Streetwise Professor

Matt Levine wrote a typically amusing piece highlighting the role of Trade at Settle (TAS) contracts in the 4/20/20 oil futures debacle:

But actually a lot of oil changed hands at those negative prices. Not because a bunch of investors came to the market all at once looking to sell, and no one would buy from them at negative prices, but for a more technical reason. Some oil traders use “trade-at-settlement” contracts: Instead of buying (or selling) oil futures at the market price at the time of your trade, you agree in advance to buy (or sell) them at whatever the official 2:30 p.m. settlement price is that day. 1  This is a good trade, for you, if your job is to obtain the day’s settlement price: For instance, if you run an index fund or exchange-traded fund that is benchmarked to that price, using TAS futures guarantees you the benchmark price. If you invest in oil futures and your boss fires you if you miss the benchmark, you might use TAS futures, that sort of thing. If you are a savvy oil trader attuned to minute-by-minute changes in supply and demand and trying to capture as much value as possible from your skills, you’ll probably just trade the futures at their current prices, selling if the price is too high and buying if it’s too low. But a lot of oil traders are doing something else, something a bit more passive, and for them the ability to guarantee the settlement price is useful.

He goes on to ponder whether the TAS mechanism could be manipulated, and whether manipulation could have contributed to the settlement fire that Red Adair couldn’t have put out:

The basic pattern—agree in advance to buy (sell) stuff at the official settlement price at some fixed future time, and then sell (buy) a bunch of that stuff in the minutes leading up to the official settlement time with the effect of pushing down (up) the price at which you are buying (selling)—is incredibly common, and the gradation from “sensibly pre-hedging the exposure you will get at settlement” to “sloppily pre-hedging the exposure you will get at settlement” to “manipulating the market to push down the price you will get at settlement” is blurry. If you type in a chat room “lol I’m gonna pound out 500 contracts to push down the settlement price and make fortune on my TAS trades, I am really ripping those muppets’ faces off, hope I don’t go to prison bro, hashtag fraud hashtag crime hashtag manipulation,” you will get in trouble. But if you don’t type that, and you quietly sell the 500 contracts and the price goes down, then as far as anyone knows that was just pre-hedging.

So could somebody have popped CLK20 with a TASer last Monday?

Funny you should ask. I wrote a paper on TAS manipulation a while ago. My interest was sparked by the CFTC’s action against Dutch trading firm Optiver, which the agency accused of doing exactly the kind of thing Levine writes about in crude, gasoline, and heating oil futures back in March, 2008. You can read the complaint–and listen to some actual “ripping those muppets’ faces off” trader braggadocio.

How does manipulation work here? First, to make manipulation profitable, there has to be an asymmetric price response to purchases and sales. If the manipulator’s purchases impact prices the same as sales, just buying and selling a lot can’t move prices in a profitable direction. Indeed, the manipulator would have to pay transactions costs (crossing the spread, brokerage, etc.) and this would cause the trading to be unprofitable.

The model in the paper derives conditions under which purchases and sales of TAS have a smaller impact on prices than do trades in the underlying futures. The basic idea is that if information is short-lived, or if there is intense competition among informed traders, new information will be incorporated into prices very quickly. Under those circumstances, informed traders will not want to trade TAS: their information will already be incorporated into the price by the time settlement occurs. Thus, TAS is a mechanism that allows traders to signal that they are uninformed: many “muppets” choose to trade TAS, and the informed don’t. Thus, the price impact of TAS trades is smaller than the price impact of regular outright trades: trades move prices because of the possibility that they are motivated by private information, so trades that are unlikely to be privately informed move prices less than trades that are more likely to be so.

This creates the asymmetry that makes manipulation possible: the manipulator buys, say, the TAS and then sells in quantity immediately before and during the settlement period and profits as Levine describes.

This type of manipulation is particularly pernicious because manipulative trades have persistent price impacts because they cannot be distinguished from informed trades (or liquidity trades, for that matter). Note that in Optiver, prices did not reverse after the firm’s trades,

This strategy is likely to be particularly profitable when markets are relatively illiquid, as in an illiquid market outright trades have bigger price impacts. Liquidity (measured by the bid-ask spread, quantity at the top of the book, price impact coefficients, etc.) has plummeted for everything since the CovidCrisis began, and the decline in CL liquidity has been particularly pronounced. Moreover, contracts close to expiration are less liquid anyways. Add to this the extreme physical constraints (which mean that small shocks to fundamentals have big price impacts) and the raging uncertainty about the logistical situation at Cushing, and it is likely that small volumes at the settle could have big impacts on prices.

To this I would add that an unexpected shortfall in buy orders (due to shorts exercising market power) at the settle could have price impacts, and exacerbate the price impacts of sell orders by exacerbating order imbalances.

Thus, the potential for a big asymmetry in price impact was pronounced on that fatal Monday.

In sum, it is not implausible that the market did indeed get TASed. Or at the very least, a TASer jolt contributed to the collapse. (Sort of like in this video!)

A final remark on the economic benefits and costs of TAS trading. TAS is a form of “cream skimming”–i.e., the skimming off of uninformed order flow. This tends to make the order flow in the regular continuous market more toxic, which reduces liquidity in that market. For this reason, other cream skimming mechanisms used primarily in equity markets (payment for order flow, dark pools, block trades) are frequently criticized. (This is why some regulators, particular in Europe, have attempted to curb such activity and force more trading into “lit” venues.)

