AI hysteria has predictably led to calls to regulate! regulate! regulate!, seemingly from all corners except for the Trump administration and NVIDIA’s Jensen Huang. Those baying for “regulation” include frontier model leaders Anthropic and OpenAI.
I put “regulation” in quotes because as is often the case, those calling for regulation are imprecise on just what regulation should entail, and have evidently given little serious consideration to how regulation works in practice. The word is usually more of a magic incantation than a description of something real that has any chance of realizing the goals of those using it.
So what should AI regulation look like? To answer that question, it’s important to start at the basics.
Decades ago Steve Shavell wrote two important articles that speak to the subject: “Liability for Harm Versus Regulation of Safety” (JLE, 1984) and “The Optimal Structure of Law Enforcement” (JLS, 1993). Shavell laid out several criteria for determining the best way to regulate.
Broadly speaking, there are two alternative methods of regulation. Harm-based (“ex post“) sanctions that attempt to deter harmful conduct, and preventative (“ex ante“) regulations. The former includes the common law tort system, and civil and criminal laws. The latter includes things like government approval for the sale of a product, with FDA approval of drugs being an example. Many of the calls to regulate AI fall in the latter category, and some members of the Trump administration basically advocate the former.
So which is best here?
Shavell identifies several factors that determine the optimal legal structure. On balance, these favor ex post/harm-based tort-like approaches.
The first criterion is “is the possibility of a difference in knowledge about risky activities as between private parties and a regulatory authority.” That is, information asymmetry between the regulator and the regulated.
This is quite intuitive. If an actor (an AI firm, in this instance) has better information about the likelihood its actions will cause harm, and the amount of harm it will cause than does a regulator, if it knows it will pay damages commensurate with the harm it causes, it can take the efficient precautions. It will appropriately trade off the cost of mitigating harms (including the cost associated with reduced performance) with the cost of the harms because the prospect of paying damages based on costs and probabilities of harms will induce it to internalize the costs of its actions.
In contrast, an ignorant regulator knows less about the probabilities and magnitudes, and is therefore likely to implement measures that are either overinclusive or underinclusive. That is, regulators are far more prone to Type I and Type II errors.
It is clearly the case with AI that the developers-the labs/frontier firms in particular-will be far better informed about their creations and the risks that they pose than any government regulator would be.
Shavell’s second criterion is “that private parties might be incapable of paying for the full magnitude of harm done.” That is, in the present context, an AI firm might be “judgment proof.” In the event, the actor will not internalize the costs it imposes on third parties, and will tend to take excessive risks and impose excessive harms. Here, external interventions that constrain actions in order to reduce the magnitude and frequency of harms can be superior to deterrence.
The inability to impose monetary damages commensurate with harms is one justification for criminal sanctions, including imprisonment or even execution. These are non-monetary penalties that enhance deterrence.
This is one factor that might-might-favor ex ante regulation. Anthropic or OpenAI (or any other firm) could not pay damages commensurate with harm if that harm is the end of humanity!
But this brings us back to the first issue. In deciding whether judgment proofness is indeed a problem, we need to have some assessment of the distribution of harms, and here the AI firms have a decisive information advantage. And as I discuss below, we absolutely cannot take at face value the pronouncements of Amodei, Altman, Musk, et al, on the potential harms posed by AI, or their likelihood.
And more, even though judgment proofness may preclude AI firms from fully internalizing costs of their products (especially the harms they may create), bureaucrats operating subject to low powered incentives clearly don’t internalize them. Nor do they internalize the benefits of more capable AI. Therefore, although judgment proofness may be a necessary condition for ex ante regulation, it is not a sufficient one.
Shavell’s third factor is “the chance that parties would not face threat of suit for the harms done.”
In the US legal system? As if! Indeed, the likely bigger problem with relying on the tort system to deter would lead to suits for harms not done, i.e., frivolous or baseless litigation.
Shavell’s last factor is the relative administrative costs of the two systems, i.e., the cost of a regulatory bureaucracy vs. the costs of litigation.
