Streetwise Professor

Perpetual futures are all the rage these days. Few things in the futures space have attracted as much attention as “perps,” in particular in the aftermath of a surge in crude oil perpetual futures trading on Hyperliquid over the weekend when the US and Israel unleashed their bombing campaign against Iran–a time when the major exchanges were closed. Below is a policy piece I prepared for ICE that examines the economics of perps. The basic conclusion: they are suitable primarily for a retail trading niche which represents a small fraction of incumbent exchanges’ business.

The Economics of Perpetual Commodity Futures

Introduction

Perpetual futures (“perpetuals” or “perps”) were developed initially to solve specific security problems in trading cryptocurrency (“crypto”). From their birthplace in the crypto markets, perpetual futures have moved into the trading of virtually every major asset class, including inter alia equities, equity indices, and currencies. And now they are making inroads into commodities like oil and corn. The Iran War triggered broader awareness of this latter trend: the volume of a crude oil perpetual futures contract traded on Hyperliquid soared to $1.7 billion with the onset of the war.[1]

This, in turn, has sparked considerable debate over how perpetual futures will affect the traditional conventional commodity futures markets, including ICE and the CME. Herein I present an economic analysis of perpetual futures. [2] The primary conclusions are:

• Due to their design, perpetual futures are ill-suited for market participants who account for a preponderant share of trading on incumbent exchanges. These are large commercial and financial entities who use traditional futures contracts to transfer risk. Crucially, the spot price mimicking structure of perpetual futures does not align with the risks that commercial hedgers manage.
• Instead, perpetual futures are tailored to small “retail” traders. As a result, perpetual futures are likely to be a niche product that is attractive to this clientele.
• The competitive impact of perpetual futures on commodity derivatives markets will depend on whether these contracts attract retail volume from incumbent exchanges, or instead disproportionately attract traders who would not trade on these exchanges.

Commodity Perpetual Futures

Perpetual futures emerged in the cryptocurrency market to solve a particular problem: recurrent thefts of cryptocurrency held by exchanges trading actual crypto (“spot exchanges”).[3] Introduced by BitMEX in 2013, perpetual futures avoided the security issues that had plagued spot exchanges because they were cash-settled instruments that did not allow, require, or countenance the delivery of crypto assets, or the custody thereof.

Incumbent crypto exchanges traded crypto assets on a spot basis, in much the same way that the NYSE or Nasdaq trade equities on a spot basis. Perpetual futures were designed to mimic spot trading but to avoid the security issues just described. Specifically, with perpetual futures, on a periodic basis throughout the day a perpetual futures exchange determines a price from an “oracle” that takes the prices from spot exchanges. The perpetual futures exchange then compares the price of its perpetual contract (for Bitcoin, say) to the price derived from the oracle. If the prevailing price on the exchange’s perpetual diverges from the oracle price (a measure of the spot price of the instrument), the exchange determines a “funding rate.” If the perpetual price is above the oracle price, the contract long pays the funding rate, and the short receives a funding rate payment.

This is intended to, and has the effect of, causing the perpetual price to track the spot price prevailing on spot exchanges. A perpetual price above the spot price incentivizes sale of the perpetual to capture the funding payment, thereby driving the price: conversely, a perpetual price below the spot price incentivizes a purchase of the perpetual, again to capture the funding payment, thereby driving up the price. These sales and purchases tend to drive the perpetual price towards the spot price, thereby causing the perpetual price to track spot prices closely. In this way, a perpetual futures trader can obtain exposure to the underlying spot price as could be obtained directly on a spot exchange, but without the security risks posed by trading on a spot exchange.

Market participants soon realized that perpetual futures offered other advantages.

First, unlike conventional futures contracts, perpetual futures have no fixed expiration date. Thus, whereas trading traditional futures requires those who wish to hold a position over a contract expiration date must “roll” these positions on or before the expiration date, this is unnecessary for a perpetual future. For example, with conventional futures, a long must sell the expiring contract and buy a later-expiring contract to maintain a long position, whereas a trader long the perpetual need not do anything. This economizes on transactions costs. 

Second, like futures but unlike spot transactions, perpetual futures incorporate leverage. For example, perpetual exchanges offer 10x, 20x, and sometimes as much as 50x leverage.

Third, like conventional futures, perpetual futures are easier to short than actual cryptocurrencies.

Fourth, perpetual futures offer 24×7 trading. In contrast, at present, conventional exchanges trade for less than 24 hours a day, and on weekdays.

As a result of these various features, perpetual futures have come to dominate crypto trading. A recent JP Morgan study estimates that perpetuals account for 90 percent of crypto derivatives trading (with conventional futures traded on exchanges like the CME representing the rest), and that perpetual futures volumes exceed spot exchange volumes.

Perpetual futures differ from conventional futures in other ways. Unlike conventional futures traded on ICE or CME, many perpetual futures are not centrally cleared.

Moreover, and relatedly, perpetual futures often have an “auto-liquidation” feature. As an example, on Hyperliquid, as on a traditional cleared market, the exchange sets an initial margin level and a maintenance margin level: these levels effectively determine leverage, with lower margins being associated with higher leverage. If the market moves against a trader and the loss drives the trader’s account equity below the maintenance margin level, Hyperliquid reduces or closes the position by engaging submitting an offsetting market order into the exchange limit order book, for example, selling when a long’s account equity is insufficient.[4] If the trader’s equity falls below two-thirds of the maintenance margin level, the position is transferred to a “Liquidation Vault,” which essentially internalizes the offsetting transaction.[5] In extreme circumstances, when the market liquidation fails and the Liquidation Vault cannot safely absorb the position loss, the exchange may trigger “Auto-Deleveraging,” whereby profitable positions on the opposite side of the market can have their positions reduced. This is effectively a “tear up” procedure.

Auto-liquidation mitigates credit risk in leveraged instruments, but can have destabilizing effects. For example, a large price increase likely results in large auto-liquidations of short positions, which tends to cause prices to increase further. In the case of perpetual futures, the price change will manifest itself primarily in the form of an increased spread between the perpetual price and the spot price it is intended to track, leading to large funding payments that require access to funding liquidity. Liquidity strains can exacerbate this mechanism.[6]

Commodity Perpetual Futures

Although perpetual futures developed in crypto markets to address crypto-specific problems, their market success has spurred the extension of the concept to a wide variety of other underlying instruments, most notably commodities such as crude oil. With respect to commodities specifically, perpetual futures differ in salient dimensions from crypto perpetual futures and even perpetual futures on other non-crypto-underlyings, such as equity indices.

There is one overriding reason for this difference: there are no transparent and liquid spot markets as there are for cryptocurrencies and many other financial assets. Thus, it is impossible to create commodity perpetual futures that track (as a result of funding payments) a spot commodity price, because for all practical purposes such a thing does not exist.

Instead, existing commodity perpetual futures are designed to track the prices of liquid futures contracts, such as CME NYMEX WTI Crude Oil Futures and ICE Brent Crude Oil Futures. With that difference, commodity perpetual futures operate similarly to the crypto perpetuals described above.

Specifically, a perpetual futures exchange obtains the price of the futures it is tracking from an oracle, and sets funding payments based on the difference between the perpetual futures price and the oracle price. For example, on 3 August 2026, Hyperliquid receives the price of September 2026 WTI Crude Oil Futures on an hourly basis from its oracle.[7]  As described above, it determines a funding payment based on the difference between the WTI perpetual futures price at that time and the Crude Oil Futures price determined from the oracle. Further, as already described, funding payments incentivize market participants to make transactions that tend to diminish the difference between the perpetual futures price and the price of the underlying futures contract.

As a result of this mechanism, the perpetual futures price tracks a futures price. But what futures price? The underlying futures contracts expire, meaning that the perpetual futures exchange must periodically shift the identity of the underlying futures contract whose price the perpetual future tracks.

There are many ways to do this. One way would be to shift at the end of trading of a particular futures contract, which would be 2:30 p.m. Eastern Time on the last trading day of an expiring contract, at which time the tracking would shift from the just-expired contract to the now front-month contract.