I showed in my Market Macrostructure paper that things aren’t so simple. If the regular market isn’t perfectly competitive, the increased competition from a cream skimming mechanism can improve welfare. Moreover, there are distributive effects here: the uninformed traders who can utilize the TAS mechanism (e.g., those who are hedging exposures tied to the settlement price) benefit, while uninformed traders who can’t lose. Informed traders can lose too. Moreover–and this is a point that is almost always overlooked–some informed trading is essentially rent seeking (e.g., trading on information that will be released shortly anyways, and accelerating its incorporation into prices has little effects on resource allocation decisions). Reducing rent seeking informed trading is a good thing.

All in all, the role of the TASer is yet another piece of the 4/20/20 WTI WTF puzzle. The forensic analysis of this entire episode will be fascinating.

Just another day at the Globex, folks. May WTI up a mere $49.88 on its last trading day at the time I write this paragraph, a while before the close. (Sorry, can’t calculate a percentage change . . . because the base number is negative!) That’s just sick. But at least it’s positive! ($12.25. No, $9.96. No . . .) (This reminds me of a story from Black Monday. My firm did a little index arb. We called the floor to get a price quote on the 19th. Our floor guy said “On this part of the pit it’s X. Over there it’s X+50. Over there it’s X-20. I have no fucking idea what the fucking price is.”)

But June has been crushed–down $7.35 (about 35 percent). Now the May-June spread is a mere $.83 contango. That makes as little sense as yesterday’s settling galactic contango (galactango!) of $57.06. (Note that June-July is trading at at $7.71 and July-August at $2.65.

I’m guessing that dynamic circuit breakers are impeding price movements, meaning that the prices we see are not necessarily market clearing prices at that instant.

A few follow-ons to yesterday’s post.

First, the modeling of the dynamics of a contract as it approaches expiration when the delivery supply/demand curve is inelastic, and some traders might have positions large enough to exploit those conditions to exercise market power, is extremely complicated. The only examples I am aware of are Cooper and Donaldson in the JFQA almost 30 years ago, and my paper in the Journal of Alternative Investments almost a decade ago.

Futures markets are (shockingly!) forward looking. Expectations and beliefs matter. There are coordination problems. If I believe everyone else on my side of the market is going to liquidate prior to expiration, I realize that the party on the other side of the contract will have no market power at expiration. So I should defer liquidating–which if everyone reasons the same way could lead to everyone getting caught in a long or sort manipulation at expiration. Or, if I believe everyone is going to stick it out to the end, I should get out earlier (which if everybody else does the same results in a stampede for the exits.)

In these situations, anything can happen, and the process of coordinating expectations and actions is likely to be chaotic. Cooper-Donaldson and Pirrong lay out some plausible stories (based on particular specifications of beliefs and the trading mechanism), but they are not the only stories. They mainly serve to highlight how game theoretic considerations can lead to very complex outcomes in situations with market power and inelasticity.

One thing that is sure is that these game theoretic considerations don’t matter much if the elasticities of delivery supply and demand are large. Then no individual can distort prices very much by delivering too much or taking delivery of too much. Then the coordination and expectations problems aren’t so relevant. However, when delivery supply or demand curves are very steep–as is the case in Cushing now due to the storage constraint–they become extremely relevant.

Perhaps one analogy is getting out of a theater. When there are many exits, there won’t be queues to get out and little chance of tragedy even if someone yells “fire.” If there is only one exit, however, hurried attempts of everyone to leave at once can lead to catastrophe. Moreover, perverse crowd dynamics occur in such situations. That’s where we were yesterday.

About 90 percent of open interest liquidated yesterday. That is why today is returning to some semblance of normality–the exit isn’t so crowded (because so many got trampled yesterday). But that begs the question of why the panicked rush yesterday? That’s where the game theoretic “anything could happen” answer is about the best we can do.

About that storage constraint. My post yesterday focused on someone with a large short futures position raising the specter of excessive deliveries by not liquidating that position, thereby triggering a cascade of descending offers until the short graciously accepted at a highly profitable price.

But there is another market power play possible here. A firm controlling storage could crash prices (and spreads) by withholding that capacity from the market. The most recent data from the EIA indicates about 55 mm bbl of oil storage at Cushing. That’s about 80 percent of nameplate capacity (also per EIA.). Due to operational constraints (e.g., need working space to move barrels in and out; can’t mix different grades in the same tank) that’s probably effectively full. Therefore, someone with ownership of a modest amount of space could withhold it drive up the spread. If that party had on a bull spread position . . .

Third, we are into Round Up the Usual Suspects mode:

And first in line is the US Oil ETF. There has been a lot of idiotic commentary about this. They were forced to take delivery! (Er, delivery notices aren’t possible before trading ends.) They were forced to dump huge numbers of contracts yesterday! (Er, they publish a regular roll schedule, and were out of the May a week before yesterday’s holocaust. They also report positions daily, and as of yesterday were 100 pct in the June.)

Not to say that USO can be implicated in hinky things going on in the June right now, but as for May–that dog don’t hunt.

Fourth–WTF, June WTI? Well, my best explanation is that the carnage in the May served to concentrate minds regarding June. No doubt risk managers, or risk systems, forced some longs out as the measured and perceived risk for June shot up yesterday. Others just decided that discretion was the better part of valor. The extremely unsettled positions no doubt impaired liquidity (i.e., just as some wanted to get out, others were constrained by risk limits formal or informal from getting in), leading to big price movements in response to these flows. If that’s a correct diagnosis, we should see something of a bounceback, but perhaps not too much given the perception (and reality) of an extremely asymmetric risk profile, with going into expiry short being a lot more dangerous than going into it long. (This is why expectations about future conditions at delivery can impact prices well before delivery.)