Both litigation and bureaucracy are costly. The advantage of ex post, harm-based regulation is that the litigation costs are only incurred when a harm incurs: no harm, no foul, no legal expense (with the caveat related to frivolous litigation mentioned above). In contrast, the bureaucracy must be fed continuously, even in the absence of harms.
And note that regulation would necessarily have to be international in scope. Look at the post-Great Financial Crisis history of banking and financial regulation for a demonstration of the monumental costs and challenges of international regulation. AI regulation would put even that in the shade, especially since it adds China and strategic competition into the mix.
To which, I would add another factor: influence costs, including regulatory capture. It is well known that the regulated exploit their information advantages and financial resources to induce regulators to take actions that benefit some regulated entities, at the expense of other entities and the public at large. This is especially likely to work when the regulated have a big information advantage (check) and a lot of money (check).
Overall, to me the scorecard tilts heavily in favor of ex post, harm-based, liability mechanisms as opposed to ex ante regulation. This is especially true given the radical uncertainty and information asymmetries inherent in such a dramatically new technology. We are learning the harms as we go, and the developers learn more and faster than any bureaucrat can. Indeed, being liable for harms gives the developers a strong incentive to identify those potential harms-their magnitudes and likelihoods. A far stronger incentive than bureaucrats have.
And that provides a segue into a discussion of one of the most fascinating aspects of the ongoing debate/hysteria: the fact that the frontier labs are the loudest and most strident voices calling for regulation. In my opinion, this is overdetermined.
A major reason is that Anthropic et al are deathly afraid of being liable for the harms their products cause. Palantir’s Alex Karp perhaps overstates the case when he says they are effectively calling for nationalization in order to avoid liability, but I think it’s pretty clear that avoiding paying for the harms their products cause is a major driver behind the calls–pleas, really–for regulation.
Which, to be honest, another reason to favor a liability-based system. It suggests they understand that even precluding the end of the world or singularity scenarios, they are aware that their products can do great damage. The only way to get them to act on this knowledge is to force them to pay the costs, and not be able to say “don’t look at me! The regulators approved what I did!”
Another reason-the one that has received most commentary-is that the frontier firms know that they would be able to capture their regulator(s) by exploiting their information advantage and vast wealth. And as George Stigler pointed out long ago, the biggest benefit a regulator can bestow on its industry masters is a reduction in competition, including by the erection of entry barriers. As I’ve written with respect to financial regulation, regulatory costs are largely fixed and therefore benefit large incumbents at the expense of new, smaller, disruptor entrants.
There is another reason that is laid bare by some of the specific rhetoric and actions of Amodei, Altman, et al: a belief that intense competition between the frontier firms, let alone between them and open weight models, small models, etc., will be ruinous to them.
The cabal has been negotiating in public for “pacing,” i.e., a coordinated slowdown of model development. Competition on the capability dimension is hugely expensive, especially with respect to training. I view the various public statements-including the apocalyptic rhetoric justifying a slowdown-to be “cheap talk” intended to coordinate reduced investment. A way of cartelizing the industry.
Which leads to another motive for favoring regulation–protection from antitrust laws.
A coordinated slowdown would also address something else that I think looms over them. The whole AI sector is a bet on future demand, future prices (which depend on demand and competition from non-frontier implementations), the costs of production (e.g., data center investment and operational costs, including notably electricity), and the execution timeline (e.g., will bringing data centers online be delayed by supply chain bottlenecks or local political opposition?) Doubts have been increasing on all these dimensions, and I strongly suspect that the industry insiders fear that their massive investments (at all levels of the value chain) will not pay off if the current pace and growth are maintained. That is, the “pause” is not driven by safety fears, but by fears of a financial bloodbath.
It reminds me of Stalin’s pausing collectivization because he was “dizzy with success.” AI has been so successful that it has led to dizzying excesses that need to be reined in.
So there are many reasons for AI executives to want a pause. I think all are operative, and all push in the same direction–regulation by means other than liability.