Due to the nature of commodity forward curves, where prices of adjacent futures contracts can differ substantially (a subject I return to below), such a mechanism would lead to abrupt changes in the perpetual futures price. Exchanges have therefore adopted other mechanisms that result in a smoother transition.

Hyperliquid, for example, has a five-day roll period running from the fifth to the tenth business day of the last month of trading of a contract.[8] On the fifth day, the reference price equals the price of the next expiring (front month) contract. On the fourth day, the reference price equals .8 times the front month price and .2 times the second month price. On each successive day, the weight on the front month contract falls by 20 percent, and the weight on the second month contract rises by that amount. This continues until on the tenth business day of the month, at which time the reference price equals 100 percent of the second month contract price. This mechanism effectively smooths out the effect of differences between front month and second-month underlying prices on the price of the perpetual.

The Economic Function of Commodity Futures

Futures markets serve two primary purposes: risk transfer and price discovery. Risk transfer relates to hedging: those who wish to reduce their exposure to the price of a particular commodity can take an offsetting exposure through the futures market, thereby transferring their risk either to a hedger with a mirror image risk exposure, or to a speculator who is willing to take on the risk in the expectation of earning a profit. Price discovery is a valuable byproduct of futures trading undertaken for hedging or speculative purposes. Those with private information (about fundamentals, for instance) trade on that information, for instance buying when their information suggests that the current price is too low. These privately informed trades tend to move prices in the direction of the private information that motivates them, thereby causing prices to reflect this information. The aggregation of private information in futures prices through this mechanism is valuable, because more informative prices guide better resource allocation decisions.

Different types of market participants have different reasons for trading. In commodities, large commercial entities’ trading is largely (though not exclusively) for hedging purposes. For example, commodity trading firms hedging the price risk of oil inventories or oil producers hedging the prices of their anticipated production are important market participants. Large banks (swap dealers) also use futures markets to hedge the exposure they incur when providing over-the-counter hedging instruments (e.g., swaps) to their clients (which could include firms like the oil producer just mentioned). Speculators now typically consist of large institutions, including hedge funds, family offices, commodity trading advisors, and financial institutions. In addition to large commercial and institutional entities, smaller entities such as individual retail traders also participate in the futures market.

Although imperfect for a variety of reasons, the Commitment of Trader (“COT”) Reports produced weekly by the US Commodity Futures Trading Commission (“CFTC”) provide a useful breakdown of futures trading activity by type of trader. These COT reports disclose the quantity of contracts held by several categories of traders, including Producer, Merchant, Processor, and User (“PMPU”); Swap Dealer; Managed Money; Reporting Traders[9] who do not fall in one of the previous categories; and non-Reporting Traders. Moreover, the COT breaks down positions by long and short, and in the case of Swap Dealers, Managed Money, and Reporting Traders, by spread positions as well (something I discuss further below).

Table 1 reports the shares for long positions for six major commodity futures contracts, and Table 2 does the same for short positions.[10] The main takeaway is that large entities dominate, with small traders holding 5 percent or less of the positions in all of the commodities except corn. Thus, the main function of the traditional futures markets is risk transfer among large commercial and financial entities. This is an important fact when considering the potential role and impact of commodity perpetual futures.

Table 1

Contract	PMPU	SwapDealers	ManagedMoney	OtherRpt	NonRpt
COFFEE C	21.84	12.78	29.90	7.03	5.35
CORN	25.11	16.48	14.83	8.53	8.19
COTTON NO. 2	23.28	21.09	13.43	13.40	4.99
NAT GAS ICE	49.44	8.49	13.77	3.48	0.60
NAT GAS NYMEX	13.40	10.33	12.06	3.39	3.57
WT PHYSICAL NYMEX	32.43	3.25	9.12	7.18	3.64

Table 2

Contract	PMPU	SwapDealers	ManagedMoney	OtherRpt	NonRpt
COFFEE C	41.49	20.52	4.70	6.23	3.97
CORN	41.79	1.09	15.14	4.60	10.50
COTTON NO. 2	24.84	4.45	35.12	6.66	5.14
NAT GAS ICE	24.59	48.51	0.77	1.62	0.29
NAT GAS NYMEX	15.31	2.37	13.21	9.25	2.62
WT PHYSICAL NYMEX	18.70	25.56	4.93	3.63	2.80

The Economic Role of Commodity Perpetual Futures

The structure of perpetual futures means that they are not well-suited to achieve the purposes of futures markets described above, at least for a very large proportion of market participants. Specifically, they are not useful to large commercial hedgers or large speculators (i.e., those supplying risk capital to the market). Instead, they are most suitable for small speculators. In servicing this niche, perpetual futures might undermine the price discovery function of established futures markets.

The most telling indication of perpetual futures’ trader clientele is contract size. The Hyperliquid WTI Crude Oil contract (linked to the CME Crude Oil Futures price) is for one barrel of crude oil, in contrast to the standard CME contract which is for 1,000 barrels. The CME offers smaller contracts, and these are explicitly focused on the retail segment of the market. [11]

Such small contract sizes are impractical for large hedgers, who often hold positions in the millions of barrels. For example, in 2025, the average PMPU long in Crude Oil Futures held a position of 10.3 million barrels, and the average PMPU short’s position was 5.5 million barrels.

These average positions dwarf the entire open interest and volume of the Hyperliquid contract. On the day of the writing of this, Hyperliquid’s Crude Oil 24 hour volume in its 20x leverage contract was a mere 1.03 million barrels, and open interest only 2.13 million barrels. Volumes and open interest for other Hyperliquid commodity contracts is similarly de minimis compared to commercial position sizes.

The very nature of perpetual futures also makes them ill-suited for commercial hedging. They are designed to track a proxy for commodity spot prices—the nearby futures price—but hedgers typically are not hedging spot exposures. Instead, they hedge specific production, inventory, transportation, or consumption commitments occurring at identifiable future dates. A producer expecting to market crude oil six months hence typically hedges using contracts maturing near the anticipated production date. A grain elevator holding inventories evaluates storage opportunities by comparing prices across delivery months.

This relates directly to an important feature of commodity futures markets that distinguishes them from financial futures: the fact that there is trading for numerous contract expiration dates, sometimes stretching years into the future. Although trading volume and open interest is largest for nearby contracts, trading volume and open interest for deferred months is material.

This is reflected in the fact that “calendar spread” trading is extremely active in commodity futures markets.[12] This is illustrated in Table 3, which shows the fraction of open interest accounted for by calendar spread trades in the five contracts analyzed above. These shares are very large. By design, perpetual futures cannot accommodate spread trades. Thus, they are of limited utility to the large numbers of swap dealers and managed money firms who trade large volumes of spreads.[13]

Table 3

Contract	SwapDealer	ManagedMoney
COFFEE C	517	834
CORN	1450	2026
COTTON NO. 2	1110	1516
NAT GAS ICE	27172	17902
NAT GAS NYMEX	4682	5929
WT PHYSICAL NYMEX	10284	5480

The auto-rolling feature of perpetual futures is also attractive to small retail traders. This feature reduces transactions costs: a trader can hold a position in the nearby without incurring the commissions and liquidity costs associated with making offsetting trades in an expiring and first deferred contract.

It bears noting that the auto-rolling feature of perpetual futures does impact the timing of cash flows as compared to a strategy of successively rolling conventional futures contracts. It is well known that the cumulative payoff to a collateralized position in the nearby contract that is rolled periodically equals the change in the spot price (measured by the nearby price) of a commodity from the date the strategy is initiated to the date it is terminated. A perpetual future generates the same payoff over an identical holding period. However, with a conventional rolling strategy, the cash flows at the time of the roll do not depend on the difference (spread) between the nearby and first-deferred contracts at the time of the roll, whereas the cash flow on a perpetual future does. As noted above, the perpetual price at the time of the roll is a function of both the nearby and first-deferred prices, and thus depends on the spread between them. If calendar spreads are large, that is, the market is in a large backwardation or contango, the perpetual price can change dramatically over the roll interval even if the underlying futures prices do not change.