Fifth, on a personal note, in an illustration of the adage that the apple doesn’t fall far from the tree (and also of Merton’s Law of Multiples) my elder daughter Renee completely independently of me used “WTI WTF” in her daily market commentary yesterday. I’m so proud! She also raised the possibility of negative prices some time ago. Good call!

And I finish this just in time to bring you the final results. CLK goes off the board settling at $10.01, up a mere $47.64. CLM settles at $11.57, down -$8.86. The closing KM20 spread, $1.56.

Someday we’ll look back on this and . . . . Well, we’ll look back on it, anyways.

Today was one of the most epochal days in the history of oil trading, which is saying something. The front month May contract–which expires tomorrow (21 April, 2020)–(a) settled at a negative price of -$37.63, (b) declined $55.90 from the previous day’s settlement, and (c) exhibited a trading range of $58.17, which is about 3.5x Friday’s settlement price.

Even these eye-popping numbers don’t tell the full story. The last traded price was -$13.10. Note that the settlement price is based on the volume weighted average price during the last two minutes of trading, so an average price of $-37.63 in the last two minutes and a last traded price of -$13.10 means prices moved more that $25/bbl in these minutes.

And we’re not done yet! As I write at 1841 CDT, the price is up to -$5.00–an increase of $32.63.

That there’s what they call volatility, folks.

To put these numbers in perspective, the largest trading range on the any day of the last three trading days of CL contracts from 2000-2019 is $26.65, a day around the time of the financial crisis and the aftermath of Hurricane Ike (October 2008 contract): here’s what I wrote about that event. The median intra-day range on the last three days is $1.6, the mean is $1.5, and the standard deviation is $1.47. So we are around a 40 standard deviation event here.

The largest daily price change during the last 3 trading days is $16.37, with a median of $.73 and a standard deviation of $1.45.

The calendar spread is also extreme, settling at $58.06 between the June and the May. Meaning that if you had storage, you could get paid to take delivery, sell it forward, and lock in that $58.06 (net of what the storage costs you). I guess you could call that the megacontango.

All in all, a historically unprecedented day.

The proximate cause of these wild gyrations, and unprecedented negative prices is, of course, the collapse of demand and the looming exhaustion of storage space, including at the delivery point of Cushing, OK. But although this is a necessary condition for today’s events, it is not a full explanation.

The storage issue has been known for weeks, and discussed intensely. It had been priced in to a considerable degree: contango was already at a historically high level. What information about the availability of storage arrived between Friday and today? Unlikely to be anything that could cause such chaotic price movements.

The likely cause is the difficulty of liquidating about 100,000 open contracts (100 million barrels!) in such extreme technical conditions. It is plausible, and indeed likely, that strategic behavior–perhaps rising to the level of manipulation–is the major cause of how prices moved today against the background of conditions that were widely known on Friday.

Let me start out by noting that something similar, though not as extreme, occurred during the demand collapse and associated flooding of storage during the Financial Crisis. As I documented here, the expiries of the January, February, and March 2009 WTI contracts saw what were then historically unprecedented price collapses. So did other US grades of oil. Here’s a picture from the linked document:

The big downward spikes in the front month-back month spreads correspond with the days around expiry.

How does strategic behavior/market power/manipulation play into this? The model of short manipulation in my 1996 book (only $169 paperback–buy two!) and 1993 J. of Business article formalizes the argument, but the intuition is fairly straightforward. Manipulation exploits frictions and bottlenecks. (My article/book refer to “frictional manipulations.”). There is now a huge friction/bottleneck in Cushing–constrained storage. This bottleneck makes the demand curve for crude at Cushing extremely inelastic, and means that the movement of even small excess quantities of oil into that location will cause prices to decline dramatically.

In these conditions, a trader, or a group of traders with modest-sized short positions can exercise market power by delivering even a small amount of oil over and above the quantity that should flow to Cushing. This drives down the price and allows the trader or traders to cover his (their) position(s) at artificially low prices.

In this situation, the storage bottleneck is the gasoline, the exercise of the market power is the match. With 100,000,000 barrels of open long positions needing to liquidate, given the storage constraint, the resulting conflagration can be epic.

This is, at this stage, a hypothesis. It is a possible explanation of the beyond extreme movements observed today. Under the circumstances, it is a very plausible explanation, and one that deserves scrutiny. And given the amount of money that changed hands today (~$6 billion on a mark-to-market basis) I’m sure that it will get it.

The only parallel I can think of is the onion market in 1955, when the movement of a couple of superfluous carloads of onions into Chicago, and delivery thereof against futures, caused the price to crash below the cost of the bags that they had to be delivered in. There was no demand for the onions (being perishable, and people eat only so many hot dogs), so many of the excess plants ended up getting dumped into the Chicago River. (Which, in 1955, probably improved the water quality.) (Another irony being that Chicago means “stinky onions” in the Miami-Illinois Indian language.)

In 1955, demand was inelastic because onions are perishable (i.e., they can’t be stored). In a way, the lack of storage space makes oil perishable. Even if that analogy isn’t perfect, the economics are the same: an economic constraint (the non-storability of, or the lack of storage space) leads to extremely inelastic demand that makes short market power manipulation possible.

Tomorrow is the last trading day for CLK20. Strap it up! It’s going to be a wild ride.

You are seeing a lot of covid-19 numbers thrown around. Virtually all of those numbers are bullshit.

The death rates are bullshit. In a given country, there is considerable subjectivity regarding how deaths are qualified. The Great Scarfini* (Dr. Deborah Birx) pretty much let that cat out of the bag when she acknowledged that not only are the decedents who test positive (regardless of other co-morbidities) declared as covid-19 deaths, but those who have some colorable connection to covid-19 (clinical presentation, exposure to someone who tested positive) are declared to be covid-19 deaths.