But the nature of the industry and the product and the economics of harm reduction mean that a liability mechanism is the best way to address the safety concerns surrounding AI. That mechanism would give AI developers the strongest incentive to control the risks that they lecture us about daily. Anthropic doesn’t need OpenAI to mitigate the risks of its models, and vice versa. Internalize costs via a liability system and they will unilaterally have the incentive to do so.
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.
[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.
[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.
[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.
Like the fracking wars, but only much bigger: the current war on data centers. This confict has achieved some notable results, including an outright ban on new data centers in New York, and restrictions even in Texas.
Much of the opposition is organic. Some is astroturfed, and traceable back to China: it provides fertilizer for even of some of the organic growth. As with all these things, a lot of the rhetoric and analysis is fundamentally confused, but there are important economic issues here that deserve serious consideratoin.
First among these is the impact of data centers on power prices. The industry and its backers have pushed back with studies claiming that the impact of data centers is benign, or actually beneficial. For example, an Institute for Energy Research policy paper does a cross sectional analysis of state electricity prices and concludes that states with the highest concentration of data centers do not have higher electricity prices, and that data center penetration is only weakly positively correlated with power price increases.
This is a superficial, not to say stupid, analysis. With respect to the cross section, data center siting is endogenous: developers tend to site in lower cost areas. With respect to time series, a state level analysis is obviously too crude. Furthermore, the analysis does not take into account capacity utilization.
Furthermore, it is based on EIA data on “Average Price of Electricity to Ultimate Customers by End-User Sector.” These are effectively regulated retail rates that roll in historical capital costs, that are effectively fixed costs, meaning that retail costs are based on average costs. As the authors state: “Electricity systems have large fixed costs. If demand increases, those fixed costs are spread over more kilowatt-hours, reduction average costs.”
The problem is that these are backwards looking measures, and that when demand starts pushing towards capacity, those declines in average cost will stop abruptly and be replaced by sharp increases. As Herb Stein famously said, “If something cannot go on forever, it will stop.” And we are at–or even past–the stopping point. Certainly, given projected data center growth, if the stopping point hasn’t been reached, it will be soon. Meaning that hyperscaling will drive up power prices, unless power producing and transmission capacity is increased commensurately.
It has been recognized for some time that the US electricity generation and transmission system is becoming dangerously overstretched. New capacity–reliable capacity (more on this below)–is essential. Data center growth will only stretch the system more.
In the short term, electricity supply curves–the marginal cost curve, which is what is really relevant for pricing–become vertical, or close to vertical. Additions to capacity push out the supply curves. Those additions are vitally needed.
Want evidence? PJM–the Independent System Operator that manages the power grid in the region where much of the largest data center growth has occurred–uses a capacity market to incentivize investment sufficient to meet power demand. Roughly speaking, PJM determines how much capacity is needed to meet anticipated demand, and holds an auction for that quantity. High capacity prices are an indication that capacity is scarce.
Here are the last 5 PJM auction results:
Delivery Year
BRA Held
RTO Clearing Price ($/MW-day)
Change
2022/2023
2021
$50.00
–64%
2023/2024
2022
$34.13
–32%
2024/2025
2023
$28.92
–15%
2025/2026
July 2024
$269.92
+833%
2026/2027
July 2025
$329.17*
+22%
Oh, 2026/2027 doesn’t look so bad, right? Er, the price hit the FERC-imposed price cap. God only knows what would have cleared the market.
What should you be thinking reading this? Here’s an idea:
Looping back to the study mentioned earlier, the days of declining average cost are over–unless capacity increases to accommodate the higher demand.
The long run effect of data center-induced demand growth on prices depends on the long run supply curve, which depends on a variety of factors, including technology, capital costs, the long run energy supply curve (especially the long run supply curve for natural gas)–and policy.
All but the last of these are largely out of the hands of policymakers. So what policies can make the long run supply curve as flat as possible given these other factors?
Perhaps the most important policy lever is to incentivize efficiently investment in “behind-the-meter” capacity, i.e., vertical integration of new capacity and data centers that will consume the power it produces. This kills a couple of birds. First, it adds to generating capacity. Second, it doesn’t add to the demand for transmission. Given that transmission is the main vulnerability of the grid at present, this is an important consideration.
