  Daily spreads are more volatile, and exhibit more extremes (kurtosis), but since margins are scaled to risk (at least theoretically–more on this below) what matters is the market move relative to the estimated risk.  Reports indicate that the spread moved 17x the typical move, but we don’t know what measure of “typical” is used here.  Standard deviation?  Not a very good measure when there is a lot of kurtosis (or skewness).

I also haven’t seen how big Aas’ initial margins were.  The total loss he suffered was bigger than the hit taken by the default fund, because under the loser-pays model, the initial margins would have been in the first loss position.

The big question in my mind relates to Nasdaq’s margin model.  Power price distributions deviate substantially from the Gaussian, and estimating those distributions is challenging in part because they are also conditional on day of the year and hour of the day, and on fundamental supply-demand conditions: one model doesn’t fit every day, every hour, every season, or every weather enviornment.  Moreover, a spread trade has correlation risk–dependence risk would be a better word, given that correlation is a linear measure of dependence and dependencies in power prices are not linear.  How did Nasdaq model this dependence and how did that impact margins?

One possibility is that Nasdaq’s risk/margin model was good, but this was just one of those things.  Margins are set on the basis of the tails, and tail events occur with some probability.

Given the nature of the tails in power prices (and spreads) reliance on a VaR-type model would be especially dangerous here.  Setting margin based on something like expected shortfall would likely be superior here.  Which model does Nasdaq use?

I can also see the possibility that Nasdaq’s margin model was faulty, and that Aas had figured this out.  He then put on trades that he knew were undermargined because Nasdaq’s model was defective, which allowed him to take on more risk than Nasdaq intended.

In my early work on clearing I indicted that this adverse selection problem was a concern in clearing, and would lead CCPs–and those who believe that CCPs make the financial system safer–to underestimate risk and be falsely complacent.  Indeed, I argued that one reason clearing could be a bad idea is that it was more vulnerable to adverse selection problems because the need to model the distribution of gains/losses on cleared positions requires detailed knowledge, especially for more exotic products.  Traders who specialize in these products are likely to have MUCH better understanding about risks than a non-specialist CCP.

Aas cleared for himself, and this has caused some to get the vapors and conclude that Nasdaq was negligent in allowing him to do so.  Self-clearing is just an FCM with a house account, but with no client business: in some respects that’s less risky than a traditional FCM with client business as well as its own trading book.

Nasdaq required Aas to have €70 million in capital to self-clear.  Presumably Nasdaq will get some of that capital in an insolvency proceeding, and use it to repay default fund members–meaning that the €114 million loss is likely an overestimate of the ultimate cost borne by Nasdaq and the clearing members.

Further, that’s probably similar to the amount of capital that an FCM would have had to have to carry a client position as big as Aas’.   That’s not inherently more risky (to the clearinghouse and its default fund) than if Aas had cleared through another firm (or firms).  Again, the issue is whether Nasdaq is assessing risks accurately so as to allow it to set clearing member capital appropriately.

But the point is that Aas had to have skin in the game to self-clear, just as an FCM would have had to clear for him.

Holding Aas’ positions constant, whether he cleared himself or through an FCM really only affected the distribution of losses, but not the magnitude.  If Aas had cleared through someone else, that someone else’s capital would have taken the hit, and the default fund would have been at risk only if that FCM had defaulted.  But the total loss suffered by FCMs would have been exactly the same, just distributed more unevenly.

Indeed, the more even distribution that occurred due to mutualization which spread the default loss among multiple FCMs might actually be preferable to having one FCM bear the brunt.

The real issue here is incentives.  My statement was that holding Aas’ positions constant, who he cleared through or whether he cleared at all affected only the distribution of losses.  Perhaps under different structures Aas might not have been able to take on this much risk.  But that’s an open question.

If he had cleared through another FCM, that FCM would have had an incentive to limit its positions because its capital was at risk.  But Aas’ capital was at risk–he had skin in the game too, and this was necessary for him to self-clear.  It’s by no means obvious that an FCM would have arrived at a different conclusion than Aas, and decided that his position represented a reasonable risk to its capital.

Here again a key issue is information asymmetry: would the FCM know more about the risk of Aas’ position, or less?  Given Aas’ allegedly obsessive behavior, and his long-time success as a trader, I’m pretty sure that Aas knew more about the risk than any FCM would have, and that requiring him to clear through another firm would not have necessarily constrained his position.  He would have also had an incentive to put his business at the dumbest FCM.

Another incentive issue is Nasdaq’s skin in the game–an issue that has exercised FCMs generally, not just on Nasdaq.  The exchange’s/CCP’s relatively thin contribution to the default fund arguably reduces its incentive to get its margin model right.  Evaluating whether Nasdaq’s relatively minor exposure to default risk led it to undermargin requires a more thorough analysis of its margin model, which is a very complex exercise which is impossible to do given what we know about the model.