It is likely that hospitals and physicians–and politicians–have an incentive to attribute deaths to covid-19. These incentives can be financial (a hospital could get greater compensation from covid victim than someone dying of something else) or power (death numbers are being used to justify draconian restrictions).

Further, different countries use different methods to count deaths.

What we are really interested in is people who would not have died but for covid-19. The official death statistics do NOT do this. And the fact that virtually all of the dead are aged and/or have multiple serious health problems, a but for attribution is dubious even in the presence of a positive test.

The only rigorous way to estimate these but for deaths is excess deaths (i.e., deaths in excess of expected deaths, conditioning on time of year, demographics, etc.). And preferably excess deaths from respiratory illness (or at least excess deaths from non-accidental causes). This is a good template for the analysis. This also presents some good cross-country data, which shows that in Italy and Spain there is evidence of excess deaths. Elsewhere? Not so much. Of particular interest is Sweden, which has implemented mainly voluntary social distancing measures, to the hysterical response of those deeply invested in mandatory lockdowns.

Do this for a variety of jurisdictions (countries, states in the US) and you would have enough cross-sectional and time series variation to do some real analysis that could provide reasonable support for policy decisions..

The case numbers are bullshit, at least if you want to measure infection rates. As I’ve been saying for weeks, there are so many selection biases that the numbers tell you NOTHING about the prevalence of the virus in the population, either at a point in time or crucially over time. Indeed, the CDC guidelines could be titled “How to Produce a Wildly Biased Sample”:

This testing protocol could be justified on clinical and diagnostic grounds, but it is a disaster from the perspective of generating data that is useful in shaping policy.

Further, trends in positive test numbers is driven to a considerable degree by . . . a greater number of tests.

The graphs that you see depicting trends deaths or cases across countries over time are bullshit. They are bullshit because the inherit all the flaws of the data discussed above (exacerbated by the fundamentally different data reporting methods across countries), and they almost fail to adjust for population size or demographic characteristics.

Chinese numbers are obviously bullshit. No need to elaborate this point.

The models that are being used to drive (or at least justify) lockdowns are bullshit. Their predictions went from apocalyptic to well, a small fraction of apocalyptic. Sometimes between one day and the next. Models should be evaluated on predictive accuracy. The predictions of these models have proved to be excessively pessimistic, i.e., bullshit.

And don’t buy the line that the lockdowns reduced the death tolls. For one thing, many of the models’ predictions included the effects of social distancing–and still came out way too high. For another, many countries’ death and case rates (above caveats apply) peaked before the lockdowns could have had any effect.

I keep hearing the IHME model referred to as the “top model.” Who says? On what basis? Basically because somebody else said it. And oh, Bill Gates is somehow involved. So that claim is bullshit too.

Also be very suspicious that the modelers are very opaque. We don’t see their assumptions or their methods. Notoriously, the most influential modeling team (at least initially) that did more than any to spark the panic, has not released its modeling code.

At least the honest modelers admit that social isolation and shutting down the economy doesn’t change the integral under the curve (i.e., the total number of deaths) but merely the time pattern of those deaths. And some epidemiologists claim that extending the period of time before the burnout may result in a higher number of total deaths.

But even putting that possibility for a higher total toll aside, the argument is made that it is necessary to “flatten the curve” in order to reduce the burden on the healthcare system. Well, one thing the models vastly overpredicted hospitalization/ICU visits as well. And I have yet to see any evidence of systematic shortages of ICU beds/ventilators. Yes, there are hotspots. But that just means that we need to understand the hotspots–and the non-hotspots–better.

Along those lines, I can’t say the numbers on ICU utilization are bullshit–because the numbers are largely non-existent. Instead we’ve had anecdotal journalistic (i.e., “if it bleeds it leads”) accounts that provide no objective quantitative standard by which to evaluate how binding the constraints are in the healthcare system.

But again the issue is cost-benefit. Basically what lockdowns do is discount future deaths/cases relative to present deaths/cases (since they accept an approximately equal number of future deaths for each death that does not occur today). And the discount rate is huge. We are losing trillions of dollars in lost output/income to push some deaths into the future. The interest rate is astronomical. Put differently, we are paying an immense price to kick the can down the road.

I understand the the supply of ICU beds, ventilators, physicians and nurses is pretty inelastic over the short run. But even given pretty substantial inelasticity, it would be far more efficient to throw billions at expanding capacity in the short run than to sacrifice ~25 percent of world income to reallocate the deaths over time. Capacity is not a fate. It is a choice.

And the fact that well into the crisis the foretold capacity disaster in hospitals has not been realized, the additional capacity required may well be quite small.

There is also the issue of how much the temporal pattern of deaths will really change. This depends on a variety of factors, including when the virus first spread and its virulence. The more we learn, the more likely it is that the virus has been spreading since late-fall/early-winter 2020. Which means that the lockdowns are reactive, not proactive, and that they have little impact even on the time pattern of deaths let alone the number: they are the proverbial locking the barn door after the horse done bolted.

In brief: our betters are destroying futures based on bullshit data. It’s as simple as that. And they are vastly increasing their power as a result, so they are destroying freedoms too.

In an earlier post I said that we have to grasp the nettle and decide what price are we willing to pay to save a life (usually of an aged, ill person). But it’s actually worse than that. It is very likely that the real question is: how much are we willing to pay to defer a death (of such a person) a few months? The cost that those who govern (or rule) us (and those who support them) are apparently willing to pay is astronomical.