This has important implications for margining and auto-liquidation. Auto-roll during a period of large backwardation or contango generates large perpetual price changes, which in turn generates large mark-to-market gains and losses, and large adjustments to margin balances. Since such changes can trigger equity shortfalls (especially in high-leverage perpetual futures), they can also trigger automatic liquidations even if the underlying futures prices change little. This is not the case for conventional futures.

In sum, perpetual futures are most attractive to retail traders, rather than the large commercial and financial firms that dominate conventional futures markets. This means that perpetual futures are competing for only a relatively small share of traditional exchanges’ customer base.

However, this could have implications for price discovery. The foregoing suggests that perpetual futures will fragment/segment order flow in much the same way as dark pools, payment for order flow, and other off-exchange trading mechanisms do in equity markets.

Since retail order flow is unlikely to be privately informed, such segmentation changes the composition of order flow in ways that can impede price discovery. Although at first blush it may seem that stripping out uninformed order flow should make futures prices more informative, the opposite is true. The subfield of finance known as “market microstructure” shows that informed traders trade less intensively when uninformed order flow declines. Therefore, such declines tend to reduce the informativeness of prices, i.e., to degrade price discovery.

The importance of this cannot be predicted with any certainty, especially given the recentness of the development of perpetual futures, but some factors can be identified. One factor is whether perpetual futures will primarily divert retail traders from traditional exchanges, or will instead attract new traders who would not otherwise trade on such exchanges. The larger the importance of diversion vs. attraction the bigger the adverse impact on price discovery. But if the main effect of perpetual futures is to attract new participants to the markets (due for instance to small contract sizes, or lower commission and account setup and maintenance costs), the effect on incumbent exchanges and price discovery on them will be modest. To the extent that diversion does occur, the relatively modest shares of trading accounted for by retail traders will limit the impact on price discovery.

Summary

Perpetual futures are the new hot thing in commodity markets. They have achieved prominence lately primarily due to a surge in oil perpetual futures trading during the Iran War. This attention exaggerates their importance, and their likely future place in the commodity derivatives landscape.

Why? Because their very design makes them unsuited for hedging of commodity price risk, which is the primary function of commodity derivatives markets. Specifically, by design perpetual futures track a spot price (or in commodities, a proxy for the spot price), but commercial hedgers are primarily exposed to price risks further out on the forward curve. Moreover, calendar spread trading is important in commodity derivatives markets, and spot-focused perpetual futures cannot support such trading. Moreover, existing perpetual futures contracts are far smaller than the contracts that are traded in large volumes on traditional exchanges.

Thus, perpetual futures are tailored to a specific clientele—small “retail” traders who account for a relatively small share of traditional exchange volumes. Although perpetual futures may divert some of this volume from traditional exchanges, they may also facilitate the entry of new traders who would not trade on traditional exchanges. The ultimate competitive effect, and the impact of perpetual futures on the performance of commodity derivatives markets, will depend on the relative importance of these two effects.

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[1] Will Canny and Al Boost, Iran war volatility is driving oil trading boom on Hyperliquid, says JP Morgan. CoinDesk (20 March 2026).

[2] I offer no analysis or opinions regarding the legal status of perpetual futures under the Commodity Exchange Act.

[3] “Perpetual Futures and the Illusion They Were Built to Escape.” John Lotian News, 22 June 2026.

[4] See https://hyperliquid.gitbook.io/hyperliquid-docs/trading/liquidations for a detailed description of the process. The exchange can profit or lose by trading out of the positions taken into the liquidation vault.

[5] Some perpetual futures exchanges do not first attempt to liquidate the position in the open market, but internalize all liquidations.

[6] Below I discuss how the nature of commodity perpetual futures can inherently lead to large price changes during “roll” periods even when underlying market prices are not volatile.

[7] I focus on Hyperliquid because its public disclosures of its methodologies and trading protocols are the most complete and detailed.

[8] This procedure mimics that of some commodity futures exchange traded funds.

[9] Each futures contract has a reporting level. Those holding positions in excess of the reporting level must report them.  Those whose positions fall below the level are not required to report.

[10] The numbers do not sum to 100 percent due to the exclusion of calendar spread positions. Due to their importance, I discuss calendar spread positions separately below.

[11] The CME also offers a “mini” contract for 500 barrels, and a “micro” contract for 100 barrels. The CME has recently announced it will launch a 10 barrel contract. See Financial Times, Oil market opens up as retail traders pour in (4 July 2026).

[12] A calendar spread trade involves simultaneous purchase and sale of futures contracts on the same underlying commodity but with different delivery dates. For example, in natural gas, a February-March calendar spread purchase involves buying a February contract and simultaneously selling a March contract.

[13] Commercial traders in the PMPU category also trade spreads in large volumes, and hold large spread positions, even though the COT reports do not report such positions.

FT Alphaville ran a long piece on oil prices, and the forward curve specifically. I guess it’s good that a mainstream outlet delves into such an arcane topic, but it would have been better if they’d done better than a Fractured Fairy Tale treatment.

The piece does a service by pointing out that yes Virginia, there is a forward curve, and as a result there are different prices of oil for different delivery dates (not to mention for different qualities). Furthermore, unlike in say the equity or FX markets, there is no real “spot market” (i.e., market for delivery on the spot/immediately) for oil–or for most commodities for that matter.

The article also rightly recognizes that “the relationship between spot and futures prices in oil, as for most commodities, is actually quite complex.” Where it goes wrong is explaining that relationship.

It starts out by explaining the cash-and-carry (no) arbitrage relationship, and claims that it doesn’t hold in oil markets. Which is correct. But it gets the reason wrong.

It says: “In practice, this is difficult because the institutional structure of oil markets impedes the process where the future price is forced to this equilibrium level through arbitrage.” Further, “[o]ne problem is that physical oil is difficult to short.” And participants in the futures market are diverse: “Another is that the participants in the physical and futures market differ. While they use futures to hedge prices, producers, refiners and consumers need the actual oil. Financial investors use the futures market to simply trade price movements.”

In fact, none of these things explain the departure of the oil forward curve from the cash-and-carry (no) arbitrage model, and in particular the existence of backwardation, which is a facial violation of the model.

My teaching mantra is that futures markets price bottlenecks/constraints in transformation processes. And since the forward curve (and thus calendar spreads) provides intertemporal relative prices, the bottleneck in the forward curve relates to transformations in time. And it is a fundamental one: time travel is impossible.

Backwardation signals that the commodity is expected to be more abundant in the future than it is today. If it were possible, we would like to use a time machine to move the commodity from the future to the present. It’s not possible, however. The best we can do is to NOT move the commodity from the present to the future, which would be like carrying coals to Newcastle. That is, backwardation signals that we should consume the commodity today, not add to storage thereby moving the commodity to the future, and in fact draw down on inventories.

Indeed, it is never optimal for inventories of the deliverable underlying a futures contract to always be positive. Instead, sometimes a “stockout” should occur. That’s essentially what we are observing now in WTI, where Cushing stocks are at “tank bottoms.” (If a stockout never occurred, then there would be stuff that is produced yet never consumed. That is suboptimal).

Formal models describe the mechanism. My book is all about such models.

The book (and books and articles by others, including Wright and Williams, and Scheinkman and Schectman) derives forward curves from dynamic programming models that optimize storage and consumption decisions. And in my models. in particular, forward prices are derived from no-arbitrage principles. Namely, to prevent arbitrage, forward prices are expected future spot prices under an equivalent probability measure. So it’s incorrect to say “actual futures prices for commodities don’t follow arbitrage models.”

They don’t follow cash-and-carry arbitrage models, but such models are applicable only to pure assets–things that are always in positive supply like stocks or bonds–and with zero transactions costs. (Positive transactions costs in stocks and bonds can result in deviations from full carry too). And that’s the key point. Commodities are not pure assets. They are consumables, and as noted above, sometimes it is optimal for the commodity underlying a futures contract (e.g., WTI stored in Cushing, Oklahoma) to be in zero supply. The possibility for a stockout breaks the transactions underlying the cash-and-carry model, which implicitly assumes that inventories are always positive.