But this all brings me back to themes I flogged to the collective shrug of many–indeed almost all–of the regulatory and legislative community back in the aftermath of the Crisis, when clearing was the silver bullet for future crises.   Clearing is all about the allocation and pricing of counterparty credit risk.  Evaluation of counterparty credit risk in a derivatives context requires a detailed understanding of the price risks of the cleared products, and dependencies between these price risks and the balance sheet risks of participants in cleared markets.  Classic information problems–adverse selection and moral hazard (too little skin in the game)–make risk sharing costly, and can lead to the mispricing of risk.

The forensics about Aas blowing up real good, and the lessons learned from that experience, should focus on those issues.  Alas, I see little recognition of that in the media coverage of the episode, and betting on form, I would wager that the same is true of regulators as well.

The Aas blow up should be a salutary lesson in how clearing really works, what it can do, and what it can’t.   Cynic that I am, I’m guessing that it won’t be.  And if I’m right, the next time could be far, far worse.

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September 5, 2018

Nothing New Under the Sun, Ag Processing and Trading Edition

Filed under: Commodities,Economics,Politics,Regulation — cpirrong @ 2:30 pm

New Jersey senator Corey Booker has introduced legislation to impose “a temporary moratorium on mergers and acquisitions between large farm, food, and grocery companies, and establish a commission to strengthen antitrust enforcement in the agribusiness industry.”  Booker frets about concentration in the industry, noting that the four-firm concentration ratios in pork processing, beef processing, soybean crushing, and wet corn milling are upwards of 70 percent, and four major firms “control” 90 percent of the world grain trade.

My first reaction is: where has Booker been all these years?  This is hardly a new phenomenon.  Exactly a century ago–starting in 1918–in response to concerns about, well, concentration in the meat-packing industry, the Federal Trade Commission published a massive 6 volume study of the industry  The main theme was that the industry was controlled by five major firms.  A representative subject heading in this work is “[m]ethods of the five packers in controlling the meat-packing industry.”  “The five packers” is a recurring refrain.

The consolidation of the packing industry in the United States in the late-19th and early-20th centuries was a direct result of the communications revolution, notably the development of railroads and refrigeration technology that permitted the exploitation of economies of scale in packing.   The industry was not just concentrated in the sense of having a relatively small number of firms–it was geographically concentrated as well, with Chicago assuming a dominant role in the 1870s and later, largely supplanting earlier packing centers like Cincinnati (which at one time was referred to as “Porkopolis”).

In other words, concentration in meat-packing has been the rule for well over a century, and reflects economies of scale.

Personal aside: as a PhD student at Chicago, I was a beneficiary of the legacy of the packing kings of Chicago: I was the Oscar Mayer Fellow, and the fellowship paid my tuition and stipend.  My main regret: I never had a chance to drive the Weinermobile (which should have been a perk!).  My main source of relief: I never had to sing an adaption of the Oscar Mayer Weiner Song: “Oh I wish I were an Oscar Mayer Fellow, that’s what I really want to be.”

Back to the subject at hand!

Booker also frets about vertical integration, and this is indeed a difference between the 2018 meat industry and the 1918 version: as the Union Stockyards in Chicago attested–by the smell, if nothing else–the big packers did not operate their own feedlots, but bought livestock raised in the country and shipped to Chicago for processing.

I am a skeptic about market power-based explanations of vertical integration, and there is no robust economic theory that demonstrates that vertical integration is anti-competitive.  The models that show how vertical integration can be used to reduce competition tend to be highly stylized toys dependent on rather special assumptions, and hence are very fragile and don’t really shed much light on the phenomenon.

Transactions cost-based theories are much more plausible and empirically successful, and I would imagine that vertical integration in meat packing is driven by TCE considerations.  I haven’t delved into the subject, but I would guess that vertical integration enhances quality control and monitoring, and reduces the asymmetric information problems that are present in spot transactions, where a grower has better information about the quality of the cattle, and the care, feeding, and growing conditions than a buyer.

I’d also note that some of the other industries Booker mentions–notably bean and corn processing–have not seen upstream integration at all.

This variation in integration across different types of commodities suggests that transactional differences result in different organizational responses.  Grain and livestock are very different, and these likely give rise to different transactions costs for market vs. non-market transactions in the two sectors.  It is difficult to see how the potential for monopsony power differs across these sectors.

Insofar as the major grain traders are concerned, again–this is hardly news.  It was hardly news 40 years ago when Dan Morgan wrote Merchants of Grain.

Furthermore, Booker’s concerns seem rather quaint in light of the contraction of merchant margins, about which I’ve written a few posts.  Ironically, as my most recent ABCD post noted, downstream vertical integration by farmers into storage and logistics is a major driver of this trend.