*Dr. Birx is always adorned with a scarf. On my first trip to NYC in 1978, when NYC was near its nadir, I saw an obviously psychotic individual dancing on near Grand Central Station waving around a long scarf. Every once and a while he would shout “I AM THE GREAT SCARFINI” and then start dancing again.

There is an ongoing outbreak of Covid-19 on the Nimitz class carrier, USS Roosevelt. The outbreak is severe, and today the CO, Capt. Brett Crozier, wrote an impassioned letter requesting onshore quarantine of the entire crew.

The first criticism that Captain Crozier raises is “Inappropriate focus on testing.” Crozier objects that tests provide little information: given the close proximity of those on board, they have presumptively been exposed, and should be isolated. Further, Crozier quantifies a relatively high rate of false negatives.

The captain is certainly correct regarding what is his primary responsibility–his ship and crew. But testing on the Roosevelt could provide invaluable information that could lead to far better policies in the United States, and the world at large. From a larger perspective, the opportunity for testing on the Roosevelt is something that cannot be allowed to slip away.

As I have noted repeatedly here, and on Twitter, policy is currently based on incredibly flawed data. In fact, the most useful piece of data is from a cruise ship Diamond Princess. The Roosevelt could provide a far bigger sample, and one that contains valuable information about the impact on non-elderly, relatively healthy individuals.

Even one of the things that Captain Crozier objects to–the presence of false negatives–is important. Quantifying that rate can provide information that greatly improves the inferences that can be drawn from other samples (apropos my earlier Bayes Rule post).

I understand that there are myriad competing considerations here. The health of the crew. The operational readiness of one of the most important combatants in the US Navy. Operational safety–e.g., who is going to operate the reactors and ensure that other systems are maintained properly even if the ship is not deployed? (You don’t leave a CVN parked in the driveway for a few weeks.)

Among those competing considerations, from Captain Crozier’s perspective, testing is indeed a near irrelevance. But it is extremely relevant for informing how we deal with the crisis around the world. The social value of this data is great indeed. I hope that those in the Pentagon, and in the administration, find a way to address Captain Crozier’s concerns while at the same time seizing on this opportunity to generate data that could save thousands of lives and trillions of dollars.

Coda: Another benefit here is that by the nature of the military, there would be excellent data at hand on virtually any interesting covariate you can think of–age, health conditions, socioeconomic background, etc. Combining BUPERS data with testing and clinical data from CVN-71 could provide a plethora of actionable insights.

In the aftermath of the last crisis, I played the role of Clearing Cassandra, warning that in the next crisis, supersizing of derivatives clearing would create systemic risks not because clearinghouses would fail, but because of the consequences of what they would do to survive: hike initial margins and collect huge variation margin payments that would suck liquidity out of the system at the same time liquidity supply contracted. This, in turn, would lead to asset fire sales, that would distort asset prices which would lead to further knock-on effects.

I wrote a lot about this 2008-2012, but here is a convenient link. Key quote from the abstract:

The author also believes that the larger collateral mandates and frequent marking‐to‐market will make the financial system more vulnerable since margin requirements tend to be “pro‐cyclical.” And more rigid collateralization mechanisms can restrict the supply of funding liquidity, and lead to spikes in funding liquidity demand that can reduce the liquidity of traded instruments and generate destabilizing feedback loops. 

Well, the next crisis is here, and these (conditional) predictions are being borne out. In spades.

Here’s what I wrote a few days ago as a contribution to the Regulatory Fundamentals Group newsletter:

In the aftermath of the last crisis of 2008-2009, G20 nations decided to mandate clearing of standardized OTC derivatives transactions.  The current coronavirus crisis is the first since those reforms were implemented (via Dodd-Frank in the US, for example), and this therefore gives the first opportunity to evaluate the performance of the supersized clearing ecosystem in “wartime” conditions.  

So far, despite the extreme price movements across the entire derivatives universe–equities, fixed income, currencies, and commodities (especially oil)–there have been no indications that clearinghouses have faced either financial or operational disruption.  No clearing members have defaulted, and as of now, there have been no serious concerns than any are on the verge of default. 

That said, there are two major reasons for concern.

First, the unprecedented volatility and uncertainty show no signs of dissipating, and as long as it continues, major financial institutions–including clearing firms–are at risk.  The present crisis did not originate in the banking/shadow banking sector (as the previous one did), but it is now demonstrably affecting it.  There are strong indicators of stress in the financial system, such as the blowouts in FRA-OIS spreads and dollar swap rates (both harbingers of the last crisis).  Central banks have intervened aggressively, but these worrying signs have eased only slightly.  

Second, as I wrote repeatedly during the debate over clearing mandates in the post-2008 crisis period, the most insidious systemic risk that supersized clearing creates is not the potential for the failure of a clearinghouse (triggered by the failure of one or more clearing members).  Instead, the biggest clearing-related systemic risk is that the very measures that clearinghouses take to ensure their integrity–specifically, frequent variation margining/marking-to-market–lead to large increases in the demand for liquidity precisely during circumstances when liquidity is evaporating.  Margin payments during the past several weeks have hit unprecedented–and indeed, previously unimaginable–levels.  The need to fund these payments has inevitably increased the demand for liquidity, and contributed to the extraordinary demand for liquidity and the concomitant indicators stressed liquidity conditions (e.g., the spreads and extraordinary central bank actions mentioned earlier).  It is impossible to quantify this impact at present, but it is plausibly large.  

In sum, the post-2008 Crisis clearing system is operating as designed during the 2020 Crisis, but it is unclear whether that is a feature, or a bug.  