So all of the stuff Alphaville says differentiate physical from paper (futures) markets are utterly irrelevant in explaining the forward curve, and deviations from full carry. Instead, it’s all about the fact that commodities are storable consumables, not assets analogous to a stock or a bond.

Alphaville really gets into fairy tale territory when it commits the common error of confusing backwardation as the term is used by traders and market participants, and Keynesian backwardation. Backwardation as the term is used in markets means that a nearby price at time T is above a futures price at time T for delivery after T. For example, when the May 2026 price was above the December 2026 price during April.

Keynesian backwardation is when a futures price is above the expected future spot price at the expiration of the futures contract. This is totally different. An expectation is not a traded price. A futures price is a traded price. So Keynesian backwardation compares a tradable price to a non-tradable one. A backwardation backwardation compares a tradeable price to another tradable price. Moreover, whereas backwardation as used in the markets compares, say, a spot price (or nearby futures price) to a futures price with later expiration, Keynesian backwardation compares prices related to the same point in time–the futures expiration date.

Long story short, a market can be in an actual backwardation and not in a Keynesian backwardation, or in a Keynesian backwardation and not an actual backwardation, or different amounts of backwardations. The concepts are not at all related.

Perhaps the best illustration is that stock index futures can be at full carry (adjusted for dividends), and simultaneously in a Keynesian backwardation. In fact, that’s what you’d expect. Futures prices drift up in a Keynesian backwardation, rewarding the long for taking on risk. If the expected rate of return on the index exceeds the risk free rate (as is definitely true), the index will be in a Keynesian backwardation.

Alphaville does get the gist of Keynes’ argument regarding “normal backwardation” correct:

Keynes’ premise was that the participants in the supply chain have asymmetric risk aversion. Refiners, distributors and some consumers might not hedge, as they can pass on price fluctuations. In contrast, producers incur high upfront capital expenditures and bear greater financial risk from lower prices, encouraging hedging. This gives futures prices a downward bias, with producers willing to sell at a discount to the theoretical futures prices.

That is, there is a short hedging imbalance that puts downward pressure on futures prices (relative to expected future spot) to encourage speculators to go long and absorb the imbalance.

The problem with the “downward bias” conclusion that says that hedging imbalance determines the bias in the futures price is that it is based on an archaic view of asset pricing, and arguably an archaic futures market structure. Keynes implicitly assumed futures markets were not integrated with broader financial markets, and also wrote before modern asset pricing theory showed that the risk premium on a given claim depends on its contribution to the risk of some portfolio (e.g., the market portfolio in CAPM), or its correlation with aggregate consumption, or something of the like. In Keynes’ world, speculators in market X were undiversified, and hence their risk exposure and the compensation they demanded for bearing risk was related to the volatility of the futures price. With much demonized “financialization,” however, the marginal market participant is almost certainly well diversified (e.g., a hedge fund) and hence the risk premium he requires to take a position in market X depends not on price volatility but its covariance with his portfolio, or aggregate consumption, or whatever your asset pricing model of choice tells you.

But again, Keynesian backwardation is about risk premiums, and that’s a different issue from what causes an observable forward curve to deviate from the predications of the cash-and-carry arbitrage model.

Alphaville compounds the confusion by mixing up the concept of convenience yields with Keynesian backwardation: “reflecting the fact that producers are prepared to pay a positive insurance premium (the convenience yield) to protect against unforeseen adverse price movements.” Relatedly, it says: “The level of backwardation can be exacerbated by shortages of stock available for immediate delivery from an adverse supply shock — as in the oil market as during the Iran war — or a positive demand shock. This reflects the ‘possession’ value, as refiners and consumers hoard available stocks as a security measure.” This is basically a convenience yield story: the security provides a flow of implicit benefits.

“Convenience yield” is another of my pet peeves. It was a kludge devised by Kaldor to explain deviations from full carry. He reasoned the future price of stocks is less than the future value of the spot price by the amount of dividends. That is, a cash flow on the underlying asset depresses the futures price. Commodities don’t pay a cash flow, but Kaldor argued perhaps they pay some implicit benefit analogous to a dividend that similarly reduces the futures price relative to the spot price and the future value of the spot price (i.e., the spot price plus financing costs).

Very hand-wavy. And there has never been a coherent model that predicts the existence of such an implicit benefit from the optimization decisions of economic agents in equilibrium. In my book, I show that convenience yield only arises when it is essentially assumed (as a kludge) or in models with magical properties (e.g., inventory lowers production costs even if it is never consumed).

So convenience yield is bad enough, but to mix it together with something unrelated to actual backwardation is really ridiculous.

The article does identify some factors that are associated with backwardation. These are factors that feed into structural, dynamic programming models like those in my book. So Alphaville has identified the correlations, but is clueless on the causal mechanism.

What it comes down to is that the extreme backwardation observed during the hot phase of the Iran War is simply explained using the dynamic programming logic. There was a huge adverse short term supply shock. The supply shock was not expected to last forever. That made oil scarce in the short term relative to what was expected in the longer term, when the supply shock dissipated with the end of the war. Under these circumstances, it was optimal to draw down on stocks. The forward curve moved into an extreme backwardation to incentivize this.

This also relates to another issue that has been discussed lately: why is the 2026 oil shock different than the 1979 oil shock in terms of its economic impact? Because the shocks are different. 1979 represented a structural change in the oil market that was expected to last indefinitely. The 2026 shock was expected to be transitory, and indeed that is apparently the case. Old hotness: shortage of oil. New hotness: impending glut. Persistent shocks have very different effects than temporary ones, again as demonstrated in the book.

One last thing. Alphaville starts the piece with a story about Keynes allegedly having to take delivery on wheat futures, and measuring the King’s College Chapel only to learn that it was not large enough to hold the wheat. Keynes told this story, and it is clearly a joke. Delivery on the futures contracts was in store. That is, Keynes received warehouse receipts giving him ownership of wheat in a warehouse. He could have kept it there, as long as he paid storage. No sacrilege required.

But what is revealing is that Saint John Maynard was a welsher: “Keynes cleverly avoided the problem by objecting to taking delivery on quality grounds.” The problem wasn’t finding a place to store the wheat delivered to him. Keynes just didn’t want to take on the obligations (namely paying storage) that delivery entailed. Not clever: commercially disreputable.

And that wheat was ever delivered to Keynes was either the result of his choice or negligence. He could have avoided this problem by selling his futures prior to the delivery period. Did he choose not to do so, then regret the consequences? Or did he have a brain cramp and forget? Either way, he should have manned up, rather than weaseled.

Time to scrape the rust off the ol’ blog! Been a while. Life comes at you fast sometimes.

The Rustoleum (a product originally from my home town, whose plant I watch burn in a spectacular fire while waiting to play baseball in James Park) is a post about the new Big Thing, which is actually something I wrote about last September–compute futures.

The CME, ICE, and tokenized exchange Architect Financial Technologies have announced the launch of futures contracts on compute. This land rush is a direct consequence of the AI boom. (I will write a post in the near future on AI issues more broadly).

The main attraction. The compute market is huge, and growing. As CME CEO Terry Duffy said in a statement touting the launch:

“As the backbone of the digital economy, compute is the new oil of the 21st century,” CME CEO Terry Duffy said in the statement. “Every AI model trained, every transaction cleared and every byte of data processed runs on compute, which is becoming a fast-emerging asset class in its own right.”

As I noted in my 2025 post, market size is a necessary but not sufficient condition for a successful futures market. Another important condition is volatility.

Most of the recent angst about compute cost is its direction, not its volatility: Compute costs have been increasing steadily as demand from AI has outstripped increases in chip supply, data centers, etc. That is, the market is largely directional:

A stead trend down through late-2025, then a steady upward trend through the present (with a weird blip in March, 2026).

Long-term trend dominated markets are not conducive to successful futures–you need substantial variation around the trend. Not much volatility evident there.

Ironically, Don Wilson of DRW–a big proponent of these markets (one of his firms creates the index graphed above)–pooh-poohed the relevance of the failure of DRAM futures as a precedent to the prospects for compute futures precisely because DRAM prices were dominated by a (downward) trend. The same logic would apply to compute futures.