Some months ago, I analogized an xAI (now SpaceXAI) investment in gas generation co-located with its Memphis data center to mine mouth coal generation plants, and argued that transactions costs considerations provided an incentive for such vertical integration and coordinated co-investment. Those economic considerations apply with data centers, but the electricity market is highly regulated at the state and federal level, and those regulations can affect the incentives for BTM investment.
The crucial issue to provide the appropriate incentives to data center developers. One of the enduring economic lessons that I have learned (from my high school economics teacher, no less) is that a maxim of business success is to “externalize costs, and internalize benefits.” If the costs of connecting a data center to the grid, or the costs of transmission, or other factors that determine the opportunity cost of a BTM investment do not reflect actual costs, developers will have too weak an incentive to vertically integrate because they can externalize their costs. The opportunity cost to colocating generation with a data center is the cost of electricity drawn from the grid. If data centers do not pay the full cost of doing so, they will invest too little in building their own generation.
Data centers don’t want to pay high costs for power, obviously. But if their consumption from the grid is subsidized, they will consume too much from it, and build too little behind the meter capacity.
So, as always: Get the prices right. I say again: Get the prices right. Unfortunately, the politicization of electricity rate setting means that seldom happens.
Some of the biggest subsidies for data centers come from tax benefits granted by states. Just stop. Please. And if you can’t break the habit, limit the subsidies to generating capacity.
The other big policy change that can mitigate the impact of data centers on power prices is to terminate, with extreme prejudice, the monstrosity of “climate crisis”-driven laws and regulations that severely distort incentives to invest in generating capacity. These penalize investment in fossil fuel generation and and encourage investment in renewables. Absent subsidies, renewables are not cheaper than conventional generation. If you want cheaper electricity-where you strip out subsidies in determining cost-reverse these destructive policies.
This is particularly important when one considers that price is not the only relevant consideration here: reliability is too. Greater renewables penetration reduces reliability, all else equal. When you consider the true cost of power, which includes the cost of service interruptions (which can be extreme) and the cost of lower quality power, reliability is an important consideration that has been largely ignored in the force feeding of renewables onto the grid.
Ironically, the unreliability of a grid with a decidedly suboptimal generation mix may encourage behind-the-meter investment. Data centers need reliable power. If they can’t get it from the grid, they’ll decouple from the grid. But there can be too much BTM investment just as there can be too little. Both result from getting the prices (and incentives) wrong.
Some states have responded to impending overwhelming of the grid by data centers in a predicable way: by restricting or stopping altogether data center development. In the case of states like New York this response was foreordained by their previous perverted policy choices which were largely driven by climate hysteria. If they want a long run fix, they need to jettison their hysteria-induced policies.
Will they? As if.
This post has focused on power. Another sensitive issue is water. I don’t know much about the technical issues involved here, so I’ll just make one observation. Markets for water don’t exist, for all practical purposes. It’s almost certain that the prices are wrong. Meaning that it is almost certain that data center water consumption is inefficient. Knowing that doesn’t require knowing anything about the technology.
Unfortunately, it is necessary to remember Chesterson’s Fence in this context. That is, to ask “why aren’t there markets for water?” The answer is ultimately a political one, and as a result hopes for a market-based water pricing mechanism are in vain. Meaning that concerns about data center water consumption are likely to be addressed with a meat cleaver–no data centers!–or not at all–go ahead, build what you want! The discriminating, balancing decisions about water consumption that would occur when water is at least remotely priced correctly will therefore not occur, and the water issue will become a bone of political contention.
In sum, the correct approach to the data center war is Econ 101: GET THE PRICES RIGHT. The prices of connecting to the grid. The price of transmission. The price of energy. The price of reliability. The price of water.
And that’s why I am pessimistic about the ultimate outcome of this war. The products involved-power and water-are highly regulated and highly politicized. Regulation and politicization are inimical to getting prices right. Regulation and politicization were not imposed by aliens. They are the equilibrium outcome of political processes that are unlikely to change. Furthermore, the political salience of energy prices in an era in which “affordability” has heightened political valence means that pricing perversities are likely to get worse, not better.