To the extent that consolidation is in play in grains (and also in softs, such as sugar), it is a reflection of the industry’s travails, rather than driven by a drive to monopolize the industry.  Consolidation through merger is a time-tested method for squeezing out excess capacity in a static or declining industry.

Booker’s bill almost certainly has no chance of passage.  But it does reflect a common mindset in DC.  This is a mindset that is driven by simplistic understandings of the drivers of industrial structure, and is especially untainted by any familiarity with transactions cost economics and what it has to say about vertical integration.

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August 20, 2018

Goodhart’s Law on Steroids, PCP, and Crack: Chinese GDP

Filed under: China,Commodities,Economics,Politics — cpirrong @ 6:46 pm

Goodhart’s Law states that if a measure becomes a target, it ceases being an informative measure.  If you want to see an illustration of Gooodhart’s Law in action on a humungous scale, just look at China.

Michael Pettis has a piece in Bloomberg which, in brief, says that China has a GDP target.   If it appears that the country will fall short of the target, local governments get the high sign to invest in infrastructure, construction, and the like.  Local governments control credit creation (by guaranteeing bank debts) so banks are willing to lend to finance this investment: further, frequently the government will jawbone banks, or will twiddle the knobs in the banking system (e.g., lowering reserve requirements) to get banks to supply the necessary funds.

The investments are guaranteed (though what revenue stream or assets back the guarantees Pettis doesn’t say, and there are reasons to doubt the value of these guarantees in a crunch).  Hence, banks never have to write down the debt even if the investments turn out to be junk, with a value far less than the cost incurred to create the underlying assets.

So basically, the Chinese government can produce any GDP number it wants.  Voila, apropos Goodhart, the GDP number is useless.

You’d like GDP to measure the value of goods and services (including investment goods) created.  Instead, in China on the fixed asset side in particular, it measures cost, which may bear little relationship to value when economic decisions are made according to the process that Pettis describes.  In market economies where banks and borrowers have hard budget constraints, investments that don’t pan out are written down, and the losses are deducted from income.  That doesn’t happen in China.

So what is national income in China?  I’d start with consumption, though even there due to issues with price indices/inflation measurement that may be overstated.  Then I’d add a constant X times reported fixed investment, where X<1.  Probably a lot less than 1, to take into account the fact that much investment has a cost that exceeds value.  Further, I’d deduct some fraction of accumulated past investment to reflect writedowns that should be made, but aren’t.

The focus of this analysis should be on determining X.  X should be a function of something related to estimated shortfall of GDP from target absent stimulus: the bigger the shortfall, the smaller X (because more bad investment is likely when the shortfall is big, as it’s then that the government encourages investment to make up the shortfall).  It could be a function of the increase in fixed asset investment, or construction investment, with a smaller X when investment in those categories shoots up.

A few other remarks.

First, it is stories like Pettis’ that convince me that modern China represents the most colossal misallocation of capital in history.

Second, it also makes me skeptical about Scott Sumner’s use of state-owned-enterprise (SOE) share of employment as a measure of centralized control of the economy. Most of the capital, and related employment, that results from the GDP targeting channel that Pettis analyzes flows through private firms.  The government controls/affects resource allocation via incentives given to local governments, which in turn incentivize banks and private firms to achieve the government objective.

Spitballing here, but I think a better measure would be something along the lines of the ratio of the volatility in fixed investment to the volatility in GDP.  Or maybe the ratio of the volatility in credit creation to the volatility of GDP.  Chinese GDP volatility, especially post-crisis, is laughably low.  The channel that Pettis identifies stabilizes GDP (reducing its volatility) by changing investment/credit creation in response to changing economic conditions (thereby increasing its volatility).  The only problem with this measure is that there is a real risk it will become infinite.

In (relatively) market-oriented economies, investment is the most volatile component of GDP, so the ratio I propose would be positive in those economies.  But that could serve as a market economy benchmark against which to compare China.  I’m guessing that China’s ratio would be substantially larger.

Third, when looking at the demand for commodities, the potential for shortfalls of economic performance from government target should be decisive.  These shortfalls induce the turning of the credit spigot which juices the demand for commodities.

In sum, what matters in China is not whether or not GDP hits the target–it will! The question is what the government has to do to hit it.

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August 16, 2018

Why ABCD Sing the Blues, Part II: Increased Farm Scale Leads to Greater Competition in Capacity and Less Monopsony Power

Filed under: Commodities,Derivatives,Economics,Politics,Regulation — cpirrong @ 6:34 pm

In “Why Are ABCD Singing the Blues?” I called bull on the claim that ag trading firms were suffering through a rough period because of big crops and low prices.  I instead surmised that gains in capacity, in storage and throughput facilities, had outstripped growth in the amount of grain handled, and that this was pressuring margins.  In yesterday’s WSJ, Jacob Bunge (no relation, apparently, to the grain trading family) had a long and dense article that presents a lot of anecdotal support for that view.  The piece also provides other information that allows me to supplement and expand on it.