As I also noted last year, another important contributor to futures market success is a fragmented, highly intermediated value chain. I further noted that the AI/data center/compute value chain appears to be anything but. The biggest players in this market are therefore not natural participants in this market.

You might argue that AI users could be, especially since AI cost has become a big issue of late. (More on this below). But in many products, big, low-leverage, public firms have little incentive to hedge price risk. Indeed, it’s long been something of a mystery why corporates hedge. Long ago Clifford Smith and Rene Stulz identified motives, including taxes, leverage, and agency costs. But I don’t really see those as driving widespread corporate demand to hedge compute costs.

This is not to say that compute costs are not an issue. Indeed, several stories in the last few days make it clear that initial AI euphoria is running up against economic reality. Namely the reality that AI ain’t cheap.

Marc Andreessen admitted on Joe Rogan that AI is making people less efficient.

The guy who funds half the AI industry. On a podcast. Just casually dropping it.

Same week: Nvidia's VP said compute now costs more than his employees.

Microsoft canceled 100,000 Claude Code…

— Alex Prompter (@alex_prompter) May 24, 2026

And:

NEW: AI consultant reveals a client accidentally spent $500,000,000.00 in a single month after failing to set employee limits on Claude usage.

— Polymarket (@Polymarket) May 28, 2026

And this too.

He said that Uber CTO Praveen Neppalli Naga went viral after telling The Information in an April interview that Uber had already blown through its Claude Code budget for 2026.

The comment led to what he described as a “head-exploding moment,” sparking discussions about AI token consumption within the company and the trade-offs it creates, such as on head count.

What makes my head explode when reading these stories is that these supposed business geniuses didn’t realize that treating something as free would result in massive overconsumption. I mean seriously: they didn’t think this was going to happen with probability one?

Maybe they should have considered the fate of Red Lobster.

A couple of implications of this. The first is that it is likely that Anthropic et al have way underestimated the elasticity of demand when the market settles down into an equilibrium in which AI end users exercise more judgment and control over their use.

The second is that companies will definitely have to figure out organizational and management strategies to incorporate AI into their businesses. The proper use of AI will require the exercise of judgment, and the development of mechanisms that incentivize the balancing of benefits and costs. Since (a la Coase, 1937) whereas markets allocate resources by price, within firms price mechanisms are typically uneconomic so they rely on fiat instead, these mechanisms will focus on budgets and managerial decisions on when and how to employ AI.

These implications have implications of their own.

First, since AI is costly–not least because of compute cost–normal market mechanisms will prevent or at least strongly mitigate the doomsday scenarios of mass unemployment. Many of the doomsday scenarios are based on bad economics, specifically, focusing on absolute rather than comparative advantage. “AI can do everything better!” But when a resource is scarce, that doesn’t determine market outcomes. Market prices, for compute, and for labor and capital, will reflect comparative advantages. AI will hurt those occupations in which it has a comparative advantage. But you can’t have a comparative advantage in everything.

Second, we are in the early adoption stage of AI. The shock companies are experiencing when looking at their compute costs will lead them to rethink and refine their usage (as discussed above). This will impact the demand for AI–both in the level and the elasticity. That will be a learning process, leading to demand fluctuations. AI suppliers will take some time to learn how demand is evolving, and their pricing policies will change accordingly. Which will feed back to firms, and also to upstream suppliers (GPU fabricators) will also have to adjust their pricing.

In the near term this will lead to price volatility. A sort of reflexive, endogenous volatility. But I would expect that to settle down in relatively short order, in which case pricing will be dominated by upstream costs, notably chips and power. Especially since there will likely be pretty intense competition between AI firms.

Meaning that this adoption learning process volatility is likely to be transient, and insufficient to support a big futures contract in the longer run. And the trending aspect implied by my posited post-settling down equilibrium will not generate the short term volatility needed to support a futures contract–even assuming the other issues (the value chain, weak corporate hedging demand) do turn out differently than I expected.

In other words, despite the hype, I’m skeptical about the long term viability of compute futures.

A week ago Friday (10/10/25) there was a crypto crash. Not huge, but not small. Bitcoin:

Ether was also hit.

The proximate cause was the most recent Trump tariff spasm. But that was just the spark. The initial down move triggered classical self-reinforcing crash dynamics. And the crypto market structure is arguably uniquely vulnerable to such events.

As I’ve written for years and years, (a) tightly coupled systems are vulnerable to catastrophic collapse, and (b) a wide variety of factors produce tight coupling in these markets. The most important of these factors is marking to market and variation margin.

And this dynamic is particular fraught in crypto. Positions are marked to market at a very high frequency. Moreover, the resulting margin calls occur at the same frequency. That frequency is so high that margin breaches result in liquidations of positions (“auto liquidations”) because there is no time to replenish collateral. These liquidations exacerbate price movements, creating a positive feedback mechanism–and positive feedback is almost always a bad thing in financial markets.

But it gets better! Risky positions are sometimes collateralized with . . . risky collateral! Specifically so-called “stablecoins.” I say “so-called” because stability is aspirational rather than real, especially for a new variety of “stablecoins” implicated in the 10/10 event, like USDe.

Whereas traditional stablecoins like Tether are supposedly backed by high quality, low-risk dollar assets (and the recent “Genius Act” will reduce any uncertainty about that), USDe is “backed” by both dollar assets and–wait for it–crypto. So what is the lithium that keeps USDe “stable”? Hedging using perpetual futures.

No. Seriously. Yeah, because hedges are always perfect, right? Here’s the only perfect hedge I’m aware of:

So in reality the “stablecoin” has basis risk, the basis being the difference between the price of the crypto collateral and the perp futures.

Arbitrage is supposed to keep the basis small with little volatility. However, if you look at data, e.g., the spread between BTC and BTC perp futures on Coinbase, you will see that there are frequent and large basis movements, often as big as 1 percent.

Moreover, USDe is traded on multiple exchanges, and the prices can diverge. Again, arbitrage is supposed to keep those divergences small and of short duration, but the market moves faster than arbitrage: given the frequent revaluing of positions and collateral, even a very brief price divergence can trigger a self-reinforcing spiral before arbitrage can close the gap.

On 10/10, Coinbase used the price of USDe on its platform to market collateral to market. But that exchange is not the primary USDe platform, and is therefore relatively illiquid. So forced liquidations of positions on Coinbase due to the price move and marking to market led to liquidations of USDe collateral which pushed downward pressure on the Coinbase USDe price which led to more positions being undercollateralized which led to those positions being liquidated. And this all happened faster than the speed of arb. Rinse, wash, repeat.

Can you say wrong-way risk? I knew you could.

The industry cope is to say “well ackshually if Coinbase had used the USDe price on more liquid exchanges as its USDe ‘oracle’ this wouldn’t have happened.”

Maybe. But irrelevant. What happened on 10/10 was a “normal accident.” Normal accidents result from a sequence of events in a tightly coupled. complex system. Accidents are “normal” in such systems because there are many, many, many possible sequences that can trigger accidents. Meaning that even if this particular sequence is eliminated by referencing prices from the most liquid exchange, many, many, many other such sequences will remain.

The underlying “cause” of such events is the very nature of the system itself. The reason crypto is particularly susceptible is the tight coupling due to frequent marking to market and high leverage. Those factors remain.

The crypto system is acutely vulnerable due to another structural factor–the fragmentation of trading. As I’ve frequently written, crypto market structure is puzzling. The centripetal force of liquidity typically results in the concentration of trading on a single platform. Crypto, in contrast, is traded on multiple exchanges, with several have a fairly large market share.

This fragmentation increases the number of connections and therefore the number of possible paths by which a destabilizing shock can spread. And referencing prices from other exchanges to value positions or collateral on a given exchange creates more such interconnections.

In brief, a shock–a price shock, or an operational shock–on one exchange can impact other exchanges. These shocks can trigger the destabilizing auto liquidations.