It is becoming more clear: Bug, and the bugs are breeding. There have been multiple stories over the last couple of days of margin calls on hedging positions causing fire sales, with attendant price dislocations in markets like for mortgages. Like here, here, and here. I guarantee there are more than have been reported, and there will be still more. Indeed, I bet if you look at any pricing anomaly, it has been created by, or exacerbated by, margin calls. (Look at the muni market, for instance.)

But those in charge still don’t get it. CFTC chairman Heath Tarbert delivers happy talk in the WSJ, claiming that everything is hunky dory because all them margins bein’ paid! and as a result, derivatives markets are functioning, CCPs aren’t failing, etc.

This is exactly the kind of non-systemic thinking about systemic risk that I railed about a decade ago. Mr. Tarbert has a siloed view: he is assigned some authority over a subset of the financial system, sees that it is working fine, and concludes that rules regarding that subset are beneficial for the system as a whole.

Wrong. Wrong. Wrong. Wrong. WRONG.

You have to look at the system as a whole, and how the pieces of the system interact.

In the post-last-crisis period I wrote about the “Levee Effect”, namely, that measures designed to protect one part of the financial system would flood others, with ambiguous (at best) systemic consequences. The cascading margins and the effects of those margin calls are exactly what I warned about (to the accompaniment a collective shrug by those who mattered, which is why we are where we are).

What we are seeing is unintended consequences–unintended, but not unforeseeable.

Speaking of unintended consequences, perhaps one good effect of September’s repo market seizure was that it awoke the Fed to its actual job–providing liquidity in times of stress. The facilities put in place in the aftermath of the September SNAFU are being expanded–by orders of magnitude–to deal with the current spike in liquidity demand (including the part of the spike due to margin issues). Thank God the Fed didn’t have to think this up on the fly.

It also appears that either (a) the restrictions on the Fed imposed by Frankendodd are not operative now, or (b) the Fed is saying IDGAF so sue me and blowing through them. Either way, such liquidity seizure are what the Fed was created to address.

I’ve expressed considerable skepticism about relying on test data to craft COVID-19 (AKA CCPVID-19) policy responses. This note formalizes the basis for my skepticism. Testing data would provide an accurate measure of the prevalence of severe infection if (a) the tests had low rates of false positives and false negatives, and (b) testing was random. Neither condition is remotely correct. Meaning that the test-based statistics are an extremely poor guide for policymakers, and a particularly dubious basis for driving the world economy into a depression, at the cost of trillions of dollars.

So what should we look at? If this is a particularly prevalent, virulent, and deadly respiratory disease, it will result in elevated levels of hospital admissions or physician visits for respiratory illness, and elevated levels of death from respiratory causes. That’s what we should be looking at. Or more to the point, what policymakers should be looking at. Is this a particularly deadly and widespread disease? If it is, it will have measurable effects on mortality and hospital admissions.

The CDC does collect data on influenza. Unfortunately, many of the statistics condition on a positive influenza test. For example, hospital admissions with a positive influenza test. That is not helpful, because we are focused on something other than the influenzas the CDC tracks. But the CDC does report deaths from influenza and pneumonia. That is more useful, as a deadly new respiratory illness should lead to higher pneumonia death rates.

Through last week, these data demonstrate little elevation on a national or regional basis. There was a spike in deaths above the “threshold” level early in 2020 (where the threshold basically is at the 5 percent significance level above the seasonally adjusted baseline), but subsequently it converged almost back to the baseline:

It is particularly interesting to compare 2019-20 with 2017-18. Heretofore, 2019-20 compares very favorably to that year, and even to less extreme years 2016-17 and 2018-19. Through now, in other words, 2019-20 does not look at all unusual.

The CDC also tracks data on those seeking medical treatment for flu-like symptoms: these data do not require a positive influenza test, and thus should reflect people suffering flu-like symptoms caused by something other than the flu. These data show somewhat higher levels for 2019-20 compared to previous years (except for 2017-18, which was much higher), but not extremely so. The main worrying aspect to the 2019-20 data is that they do not appear to be declining as rapidly with the approach of spring as in prior years. But the data do not exhibit a huge spike–they are just declining less rapidly than in prior years.

Yes, these data are backwards looking. I can imagine scenarios, such as the late introduction of CCPVID-19 into the US, which would mean that the wave of deaths/illness would not be manifest in the data, as it is still to come. But there are indications that the virus has been on the loose in the US at least since mid-January, and given its existence in China no later than mid-November, it could have been present in the US prior to mid-January. If it is indeed highly contagious and deadly, it should be leaving tracks in the mortality data.

It would be highly informative to have such data for other countries. I am not aware of it in as accessible a form as is provided by the CDC. If anyone can point me to it, that would be greatly appreciated.

You might argue that I am whistling past the graveyard. All I can say is that the data that alarmists point to is highly unreliable (and inherently so), and the reliable data as of yet demonstrate nothing out of the ordinary on the dimension that really matters–people dying from respiratory ailments.

What I can say with considerable confidence is that policymaking is driven by flawed data, and that there are types of data that would be more informative, and which are not infected by (deliberate choice of words) the problems inherent in the flawed data that dominates public discourse, and apparently dominates public policymaking. Produce that data. Disseminate that data. Make sure policymakers are aware of it, and are aware of the deficiencies of the data we hear about 24/7.

One of the refrains we’ve heard repeatedly during the Panicdemic (which is arguably worse than the pandemic) is: “We Need More Tests! We Need More Tests!”

There is a Chicken Little vibe to these calls for testing. A sense that people are running around like the sky is falling, and not thinking through the right testing strategy.