In a nutshell, due to increased economies of scale in farming, farms have grown larger.  Many farms have grown to the point that they can achieve efficient scale in storage and logistics to warrant investment in storage facilities and trucks, and thus can vertically integrate into the functions traditionally performed by Cargill and the others.  This has led to an expansion in storage capacity and logistical capacity overall, which has reduced the derived demand for the storage and logistics assets owned and operated by the ABCDs.  Jacob’s article presents a striking example of an Illinois farmer that bought a storage facility from Cargill.

In brief, more integrated farms have invested in capacity that competes with the facilities owned by Cargill, ADM, Bunge, and smaller firms in the industry.  No wonder their profits have fallen.

The other thing that the article illustrates is that scale plus cheaper communication costs have reduced the monopsony power of the grain merchants.  The operation of the farmer profiled in the piece is so large that many merchants, including some from a distance away, are competing for his business.  Furthermore, the ability to store his own production gives the farmer the luxury of time to sell: he doesn’t have to sell at harvest time to the local elevator at whatever price the latter offers–which was historically low-balled due to the cost of hauling to a more distant elevator.  Choosing the time to sell gives the farmer the value of the optionality inherent in storage–and the traditional merchant loses that option.  Further, more time allows the farmer to seek out and negotiate better deals from a wider variety of players.

The traditional country market for grain can be modeled well as a simple spatial economy with fixed costs (the costs of building/operating an elevator).  Fixed costs limit the number of elevators, and transportation costs between spatially separated elevators gave each elevator some market power in its vicinity: more technically, transportation costs meant that the supply of grain to a country elevator was upward sloping, with the nearby farms willing to sell at lower prices than more distant ones closer to competing elevators.  This gave the elevators monopsony power.  (And no doubt, competition was limited even in multi-elevator towns, because the conditions for tacit collusion were ripe.)

Once upon a time, the monopsony power of elevator operators was a hot-button political issue.  One impetus for the farm cooperative movement was to counteract the monopsony power of the line elevator operators.  The middlemen didn’t like this one bit, and that was the reason that they excluded cooperatives from membership of futures exchanges, like the Chicago Board of Trade: this exclusion raised cooperatives’ costs, and was effectively a raising-rivals-cost strategy.  Brokers also supported excluding cooperatives because as members cooperatives could have circumvented broker commission cartels (i.e., the official, exchange-approved and enforced minimum commission rates).  This is why the Commodity Exchange Act contains this language:

No board of trade which has been designated or registered as a contract market or a derivatives transaction execution facility exclude  from membership in, and all privileges on, such board of trade, any association or corporation engaged in cash commodity business having adequate financial responsibility which is organized under the cooperative laws of any State, or which has been recognized as a cooperative association of producers by the United States Government or by any agency thereof, if such association or corporation complies and agrees to comply with such terms and conditions as are or may be imposed lawfully upon other members of such board, and as are or may be imposed lawfully upon a cooperative association of producers engaged in cash commodity business, unless such board of trade is authorized by the commission to exclude such association or corporation from membership and privileges after hearing held upon at least three days’ notice subsequent to the filing of complaint by the board of trade.

Put differently, in the old days the efficient scale of farms was small relative to the efficient scale of midstream assets, so farmers had to cooperate in order to circumvent merchant monopsony power.  Cooperation was hampered by incentive problems and the political nature of cooperative governance.  (See Henry Hansmann’s Ownership of Enterprise for a nice discussion.) The dramatic increase in the efficient scale of farms now means (as the WSJ article shows) that many farmers have operations as large as the efficient scale of some midstream assets, so can circumvent monopsony power through integration.  This pressures merchants; margins.

Jacob Bunge is to be congratulated for not imitating the laziness of most of those who have “reported” on the grain merchant blues, where by “reporting” I mean regurgitating the conventional wisdom that they picked up from some other lazy journalist.  He went out into the field–literally–and shed a good deal of light on what’s really going on.  And what’s going on is competition and entry, driven in large part by economic and technological forces that have increased the efficient scale of grain and oilseed production.  Thus, the grain handlers are in large part indirect victims of technological change, even though the technology of their business has remained static by comparison.

 

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August 1, 2018

This Is My Shocked Face: Blockchain Hype Is Fading Fast

Filed under: Blockchain,Commodities,Cryptocurrency,Economics — cpirrong @ 7:02 pm

Imagine my great surprise at reading a Bloomberg piece titled: “Blockchain, Once Seen as a Corporate Cure-All, Suffers Slowdown.

That was sarcasm, by the way.  I’ve long and publicly expressed my skepticism that blockchain will have revolutionary effects, at least in the near to medium term.  In my public speaking on the topic, I’ve explored the implications of three basic observations.  First, that blockchain is basically a way of sharing/communicating information, which can in turn be put to various uses.  Second, there alternative ways of sharing/communicating information, with different costs and benefits.  And third, it is necessary to distinguish between sharing information within an organization and between organizations.