“Too big to fail” is not the right description of the crypto market. Instead, “too interconnected and tightly coupled and complex to succeed” is more accurate.

It must also be noted that the factors that make the crypto market systemically fragile make it unduly susceptible to manipulation which can in turn trigger systemic crises.

One of the oldest strategies in the book is “gunning the stops.” The play being that if you force prices to hit stops order triggers, that will induce trades that move prices further in the same direction.

In crypto, the game is “gunning the liquidations.” The idea is the same. Trade like a whale to push the price in a particular direction. If the move is big enough, that will trigger liquidations that move the price further in that direction. And since some collateral is risky, there is another way to manipulate–bash the collateral.

Given the high leverage and high frequency marking to market, the necessary push isn’t that big–maybe a dolphin can do it. And as shown on 10/10, and as discussed above, once the price moves begin they can trigger a crash.

It is also disturbing that crypto exchanges can profit from liquidations. Conceivably this could provide an incentive for the exchanges to manipulate themselves. But even if that’s not the case, the fact that they can profit from destabilizing manipulations, and instability generally, means that they have a weaker incentive, and perhaps a positive disincentive, to prevent/deter manipulations or big price moves.

In sum, crypto market structure is crapto. It has all of the inherent vulnerabilities of markets, but on meth. Supposed “innovations”–like near continuous marking to market and unstablecoin collateral–exacerbate these vulnerabilities. As does the fragmentation.

All meaning that it is inevitable that you will see this movie again.

In my recent post on Don Wilson and GPU trading, I expressed skepticism at the prospects for such trading based primarily on market structure issues. Specifically, a commodity futures market can thrive if the underlying product has a highly intermediated value chain, but that vertical integration or long-term contractual relationships between producers and ultimate buyers is inimical to their success. Further, I surmised that transactions costs considerations would lead to long-term contracts and/or vertical integration dominating the GPU sector.

And indeed, that outcome is becoming clearer by the day. OpenAI (it of ChatGPT) has agreed to a massive chip purchase from AMD and has also bought a 10 percent stake in the chipmaker. OpenAI has also entered into a five-year deal with Oracle to buy “computing power.”

There is also increasing verticality in power supply arrangements. (Apparently the new industry buzzword is “circularity” but we are really talking about vertical relationships here, so I’ll stick with traditional IO speak). Example: AI firms building gas-fired plants in Texas. Musk’s xAI building a gigawatt scale plant in Mississippi to power a data center outside Memphis.

Microsoft has entered a 20 year deal with Constellation Energy to restart–believe it or not–a unit at Three Mile Island. And on and on.

It’s somewhat amusing to me. Years ago Paul Joskow wrote a seminal article about coal mine mouth power plants in which he showed that this co-location led to long term contracts or vertical integration. We are now seeing data center mouth power plants.

The economics are basically the same. It makes sense to locate something that consumes as much (or more) power than a generator can produce to economize on transmission costs and enhance reliability. However, this creates bilateral monopoly driven transactions costs issues that lead to vertical integration and long term contracting.

Going back to GPUs, there is no reason for colocation, but economies of scale on both sides of the market–huge consumers operating data centers, and a small number of GPU producers–create similar bilateral monopoly opportunism risks, which in turn are leading to the implementation of traditional responses to these risks–tight vertical connections between producers and consumers.

This market structure is inimical to the development of a thriving derivatives market. So I wouldn’t be surprised to see the launching of GPU futures contracts, but I fully expect them to sink faster than a Nork destroyer after launch. But unlike the Nork ship, GPU futures would not be refloated.

Odd Lots was in Chicago last week, and after interviewing Don Wilson they interviewed CME CEO Terry Duffy. The interview was posted today.

CME's Terry Duffy touts its partnership with FanDuel. What CME will offer is basically what bucket shops offered back in the day. The irony is that back then bucket shops were the betes noire of exchanges. https://t.co/Z34P89KDJZ

— streetwiseprof (@streetwiseprof) October 2, 2025

I’ll get to the FanDuel tie-up in a bit, but first I want to discuss something else that came up–perpetual futures, which are a hot topic now (even though hot topic is not punk rock). Duffy cast doubt on the feasibility of perpetual futures in most CME products–he mentioned ags specifically. I agree with his conclusion but not his reasoning.

Duffy argues that perpfuts can’t work for commodities because they don’t fit the legal definition of a futures contract, which involves pricing something for delivery at a later date. He also argues that it can’t work for deliverables.

Maybe perfuts check the legal box, maybe they don’t. Twirling my Oliver Wendell Holmes-esque mustache, I could come up with a clever legal argument that based on the way perpfuts work, they are pricing a commodity at the time of the next marking-to-market, say an hour from now. Or a minute from now. Which would be in the future, either way.

And that marking-to-market is the real reason commodity perpfuts won’t work. Take the case of Bitcoin. Bitcoin perpetuals involve periodic (usually hourly) “funding payments” based on the difference between the futures price and the reference spot price taken from an exchange like Coinbase or Bitstamp. Basically cash settlement on an hourly frequency. But the contract remains live after each settlement.

So basically, a perpfut it is a perpetually (automatically) rolling cash-settled one hour futures contract. The advantages of the future over trading the spot are (a) embedded leverage, often 100-to-1, and (b) the ability to short without borrowing the underlying. Perpfuts are like trading spot, but with leverage.

This works for things for which there is a liquid reference spot market. Bitcoin and some other cryptos have that.

You could do this with say the S&P 500 because there is a large, liquid, continuously traded, transparent cash/spot market. You could do it with currencies, and via its FX Spot+ (“Rolling spot”) contract, the CME (via its EBS subsidiary) is doing just that.

You can’t do it with most commodities because there are no liquid, continuously traded, transparent cash/spot markets. Not in grains. Not in oil or other energy products (with the possible exceptions of natural gas and power). Not likely in precious metals–there is RTC trading in gold, but it is bilateral and there is no transparent pricing. Maybe you could do it with LME non-ferrous metals, because of their unique “cash” pricing mechanism.

So it’s not legal obstacles or deliverability that preclude perpetual futures in most (if not all) physical commodities. It’s the lack of a reliable spot pricing mechanism that can tie the futures and cash/spot prices together as perps are intended to do.

This is basically a special case of why I have ridiculed proposals for cash settlement in commodities for over 30 years. It’s only that the problem is even more pronounced for perpfuts because you need to observe spot prices with metronomic regularity every day, not just around contract expirations.

Turning to CME and FanDuel, my X post basically says it all. It is hilarious to me that CME is offering “event contracts” that are indistinguishable from the kinds of bets one could make on bucket shops back in the bad old days. Duffy describes contracts like “will the price of gold be above X an hour from now?” (NB: if that’s a futures contract, as Duffy claims it is, then perpfuts are no different: see above). The contracts would be “digital” (i.e, have a fixed positive payoff if the event happens, zero payoff if it doesn’t).

Well, that’s exactly the kind of thing bucket shops traded back in the day. (0dte options, which are also a thing now on many exchanges, are also very similar to old-school bucket shot bets). Exactly like them.

That’s an 1892 painting of a bucketshop. Note the use of the high tech chalkboard app.

So why hilarious? Because exchanges waged war on bucket shops for decades in the late-19th and early-20th centuries. A journal article provides some color:

On the morning of August 29, 1887, Abner Wright, president of the Chicago Board of Trade, forcibly removed the instruments of the Postal Telegraph and the Baltimore and Ohio Telegraph companies from the floor of the exchange, literally throwing their equipment out of the building. A few months later, on the night of December 15, Wright discovered some mysterious electrical cables leading out of the basement of the exchange building. Thinking that they were telegraph lines, he ordered them cut with an axe. Instead, they were cables connecting the building to the police and fire departments.¹ His desire to sever the Board of Trade’s telegraph connections might seem surprising, since the telegraph network was indispensable to the operations of the major stock and commodity exchanges.

That’s why the CME’s current move is hilarious. If you can’t beat ’em, join ’em.