What are tests for? One is for diagnostic purposes in specific cases. To be frank, the value of such tests is minimal. There is no unique therapy for acute Wuhan Virus sufferers. The protocol is to treat the symptoms of acute respiratory distress the same way as one would treat such distress from other causes. So knowing that someone’s acute distress is caused by agent X as opposed to agent Y is of limited therapeutic value.

Insofar as identifying someone expressing symptoms would help identify others so exposed, a much more efficient strategy is to presume that the symptomatic individual is suffering from WV, track his/her contacts, and monitor and quarantine said individuals accordingly. Yes, there will be Type I (false positive) errors, but the cost of such errors is likely to be relatively small if an individual is suffering from an acute condition, regardless of the exact pathogen that caused it. That pathogen is obviously capable of causing severe problems, so why not isolate those exposed to it, even if you don’t know exactly what it is?

Another purpose of testing is to collect information about the prevalence, virulence, contagiousness, and fatality of the disease. Such information can be used to optimize the policy response.

Testing those who are symptomatic and/or have been exposed is exactly the wrong way to go about that. Such a testing strategy is rife with sample selection bias.

For weeks (mainly on Twitter) I advocated construction of a random panel data set. Select people at random. Test them, and test them at regular intervals–including those who tested negative. This would provide an unbiased sample that would permit more reasoned assessments and judgments about the nature of the pathogen. We could see how many people had contracted the virus, how many people they infected, the mortality rate (and how the mortality rate varied with age, health status, etc.), and the trajectory of the virus.

If that had been done, say, in January when shit started to get real in China, perhaps we could have been able to condition policy on better information. (Not to mention if the CCP had done that in, say, December, when it knew it had a problem on its hands–but decided to suppress information rather than suppress the pathogen.)

Why didn’t our Technocrats figure this out? Yeah. We should put more of our lives in their hands.

But that opportunity to get unbiased data has passed. Now we are forced to respond based on the most sketchy and biased data. Chicken Little proposals about testing will generate . . . more biased data. Which is arguably worse than useless.

What a helluva few weeks it’s been, eh boys and girls? By way of post mortem (hopefully?) rather than prediction, here’s my take.

Under “normal” circumstances, two factors drive asset valuations: expectations of cash flows, and the rate at which investors discount those cash flows. COVID-19–Wuhan Virus, to call it by its proper name–has has profound influence on both.

WV has caused a major aggregate supply shock, and an aggregate demand shock, and these amplify one another. The aggregate supply shock stems from shutdown of productive capacity due to social distancing. And people who aren’t working aren’t earning and aren’t spending, hence the aggregate demand shock.

These developments obviously reduce the income streams from assets (e.g., corporate profits). That’s a negative for stocks.

As an aside, these factors defy traditional policy prescriptions. Monetary and fiscal policy are focused on addressing aggregate demand deficiencies, i.e., trying to move demand-deficient economies (where demand deficiencies arise from price rigidity and nominal shocks) back to the production possibilities frontier. Supply shocks shrink the PPF. Pushing the PPF back to its normal state in current circumstances is a function of public health policy, and even that is likely to be problematic given the huge uncertainties (that I discuss below) and the dubious competence of government authorities (which I discussed last week).

The pandemic nature of WV also makes it the systematic shock par excellence. It hits everyone and every asset class, and cannot be diversified away. A big increase in systematic risk results in a big increase in risk premia, meaning that the already depressed expected cash flows on risky assets get discounted at a higher rate, leading to lower valuations.

A lot higher rate, evidently. Why? Most likely because of the extreme uncertainty about the virus. Data on how infectious it is, how many people have been infected, the fatality rate, how it will be affected by warmer weather, etc., are extremely unreliable. In other words, we know almost nothing about the salient considerations.

This is in part due to lack of testing, and to inherent defects in the testing: those who get tested are disproportionately likely to be symptomatic, exposed, or hypochondriacal, leading to extreme sample selection biases. The tests are apparently unreliable, with high rates of false positives and false negatives. The RNA tests cannot detect past infections. It is in part due to the novelty of the virus. Is it like influenza, and will hence burn out when temperatures warm? Or not?

Another major source of uncertainty is due to the fact that the initial outbreak in China was covered up by the evil CCP regime. (Which now, in an Orwellian twistedness that only totalitarian regimes can muster, is boasting that it will save the world. And which is blaming the United States for its own abject failures. Which is why I insist on calling it the Wuhan Virus–so go ahead, call me a racist. IDGAF.) Thus, data from Ground Zero is lacking, or wildly unreliable. (Ground One–Iran–is equally duplicitous, and equally malign.)

This huge uncertainty regarding a major systematic factor leads to even greater discount rates–and hence to lower stock prices.

And then there is the truly disturbing factor. These textbook causal channels (lower expected cash flows, higher discount rates) have in turn caused changes in asset prices that force portfolio adjustments that move us into the realm of positive feedback mechanisms (which usually have negative effects!) and non-linearities. This represents a shift from “normal” times to decidedly abnormal ones.

When some investors engage in leveraged trading strategies, big price moves can force them to unwind/liquidate these strategies because they can no longer fund their large losses. These unwinds move asset prices yet more (as those who placed a lower valuation on these assets must absorb them from the levered, high-value owners who are forced to sell them). Which can force further unwinds, in perhaps completely unrelated assets.

Not knowing the extent or nature of these trading strategies, or the degree of leverage, it is virtually impossible to understand how these effects may cascade through the markets.