Sorry to break it to you. But you probably didn’t come here to get sunshine blown up your backside.
Although I broadly support Trump on foreign policy and many domestic issues (notably immigration) I find his pronouncements and policy proposals on economic issues to be cringey at best, and downright idiotic at worst. As I discuss below, the emphasis is on “his”–many administration policies are actually quite laudable. But when he pontificates on economics–oi.
Tariffs are obviously front and center of any discussion of Trump policies. Here matters are more complex, precisely because one cannot evaluate them purely in terms of economic policy. Yes, like most economists, I believe tariffs qua tariffs are objectionable (with a caveat–see below). But with Trump, tariffs are a negotiating tactic, and even as such not just a means of extracting trade concessions from other countries. He employs them to achieve geopolitical objectives, such as forcing India to wean itself from Russian oil.
Even though one cannot evaluate Trump tariff-o-philia in purely economic terms, certain aspects of his tariff policies are positively daft. The most principled economic argument one can offer for tariffs is derived from the “theory of the second best.” Specifically, yes, tariffs are distorting, but so are other taxes. It is at least conceivable that raising revenue via tariffs is preferable to doing so through other taxes (including future income taxes, capital gains taxes, etc.).
But then Trump completely undermines that defense by promising/proposing to use tariff revenues to give every American a check, $2000 if memory serves. If implemented, this would leave us with all of the distortions of tariffs but none of the fiscal benefits.
Then there is the proposal to prevent corporate purchases of single family homes, purportedly to reduce the cost of homeownership. In justifying this proposal, Trump engages in all the blue sky and apple pie rhetoric about the wonders of home ownership that rationalized the policies of the 1990s and 2000s that culminated in the Great Financial Crisis.
The proposal and the rhetoric fail to answer–hell, even raise–a crucial question: why have corporations invested in single family homes? Are there some other underlying economic factors or policies that have driven this?
My conjecture is that it is the consequence of banking and lending policies adopted as a response to the (housing policy-driven) GFC that made mortgages more expensive and difficult to obtain. People who want to live in single family homes find it more economical to rent them than buy them, and the corporations are responding to that.
So if you believe the post-crisis regulations were needed and beneficial, the corporate purchases are a feature not a bug. Maybe you don’t think that these regulations are on the whole beneficial, but if that’s the case constraining corporate purchases will make things worse, not better. This won’t remove the constraint that is driving the purchases, but will reduce the supply of SF rentals.
In any event, suppressing a symptom without even attempting to understand the cause is a recipe for failure.
And then the other day Trump turns around and says he wants home prices to be higher. Choose one, man.
Then there is the policy of capping credit card interest rates. Here one cannot even make an argument like that for tariffs (i.e., there is some economic benefit that could offset the effect of the distortions). The effect of this will be to harm many of those it is intended to help. Credit card interest rates reflect the credit risk of those who use cards to borrow regularly, and often heavily. Capping rates will result in those people losing cards, and therefore access to that credit. Some will do without credit altogether. Others will turn to even more marginal and expensive sources of credit, such as payday or auto title loans.
All of these policies have something in common: they are fundamentally populist. Populist critiques of the current American system have considerable validity. However, populist policies are seldom salutary responses to those critiques.
The irony is that overall, Trump administration economic policy is pretty good. This is especially true regarding deregulation, with this week’s elimination of the Obama EPA’s CO2 “endangerment” finding being a particularly welcome example, but by far from the only one. Across the spectrum of energy, environmental, financial, and telecom regulation considerable progress has been made.
Maybe the best thing that can be said for Trump’s personal forays into economic policy is that his unmatched ability to attract attention and criticism is preventing critics from going after these good policies with the same intensity they otherwise might. Cringe has its uses, I guess.
I vacillate between calling Germans the world’s dumbest smart people or the world’s smartest dumb people. Whichever way you look at it, however, it is abundantly clear that a people that has produced amazing scientists, engineers, philosophers, and writers has a penchant for self-destructive stupidity, e.g., starting world wars that they have no hope of winning that result in national destruction.