I’m not censorious of bucket shops per se. People gonna gamble. The CBOT’s (and NYSE’s and other exchanges’) ire reflected not a moral judgment, but a dislike of competition. (Yes you can make arguments like Mulherin, Netter and Overdahl that the shops free rode on the price discovery of exchanges, and that impaired liquidity. But as I’ve shown in my academic work, this “free riding” can actually enhance efficiency if exchanges have market power, which they almost certainly do).

Exchanges today almost certainly realize that they can’t drive bucket shops, excuse me, betting apps, out of business: Terry Duffy can’t forcibly remove their equipment, a la his distant predecessor Abner Wright. So the best alternative is to get in the game with them.

Perfectly reasonable commercial strategy, but a deeply ironic one.

SEC Chairman Paul Atkins has clearly set out to be the anti-Gary Gensler. If that’s not an endorsement, I don’t know what is!

Case in point–his move to abandon to the mercies of the courts Gensler’s cretinous, pointless, and incredibly burdensome climate reporting rule. (I use that Sierra Club link to provide some levity. I love the smell of hysterical panic in the morning).

Atkins is also largely reversing GiGi’s anti-crypto agenda. (Well, he is a “crypto peddler” after all, at least according to the Sierra Club!) I am not a crypto evangelist by any means, but broadly support letting consenting adults trade what they want to trade. The regulatory framework for such trading should focus on legitimate investor protection, anti-fraud, and anti-manipulation measures as set out in the SEC’s authorizing statutes. That’s what Atkins’ SEC is striving to do, in contrast to Gensler’s mission to strangle crypto at every opportunity.

In crypto, but more generally, Atkins is also moving sharply away from Gensler’s regulation by enforcement approach, which is another welcome change.

And hot off the presses, Atkins has taken aim at the bloated and (IMO) pointless Consolidated Audit Trail. (Seriously–I found the above announcement while Googling “Atkins CAT” while writing this post. It came out hours before I put pixels to screen). Proposed in the aftermath of the 2010 Flash Crash, it always struck me as a hugely expensive solution to a non-existent problem. Yes, the CAT could assist in forensic evaluations of market events like the Flash Crash and market manipulation, but having worked on such evaluations in markets lacking the CAT, and reading the analyses of others, I conclude it is possible to carry out these analyses without CAT. Put differently, the marginal benefit of the CAT is minuscule relative to the cost.

One issue that has received less attention outside the equity trading community is market structure, and particularly RegNMS. The 20th anniversary of that regulation was the occasion for an SEC conference to reevaluate that regulation. (At the time of the passage of RegNMS, I wrote an article saying that it marked the 30 Years War over market structure that began with the 1975 Securities Act, and wryly suggested that there would actually be a 100 Years War over market structure. We are now at the halfway mark!)

Along with several participants, Atkins criticized a key provision of RegNMS–the so-called “trade through rule” that prevents the execution of a trade on one exchange when a better price is displayed on another. Atkins favors elimination of the rule.

This rule is the keystone of RegNMS, and its removal would reshape the equity exchange environment as thoroughly as the regulation did 20 years ago. Indeed, its elimination would signal that the entire experiment was ill-conceived.

As I wrote in my Regulation Magazine piece, the trade through rule was the “centerpiece” of RegNMS. Its very purpose was to cause a fundamental restructuring of equity trading in the US. And it did.

As I phrased it, the purpose of the rule was to “socialize order flow.” That is, eliminate–or at least sharply circumscribe–the control that an exchange had over the order flow directed to it. The objective of this was to make the exchange landscape more competitive, and specifically to reduce the market power of the NYSE and NASDAQ. At the time of RegNMS’s passage, the NYSE executed 80 percent of the volume of shares listed on it.

Evaluated against its stated objective, RegNMS was a . . .

The equity trading landscape looks nothing like it did in 2005 or before. More than half of trades are executed off exchange. The remainder is divided among 12 exchanges, with the largest having a 10 percent market share:

But to Atkins and the critics of the trade through rule, this represents not great success, but great failure. To the critics, this fragmentation is deleterious.

That is, one man’s competition is another man’s fragmentation.

The post-RegNMS environment has clearly seen dramatic improvements in liquidity and declines in trading costs. This is plausibly, though not definitively, materially the result of increased competition. Moreover, the exchanges have adopted different order types, pricing rules (e.g., taker-maker/inverted v. maker-taker), and trading infrastructures. This means that heterogeneous market participants can choose among heterogeneous platforms, thereby achieving a better match than a one-size-fits-all system would.

The main disadvantage of fragmentation–and a major source of discontent–is that brokers and traders must connect to a variety of platforms, and crucially, must buy data from each. As I wrote some years ago, this creates strong complementarity among the data streams that exchanges provide, which as Cournot demonstrated two centuries ago creates considerable market power which inflates prices. Data is expensive, and economics implies it is too expensive.

What would happen if the trade through rule is abolished? I predict that by effectively re-privatizing order flow it would lead to a massive consolidation of trading venues, and a return to a market structure resembling that of 2004 and before. Less fragmentation, but less competition. Those are essentially two sides of the same coin.

The busy Mr. Atkins has also weighed in on the issue of the frequency of corporate reporting, favoring (fast-tracking, actually) the elimination of the quarterly reporting requirement. Since Trump proposed the same, of course this has led to shrieks and wails–which have been markedly absent when Warren Buffet mooted the idea.

Here’s another issue where I support letting the market decide, as is typically the case when there are trade-offs. Reporting frequency has costs and benefits. A company that provides too little information largely internalizes the cost, in the form of a lower stock price. That is to say, its shareholders internalize the cost. Yes, agency problems mean that managers who make disclosure decisions may act against the interest of shareholders and disclose too little or too infrequently, but that is a problem that is inherent in the corporate form (and the consequent “separation of ownership and control”) that manifests itself in many ways that are not the subject of government mandates and intricate regulations. It’s hard to see why corporate disclosure should be different. Yes, punish false disclosures, but don’t dictate how often companies must avoid lying.

One concern in recent years has been the large number of companies deciding to go private or remain private (rather than IPO’ing). There are myriad reasons companies make these choices, but one of them is the cost of being subject to the reporting obligations imposed on public companies. Easing those obligations somewhat would nudge more companies toward going/remaining public, meaning that the amount of information available to investors and the opportunities available to them might increase if the Trump/Atkins proposal goes into effect.

Overall, Atkins is a welcome relief. Though anyone probably would have been, after Gensler’s Reign of Error (and Terror). I’m ambivalent about his hostility to the trade through rule, but on non-market structure matters he is moving in the right–and pro-market–direction.

Today’s Bloomberg Odd Lots has an interesting conversation with Don Wilson, founder of DRW whom Odd Lots calls the “King of Chicago Trading.”

Overall the ~40 minute conversation was very interesting. One of the most interesting parts related to the possibility of GPU futures trading. Wilson thinks it could become the world’s biggest market, and is putting his money where his mouth is. This includes the creation of GPU price indices.

I’m pretty skeptical on the prospects for this market. I note at the outset that it is very difficult to predict which futures contracts will succeed, and which will not. Most don’t.

In a nutshell, the necessary conditions for a successful futures contract are pretty well established. The underlying market must be large. The price of the underlying must be volatile. The underlying can be heterogeneous, but not too heterogeneous, otherwise basis risk is too big with any single contract, and no single contract can generate enough volume to be viable.

GPU would certainly tick the market size box. Perhaps the volatility box. The heterogeneity box–not so sure, although trading a contract that prices based on a service flow (e.g., flops) rather than chips themselves would probably tick that box as well.

Although the necessary conditions for a successful contract are pretty well established, sufficient conditions are not. One can launch a futures contract on something volatile with a large underlying and price volatility and watch it flop. That happens quite frequently.

What’s missing from most analyses, in my view, are more qualitative things. The most notable of these is market structure. In particular long marketing chains, with a lot of intermediation. Also, relatively low concentration on either the producer or the consumer side.

In commodities, for example, trading firms generate a lot of the hedging activity. They intermediate the flow of the physical, and typically their exposures turn over relatively rapidly, meaning that they are putting on and taking off positions frequently. This generates trading volume and liquidity.