The most evident indicators of these stresses are in the funding markets. And we are seeing such stresses. The FRA-OIS spread (known in a previous incarnation–e.g., 2008–as the LIBOR-OIS spread) has blown out. Dollar swap rates are blowing out. The most vanilla of spreads–the basis net of carry between Treasury futures and the cheapest-to-deliver Treasury–have blown out. Further, the Fed has pumped in huge amounts liquidity into the system, and these alarming spread movements have not reversed. (One shudders to think they would have been worse absent such intervention.)

One thing to keep an eye on is derivatives clearing. As I warned repeatedly during the drive to mandate clearing, the true test of this mechanism is during periods of market disruption when large price moves trigger large margin calls.

Heretofore the clearing system seems to have operated without disruption. I note, however, that the strains in the funding markets likely reflect in part the need for liquidity to make margin calls. Big margin calls that must be met in near real-time contribute to stresses in the funding markets. Clearinghouses themselves may survive, but at the cost of imposing huge costs elsewhere in the financial system. (In my earlier writing on the systemic impacts of clearing mandates, I referred to this as the Levee Effect.)

The totally unnecessary side-show in the oil markets, where Putin and Mohammed bin Salman are waging an insane grudge match, is only contributing to these margin call-related strains. (Noticing a theme here? Authoritarian governments obsessed with control and “stability” have a preternatural disposition to creating chaos.)

Perhaps the only saving grace now, as opposed to 2008, is that the shock did not arise originally from the credit and liquidity supply sector, i.e., banks and shadow banks. But the credit/liquidity supply sector is clearly under strain, and if parts of it break under that strain yet another round of extremely disruptive knock-on effects will occur. Fortunately, this is one area where central banks can palliate, if not eliminate, the strains. (I say can, because being run by humans, there is no guarantee they will.)

Viruses operate according to their own imperatives, and the imperatives of one virus can differ dramatically from those of others. Pandemic shocks are inherently systematic risks, and the nature of the current risk is only dimly understood because we do not understand the imperatives of this particular virus. Indeed, it might be fair to put it in the category of Knightian Uncertainty, rather than risk. The shock is big enough to trigger non-linear feedbacks, which are themselves virtually impossible to predict.

In other words. We’ve been on a helluva ride. We’re in for a helluva right. Strap it tight, folks.

In a reply to the Paul Romer post, ex-colleague and friend Scott points out the richness of Romer calling for a “new humility” among economists. But not all economists, mind you, just those who value freedom and express skepticism about regulation:

Among these pretend economists, the ones who prized supposed freedom (especially freedom from regulation) over all other concerns proved most useful—not to society at large but to companies that wanted the leeway to generate a profit even if they did pervasive harm in the process. 

This brings to mind Hanna Arendt’s observation: “One of the greatest advantages of the totalitarian elites of the twenties and thirties was to turn any statement of fact into a question of motive.” This is exactly what Romer does. He ascribes venal motives to those who dare disagree with his eminence. He has precious few facts in his article, but casts many aspersions on motives of those with whom he disagrees.

Romer again refers to economists plural, but provides a single illustration–Alan Greenspan. To make him the Representative Agent for an entire swath of the economics profession is utterly ridiculous. He was the head of an economics consulting firm for decades before ascending to the Fed. He never held an academic appointment. He did not publish in peer-reviewed journals. He is an eminently bad person to serve as the epitome of regulation-skeptical economists.

Further, Romer has things exactly upside down. It was–and is–the pro-regulatory/pro-state/pro-government contingent of the economics profession (which has always represented a majority) that could have used, and could use, a dose of humility. Always dismissive of the knowledge problem, they believed that they could design regulatory and legislative schemes that would achieve superior outcomes, only to find out that unintended consequences are a bitch, and regulatory capture undermines the most well-intended schemes.

Indeed, in financial regulation in particular, major financial institutions play B’rer Rabbit, begging not to be thrown in the regulation briar patch, only to profit quite well from it.

In fact, it has long been government/regulation-skeptical economists who have counseled against hubris. In a book tellingly titled “The Fatal Conceit,” Hayek famously wrote: “The curious task of economics is to demonstrate to men how little they really know about what they imagine they can design.”

The main issue I’ve always had with this quote is that it clearly expresses what Hayek thought economics should do, but did not accurately express the views of the profession at large. Those who view economies and markets as complex systems, as emergent orders, are very dubious of their ability–or the ability of anyone–to impose their designs. But they are, and always have been, a minority.

Another example of a great economist who was humble about attempts to control such complex systems via government was Adam Smith, who famously said:

The man of system, on the contrary, is apt to be very wise in his own conceit [there’s that word again]; and is often so enamoured with the supposed beauty of his own ideal plan of government, that he cannot suffer the smallest deviation from any part of it. He goes on to establish it completely and in all its parts, without any regard either to the great interests, or to the strong prejudices which may oppose it.

He seems to imagine that he can arrange the different members of a great society with as much ease as the hand arranges the different pieces upon a chess-board. He does not consider that the pieces upon the chess-board have no other principle of motion besides that which the hand impresses upon them; but that, in the great chess-board of human society, every single piece has a principle of motion of its own, altogether different from that which the legislature might chuse to impress upon it. If those two principles coincide and act in the same direction, the game of human society will go on easily and harmoniously, and is very likely to be happy and successful. If they are opposite or different, the game will go on miserably, and the society must be at all times in the highest degree of disorder.

In sum, Paul Romer is projecting. As Smith recognized centuries ago, and Hayek echoed many years later, it is the statists who need humility, and those who advocate a reliance on freedom often do so out of a humble conviction of the difficulty in even understanding the operation of complex systems, let alone controlling or regulating them.