Much of the hype about blockchain relates to the potential benefits of more efficient sharing and validation of information.  But this does not address the issue of whether blockchain does this more efficiently than alternative means of sharing/communicating/validating.  As in all institutional/technology issues, a comparison of alternatives is necessary.  This comparison has been sadly lacking in public discussions of the potential for blockchain, beyond incantations about blockchain eliminating the need for trusted third parties which is (a) often untrue (in part because trusted parties may be required to enter information into a blockchain, and (b) is not necessarily a feature, because trusted third parties may be able to operate more efficiently than consensus based systems employed on a blockchain.

The most developed implementation of blockchain (Bitcoin) involves very large cost to solve a particular problem that (a) is unique to cryptocurrency, and (b) is not necessarily important in other contexts–namely, the double spend problem in crypto.  Maybe blockchain is the best way to solve that particular problem (which itself begs the question of whether cryptocurrency`is an efficient solution to any economic problem), but that doesn’t mean that it will be a more efficient way of solving the myriad types of opportunism, fraud, and deceit that plague other kinds of transactions.  Double spend is not the alpha and omega of transactional challenges.  Indeed, it might be one of the most trivial.

Thinking in Williamsonian transaction cost terms, where the transaction is the unit of analysis, transactions are highly diverse.  Different kinds of transactions are vulnerable to different kinds of information and opportunism problems, meaning that customized blockchain approaches are likely necessary.  One likely cause for the waning enthusiasm mentioned in the Bloomberg article is that people are coming to the recognition that customization is not easy, and it may not be worth the candle, compared to other ways of addressing the same issues.  Relatedly, customization makes it harder to exploit scale economies, and recognition of this is likely to be making initially enthusiastic commercial users less keen on the idea: that is, it may be possible to use blockchain in many settings, but it may not be cost-effective to do so.

The siloed vs. cooperative divide is also likely to be extremely important, and the Bloomberg article mentions that issue a couple of times.  The blockchain initiatives that do seem to have been implemented, at least to some degree, as with Maersk in container shipping or Cargill with turkeys, are intra-firm endeavors that do not require coordination and cooperation across firms, and can exploit the governance structure that a firm has in place.  Many of the other proposed uses–for instance, in trade finance, or in commodity trading, both of which require myriad parties in a single transaction to communicate information among one another–are inherently multilateral.

This creates all sorts of challenges.  How can commercial rivals cooperate?  How are the gains from cooperation divided?–this is a problem even when participants supply complementary services, such as a trading firm, banks providing trade finance, and the buyer and seller of a commodity.  As oil unitization has shown, battles over dividing the gains from cooperation can dissipate much of those gains.  Who gets to see what information?  Who makes the rules?  How?  How are they enforced? What is the governance structure?  How is free riding prevented?  Who pays?

Ironically, where the gains from cooperation are seemingly biggest–where there are large numbers of potential participants–is exactly where the problems of coordination, negotiation, and agreement are likely to be most daunting.

I’ve drawn the analogy between these cooperative blockchain endeavors and commodity exchanges, which (as I showed in a 1995 JLS paper) were formed primarily as ways to reduce transactions costs via cooperative rule making and enforcement.  The old paper shows that exchanges faced serious obstacles in achieving the gains from cooperation, and often failed to do so.  Don’t expect blockchain to be any different, especially given the greater complexity of the transactional problems that it is being proposed as a fix.

Thus, I am not surprised to read things like this:

“The expectation was we’d quickly find use cases,” Magnus Haglind, Nasdaq’s senior vice president and head of product management for market technology, said in an interview. “But introducing new technologies requires broad collaboration with industry participants, and it all takes time.”

or this:

Most blockchains also can’t yet handle a large volume of transactions — a must-have for major corporations. And they only shine in certain types of use cases, typically where companies collaborate on projects. But because different businesses have to share the same blockchain, it can be a challenge to agree on technology and how to adopt it.

One of my favorite illustrations of the hype outstripping the reality is the endeavor launched with much fanfare in the cotton market, where IBM and The Seam announced an endeavor to use the blockchain to revolutionize the cotton supply chain.   It’s been almost two years, and after the initial press releases, it’s devilish hard to find any mention of the project, let alone any indication that it will go into operation anytime soon.

Read the Bloomberg article and you’ll have a better understanding of R3’s announcement of an IPO–and that they might have missed their opportunity.

In 2017 and a little before, Blockchain was a brand new shiny hammer.  People have been looking everywhere for nails to pound with it, and spending a lot of money in the effort.  But they’re finding that many transactional problems aren’t nails, that there are other hammers that might do the job better, and there are other problems that require many parties to agree on just how the hammer is to be used and by whom.  Given this, it is not surprising that the euphoria is fading fast.  The main question that remains is in what shrunken domain will blockchain actually be employed, and when.  My guess is that the domain will be relatively small, and the time until employment will be pretty long.