The fragmentation of the value chain and fundamental volatility militate against long term contracts for most commodities. Moreover, the lack of concentration mitigates market power which is often associated with price rigidity (as Dennis Carlton showed many moons ago).

My understanding of the GPU market is that the marketing chains are short, not intermediated, and highly concentrated. For example, there are a small number of very large cloud storage providers. AI is likely to be highly concentrated on the buy side. The GPU production side is obviously very concentrated, being dominated by NVIDIA.

Moreover, transactions cost considerations are likely to lead to a good deal of vertical integration. AI firms are going to buy their own chips. Vertical integration also militates against the development of a liquid futures market.

Given the concentration on both sides of the market, I anticipate that to the extent buyers and sellers want to achieve some price stability, it will be via bilateral contracts between the producers and the big users.

Joe Weisenthal asked the question that came to my mind–what about DRAM futures, which were highly touted at the height of the dotcom boom, and crashed along with NASDAQ in 2000?

I have some personal history here. One of the (many) nascent DRAM futures markets (Buckaroo.com, if you are wondering) flew me out to Mountain View to give my opinion on the prospects for the market. Ironically, I was there the day NASDAQ crashed. (Hmmm, I also had some connection with the ’87 Crash–maybe it’s me!)

My assessment was negative. Not long after, the late CEO of Micron, Steve Appleton had me come out to Boise to give my view. Buckaroo had been importuning him, and when they told him they had talked to me, he invited me to tell him what I had told them. After hearing my take, he told Buckaroo “hard pass.”

My prediction, vindicated by experience, was based on market structure considerations. My skepticism about GPU futures is similarly grounded.

For his part, Wilson dismissed DRAM as a precedent. In his view, the fact that DRAM prices trend down strongly meant that there was no underlying volatility to hedge or trade around.

I don’t find that compelling. Yes, everybody knew that prices for a particular chip type tended to trend down (due mainly to learning-by-doing). But as long as there is variation around that trend, there is a price risk that sellers and/or buyers might want to hedge.

DRAM might not be the only cautionary tale. I’m so old I remember Enron touting bandwidth as the future world’s biggest commodity. Yeah. No.

Another issue is information flow. A high rate of information flow causes high rates of price changes which drive both hedging and speculation. Weather derivatives tick a lot of the necessary condition boxes, but information flow is very lumpy. Consider a weather derivative on July 2026 cooling degree days in Houston. From now until, oh late-June–maybe–there will be relatively little information that flows. So there’s nothing really to trade on until then. Meaning that volumes–and liquidity–will be moribund.

Which is why exchange-traded weather derivatives have never done any real volume.

My intuition is that the information flow about future DRAM value will be pretty modest.

So maybe GPUs will be unlike DRAM and bandwidth and weather, maybe it won’t. I will watch with interest.

Another interesting part of the Odd Lots conversation related to the CME’s move to the cloud. Wilson made the great point that the way the cloud works is not compatible with housing the matching engine there. Especially in a high speed world, it is nigh on impossible to implement time priority on the cloud.

Wilson, Alloway, and Weisenthal also discussed tokenization. Here I agree that this could become a thing, and a big one. It may finally be the moment for DLT/blockchain technology to have a big impact.

It was widely hyped for years as transformative, for everything–including financial markets and commodity markets. I had a 2022 JACF paper expressing skepticism about the potential of DLT/blockchain. I still am skeptical about a lot of the proposed uses (e.g., tokenizing shares of fine art or real estate, or in physical commodity trading). However, tokenizing standard securities, derivatives, or collateral offers some advantages. Like I wrote in the 2022 piece, the interesting thing to watch will be whether the blockchains will be permissioned or public.

Some tokenized ETFs and MFAs trade on permissioned chains, but some are on Ether. In 2022 my intuition was that most trading applications would be via permissioned blockchains, but now I’m not so sure. I’ll revisit that issue as time allows me to get more up to speed on what is going on.

Kashyap, Stein, Wallen, and Younger have attracted a lot of attention with their recent Brookings paper, “Treasury Market Dysfunction and the Role of the Central Bank.”  Their pathbreaking findings: (a) Treasury basis trades are systemically risky; (b) the rapid growth in the amount of US bonds and notes outstanding increases this risk; and (c) Fed intervention is required to prevent the systemic risk in basis trades from crystalizing.

Wow! Who knew?

Oh, right. I did. Five years ago. (First draft, 7 August 2020). (The lack of a citation is very bad form, guys).

A year and a half ago I provided an answer to a question that most analyses of the basis trade begged: why does it exist in the first place? My answer: constraints on money managers ability to buy cash Treasuries directly, especially leveraged purchases. They achieve implicit leverage through futures, which is mirrored by the explicit leverage of basis trading hedge funds.

Kashyip et al tart up their paper with a simple model. The most important component of this model is money managers’ demand for long futures, but that is really assumed, not derived from a model that specifies the underlying constraint. They merely say: “In our model, asset managers pay the basis for the convenience of holding Treasuries off balance sheet.” Whence that “convenience” arises is left to our imaginations. (They also say “there are specific stories we have in mind” but apparently they’d have to kill us if they told us).

The only real contribution of the paper is their proposal that the Fed engage in hedged bond buying to absorb a (margin induced!) unwind by hedge funds. But that’s just a particular way of doing this: “As a consequence, the soundness of the U.S. financial system continues to depend on the prompt and effective intervention of the Fed as lender of last resort, with all the attendant moral hazard problems.” (They do discuss the moral hazards inherent in their proposal).

By way of faint praise, I do acknowledge that they don’t advance clearing as a nostrum. But I am somewhat startled by this statement: “Although clearing may have significant benefits, these benefits are largely orthogonal to the issues of concern here.” Especially since they also acknowledge that “when volatility increases margin requirements can increase as well, potentially triggering forced unwinds.”

Orthogonal? Hardly: margining, of both the futures and repo legs of a basis trade, is THE source of instability. It is at the heart of these issues. “Can increase”? Does increase. “Potentially triggering forced unwinds”? That’s exactly what triggers forced unwinds.

Their proposal–again, not really novel–that the Fed intervene is necessitated by two things: clearing, and the constraints that induce money managers to launder leverage via futures, rather than take leverage on their balance sheets. Meaning that they let off clearing and regulation far too easily.

The travails of Disney’s latest attempt at suicide-by-woke, the Snow White reboot, brings to mind one of the spawn of Frankendodd–the ban on movie box office futures.

In 2008, Howard Lutnick’s Cantor Fitzgerald filed to create the CX Futures Exchange to trade futures on movie box office receipts. Hollywood lost its mind, and in the post-GFC anti-derivatives hysteria and the resulting legislative response, succeeded in getting Congress to include a ban on such futures as one of the many pieces stitched together to create Frankendodd. Thus, such futures joined onions as the only thing that was otherwise legal on which it was illegal to trade futures.

Why was Hollywood so hot and bothered about box office futures? That’s what brings me back to Snow White.

Disney is in a panic because it is widely predicted that the movie will bomb epically at the box office, and in particular since it is Peak Woke, with the wokiest of actresses in the title role. As of now, those predictions are merely conjectures bandied about in the press or in online conversations, and don’t involve anyone putting their money where their mouths are.

But, if there were futures, all of that diffuse information about how bad the movie is, and how badly it will perform, would be crystalized into one number for all the world to see. And the number would have credibility, because the people behind it would have skin in the game. And it would have credibility because the futures would incentivize those with information about the picture to communicate that information to the market via their trades.

Futures would have been a box office bomb detection device.

Without futures, Disney can hope that many people won’t have heard the Hollywood chatter, or will discount it and give the movie a chance. And they can hope that woke reviewers will pump the movie.

Futures would have been a box office bomb detection device. If there were futures, there would be a hard number to communicate objective information about how bad Snow White is. Nobody would attend, and the movie would bomb.

Absent futures, Hollywood can fool enough of the people enough of the time with marketing and propaganda to at least mitigate the revenue disaster of a bad film. With futures, that would be much, much harder. Which is why they successfully importuned the authors of Dodd-Frank to strangle movie box office futures in their crib.