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July 16, 2018

Oil Spreads Go Non-Linear (Due to Infrastructure Constraints), To the Chagrin of Many Traders: The Pirrong Commodity Catechism in Action

Filed under: Commodities,Economics,Energy,Exchanges — cpirrong @ 3:59 pm

When I wrote about the demise of GEM Trading a few weeks ago, I hypothesized that sharp movements in various spreads had been its undoing.  A story in Reuters says that GEM was not the only firm rocked by these changes.  Big boys–including BP, Vitol, Trafigura, and Gunvor–have also suffered, and the losses have caused traders their jobs at Gunvor and BP:

The world’s biggest oil traders are counting hefty losses after a surprise doubling in the price discount of U.S. light crude to benchmark Brent WTCLc1-LCOc1 in just a month, as surging U.S production upends the market.

Trading desks of oil major BP and merchants Vitol , Gunvor and Trafigura have recorded losses in the tens of millions of dollars each as a result of the “whipsaw” move when the spread reached more than $11.50 a barrel in June, insiders familiar with their performance told Reuters.

The sources did not give precise figures for the losses, but they said they were enough for Gunvor and BP to fire at least one trader each.

The story goes on to say that binding infrastructure constraints are to blame, which is certainly the case.  But implicit in the article is a theme that I have emphasized for literally years (I recall incorporating this into my class lectures in about 2004).  Specifically, bottlenecks imply that marginal transformation costs (e.g., marginal costs of transporting oil between Cushing and the GOM) tend to rise very steeply when capacity constraints are reached.  That is, when you are operating at say 90 percent of capacity, variations in utilization have little impact on marginal transformation costs, but going from 95 to 96 can cause costs to explode, and basically go vertical as capacity is reached.

This has an implication for spreads.  Another part of the Pirrong Commodities Catechism is that spreads equal marginal transformation costs, and are essentially the shadow prices on constraints.  The behavior of marginal transformation costs therefore has implications for spreads: in particular, spreads can be very stable despite variations in the utilization of transformation assets, but as utilization nears capacity, the spreads become much more volatile.  Moreover, and relatedly, small changes in fundamentals can lead to big moves in spreads when constraints start to bind.  The relationship between fundamentals and spreads is non-linear as capacity constraints become binding, and well, here spreads have gone non-linear, to the chagrin of many traders.

Put differently, spread trades aren’t always “widowmakers” (as the article calls them)–sometimes they are quite safe and boring.  But when bottlenecks begin to bind, they can become deadly.

There is one odd statement in the article:

“As the exporter of U.S. crude, traders are naturally long WTI and hedge their bets by shorting Brent. When the spreads widen so wildly, you lose money,” said a top executive with one of the four trading firms.

Well, why would you hedge WTI risk with Brent?  You could hedge your WTI inventory by selling . . . WTI futures.  The choice to “hedge” WTI by selling Brent is effectively a choice to speculate on the spread.  That brings to mind the old Holbrook Working adage that hedging is speculation on the basis.  The difference here is that most, say, country grain elevators about which Working was mainly writing had no choice in hedging instrument (at least not in liquid ones), and perforce had to live with basis risk if they wanted to eliminate flat price risk.  Here, BP and Gunvor and the rest had the choice between two liquid instruments, and if the “top executive’s” statement is correct, deliberately chose the one that exposed them to greater spread (basis) risk.

So this isn’t an example of “sometimes stuff happens when you hedge.”  The firms chose to expose themselves to a particular risk.  They took a punt on the spread, which was effectively a punt that infrastructure constraints would ease.  They lost.

In my 2014 white paper on commodity trading firms (sponsored by Trafigura, ironically) I noted that to the extent that they speculate, commodity trading firms tend to speculate on the spreads, rather than flat prices, because that’s where they have something of an information advantage.  But as this episode shows, that advantage does not immunize them against risk.

This also makes me wonder about the risk models that the firms use, which in turn affect the sizes of positions traders can put on, and where they put them on.  I, er, speculate that these risk models don’t take into account the non-linearity of spread risk.  If that’s true, traders would have been able to put on bigger positions than they would have been had the risk models accurately reflected those risks, and further, that they were incentivized to do these trades because the risk was underpriced.

All in all, an interesting casebook study of commodity trading–what can go wrong, and why.

Correction: Andrew Gowers, head of corporate affairs at Trafigura says in the comments that (a) Trafigura did not suffer a loss, and (b) the company had told this to Reuters prior to the publication of the article.  I have contacted the editor of the story for an explanation.

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July 6, 2018

Chinese Oil Futures: Performing As Predicted

Filed under: China,Commodities,Derivatives,Economics,Energy — cpirrong @ 6:27 pm

The recent introduction of Shanghai oil futures has resulted in a lot of churn in the front month, and very little activity in even the 1st and 2nd nearby:

China’s new oil futures are a hit with investors but they’re facing commitment issues.

While daily volume in the yuan-denominated contract has increased five-fold since its debut in late-March amid steady growth in open interest, almost all trading is focused in front-month, September futures.

. . . .

It suggests that, for now, traders are using the futures principally to speculate on short-term price fluctuations, as opposed to hedge long-term consumption or production, according to Jia Zheng, a portfolio manager at Shanghai Minghong Investment Co.

Which is pretty much what I predicted on the day of the launch:

Will it succeed?  Well, that depends on how you measure success.  No doubt it will generate heavy volume.  Speculative enthusiasm runs deep in China, and retail traders trade a lot.  They would probably make a guano futures contract a success, if it were launched: they will no doubt be attracted to crude.

. . . .

If you are looking for a metric of success as a commercial tool (rather than of its success as a money making venture for the exchange) look at open interest, not volume.  And look in particular in open interest in the back months.  This will take some time to build, and in the meantime I imagine that there will be a lot of awed commentary about trading volume.  But that’s not the main indicator of the utility of a contract as a commercial risk management and price discovery tool.

 

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June 28, 2018

A Tarnished GEM: A Casualty of Regulation, Spread Explosions, or Both?

Filed under: Clearing,Commodities,Derivatives,Economics,Energy,Exchanges,Regulation — The Professor @ 6:28 pm

Geneva Energy Markets LLC, a large independent oil market maker, has been shuttered.  Bloomberg and the FT have stories on GEM’s demise.  The Bloomberg piece primarily communicates the firm’s official explanation: the imposition of the Basel III leverage ratio on GEM’s clearer raised the FCM’s capital requirement, and it responded by forcing GEM to reduce its positions sharply.  The FT story contains the same explanation, but adds this: “Geneva Energy Markets, which traded between 50m and 100m barrels a day of oil, has sold its trading book after taking ‘significant losses’ in oil futures and options, a person close to the company said.”

These stories are of course not mutually exclusive, and the timing of the announcement that the firm is shutting down months after it had already been ordered to reduce positions suggests a way of reconciling them. Specifically, the firm had suffered loss that made it impossible to support even its shrunken positions.

The timing is consistent with this.  GEM is primarily a spread trader, and oil spreads have gone crazy lately.  In particular, spread position short nearby WTI has been killed in recent days due to the closure of Canadian oil sands production and the relentless exports of US oil.  The fall in supply and continued strong demand have led to a rapid fall in oil stocks, especially at Cushing.  This has been accompanied (as theory says it should be!) by a spike in the WTI backwardation, and a rise in the WTI-Brent differential (and other quality spreads with a WTI leg).  If GEM was short the calendar spread, or had a position in quality spreads that went pear-shaped with the explosion in WTI, it could have taken a big hit.  Or at least a big enough hit to make it unviable to continue to operate at a profitable scale.

Here’s a cautionary tale.  Stop me if you’ve heard it before:

“The notional value of our book was in excess of $50 billion,” Vonderheide said. “However, the actual risk of the book was always relatively low, with at value-at-risk at around $2 million at any given time.”

If I had a dollar for every time that I’ve heard/read “No worries! Our VaR is really low!” only to have the firm fold (or survive a big loss) I would be livin’ large.  VaR works.  Until it doesn’t.  At best, it tells you the minimum loss you can suffer with a certain probability: it doesn’t tell you how much worse than that it can get.  This is why VaR is being replaced or supplemented with other measures that give a better measure of downside risk (e.g., expected shortfall).

I would agree, however, with GEM managing partner Mark Vonderheide (whom I know slightly):

“The new regulation is seriously damaging the liquidity in the energy market,” Vonderheide said. “If the regulation was intending to create a safer and more efficient market, it has done completely the opposite.”

It makes it costlier to make markets, which erodes market liquidity, thereby making it costlier for firms to hedge, and more difficult to enter and exit positions.  Liquidity reductions resulting from this type of regulation tend to be most acute during periods of high volatility–which can exacerbate the volatility, perversely.  Moreover, like much of Frankendodd and its foreign fellow monsters, it tends to hit small to medium sized firms worse than bigger ones, and thereby contributes to greater concentration in the markets–exactly the opposite of the stated purpose.

As Reagan said: “The most terrifying words in the English language are: I’m from the government and I’m here to help.” Just ask GEM about that.

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May 24, 2018

Gazprom and Its Connected Contractors: The Credit Mobilier Scheme, With Russian Variations

Filed under: Commodities,Economics,Energy,History,Russia — The Professor @ 6:05 pm

A couple of SWP friends were kind enough to send me a copy of the swan song of one Alex Fak, an erstwhile senior analyst at Sberbank.  Alex lost his job because he committed a mortal sin: telling the truth, in this instance about the monstrosity that I have savaged for years–Gazprom.

Alex said that the oft-heard question “why does Gazprom do such stupid things?” is off base because it presumes that the company is run in the interest of shareholders: if it were, its unmatched record of value destruction would indeed be stupid.  However, Mr. Fax opined that the company’s actions over the decades are definitely not stupid if you evaluate them from the perspective of its contractors, who make massive amounts of money building obscenely negative NPV projects.

Why does this persist, in the Putin era, which allegedly cracked down on oligarchic thievery? Well, one reason is that the biggest contractors happen to be owned by–wait for it–the two biggest friends of Vova: Gennady Timchenko (a hockey buddy) and Arkady Rotenberg (a judo buddy).*  Putin did not eliminate oligarchs, so much as replace them with his cronies.  Calling out such connected men by name is no doubt why Mr. Fax is an ex-Sberbank analyst.  And saying this kind of thing puts him at risk of being an ex-person.

The Gazprom MO described by Mr. Fak  represents a continuation of, and a mega-sizing of, the bizness model of the 1990s, when the “red directors” of state-owned firms tunneled out huge amounts of funds by having their firms buy supplies and services at seriously inflated prices from firms owned by their relatives.

Indeed, in the pre-Cambrian days of this blog–2006(!)–I hypothesized that Gazprom and its contractors were in effect a Russian version of Credit Mobilier, the construction firm that the Union Pacific hired to build the railroad.

The WaPo article also mentions that Gazprom’s pipeline construction costs are two to three times industry norms. To me this suggests a Credit Mobilier-Union Pacific type situation, where inflated prices for materials and equipment flow into the pockets of companies owned by Gazprom managers. Just thinkin’.

Thomas C. Durant was the president of the Union Pacific–and the major shareholder in Credit Mobilier.  The UP paid Credit Mobilier around $94 million, and Credit Mobilier incurred only about $50 million in costs to build the UP.   The Gazprom arrangement is somewhat different given that neither Timchenko nor Rotenberg are executives at the Russian gas giant, but the basic idea is very similar. (I also noted early on that Transneft, the oil pipeline monopoly, operates on the same model.)  Gazprom and its contractors operate on the Credit Mobilier model, with Russian variations.

Once upon a time Gazprom CEO Alexei Miller boasted that he would make Gazprom the world’s first trillion dollar company.  Today it’s market cap is south of $55 billion.  Hey! anybody can be off by two orders of magnitude, right?

This is not surprising, because maximizing value to shareholders is not, nor has it ever been, the objective of Gazprom.  The objective is, and always has been, to divert resources to the politically connected via wasteful capital expenditures (that happen to be the revenues of the likes of Timchenko and Rotenberg).  Alex Fak understood this, and paid the price for shouting that the emperor had no clothes.

Both Gazprom and Rosneft are world leaders in destroying value, rather than creating it.  But this is a feature, not a bug, given the natural state political economy of Russia, which prioritizes rent creation and redistribution to the elite. And this is precisely why Russia’s pretensions to great power status rest on economic quicksand.  That should be blindingly obvious, and I am sure that Putin understands this at some level.  But revealed preference suggests that he values enriching his friends more than implementing the economic changes that would make his nation economically and militarily competitive.

*The sums tunneled from Gazprom to Timchenko make me laugh when I think about the oft-repeated allegation that oil trader Gunvor (half-owned by Timchenko) was a source of massive personal wealth for Putin (via Timchenko).  There was much more money to be made much closer to home, and completely outside the scrutiny of bankers and regulators.

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May 17, 2018

Rosneft: The Farce Continues

Filed under: Commodities,Economics,Energy,Russia — The Professor @ 7:22 pm

Remember when the Russian government said it was going to privatize a piece of Rosneft? Hahahaha. That is so 2016–please try to keep up!  In its announcement of “Rosneft 2022” the company proposes to buy back about $2 billion in shares, which is just about 20 percent of the piece sold off in 2016–no, wait–2017–no, wait–2018.  Adding even more hilarity is that the buyback plan was apparently at the insistence of Qatar, the last buyer standing which agreed to buy most of the shares initially privatized, much to the relief of the banks (Intesa and unnamed Russian ones) who were wearing a big piece of the risk.

I’m guessing that this was one of the terms Qatar laid down to absorb the entire hand-me-down stake for the original 2016 price, even though in Euro terms Rosneft’s shares are substantially lower today (despite a rallying oil price!)

Quite the vote of confidence there, eh?  Well, not that that’s surprising.  The conspicuous failure of any Chinese buyer to step into the shoes of disgraced CEFC tells you just how much confidence Rosneft inspires these days.

I am hard pressed to recall such a farcical series of events involving a major company.  If this one of  Russia’s state champions, just think of the shape the palookas are in!

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