Germany’s recent idiocies fortunately have been more contained in their effects, and have been especially concentrated in energy policy that has wreaked havoc in Germany itself. Energiewende. An obsession with renewables in a climate where the wind often stops blowing and sunshine is extremely limited for months at a time. Closing nuclear power plants. Carbon neutrality. Dependence on Russian gas.
The result of these combined acts of self-harm has been very high energy prices. These high prices have exacted a heavy toll on German manufacturing. Which is kind of a bad thing when manufacturing is your comparative advantage.
The resulting economic malaise has spurred the government into action. Prices to high? No problem! Impose price controls!
Because price controls always work, right? Right?
To illustrate just how dumb smart people can be, I give you this:
In this case, high prices are the result of insufficient supply (capacity). Indeed, high prices are a signal of scarce capacity, and provide the incentive to invest in capacity.
Capping prices distorts the signal, and eliminates the incentive to add capacity. Capping prices ensures that new capacity will never materialize, let alone materialize quickly. Meaning that the policy will inevitably have the exact opposite of its intended effect.
The caps might lead to increased industrial production, and thus increased consumption of electricity by manufacturers. But since prices are high because of inadequate capacity, where is the electricity output supposed to come from? It will have to be diverted from other consumers. Who will have to pay a higher price either directly in the form of higher prices, or in the form of reduced quality (e.g., lower reliability), or indirectly via taxes, and through lower consumption.
I’m sure they’ll be pleased. But that assumes that they realize the connection, because the government has every incentive to hide the higher cost. But a higher cost there will be.
Alas, the Germans are not unique in seizing on price controls to combat high prices. (Yeah, Mamdani–I’m looking at you). This despite the fact that at least since the time of Diocletian, the results have been the same–bad. Very bad.
In many cases–and I believe this to be so with Germany today–it is because the high prices complained of are the result of government policies, and rather than admit failure/error and undo the destructive policies, governments seize on a known nostrum that will inevitably fail. But in the meantime the government will have kicked the can down the road and claimed to have done something. The lag between doing something and the reckoning with what has been done gives it time to come up with new excuses, and new bad policies.
So enjoy, Germans. You are like the Delta guys in Animal House, intent on futile and stupid gestures. Except without the charm.
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.
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
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.
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.
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.
“Haven’t seen you around recently. So what’s up?”, says you. “Same old, same old: Just the Trump gamma trade”, says I. (After the 2016 election I wrote that a Trump presidency meant you should be long gamma. That’s even more true after 2024).
So the tariff two step has continued. Trump caved! Xi caved!
Whatever. If you believe (as I said literally on day 1, AKA Liberation Day) that the initial tariff salvoes were a negotiating tactic, the outcome was expected. Make maximalist demands, negotiate down from there.
NB. The negotiations continue. This ain’t over.
I only remark that if Trump had announced the current (interim) levels on 2 April, the frenzy would have no doubt been about the same as it was with the 125 percent charge for China, etc. But now these levels (30 to 50+ percent, depending on how you calculate) are greeted with a huge sigh of relief and a complete reversal of the market selloff that greeted the initial announcement. In other words, if you panicked, you were played.
Another fairly recent development has been the prescription drug EO. The initial (over-) reaction has been that Trump is ordering price controls. Well, not exactly. (The irony here is that the right has been critical, and the left hypocritically silent, because Trump).
At the outset, let me say that (as I will explain in more detail below) I am ambivalent about overall policy framework. But the reason I say there has been an overreaction is that all it is right now is just a framework. The only real action item is a direction that HHS formulate a set of rules that will require pharmaceutical companies to allow consumers to purchase directly from them at “most favored nation” (“MFN”) prices. We know how long the rule process takes. We also know that the devil will be in the details of the rule, and those details will be determined in a highly politicized environment where the regulated have a huge say, so it is impossible to draw conclusions about what effects it will actually have, or when it will have them.
Trump’s basic attack–that comparisons between US and foreign prices for the same drugs prove that Americans are being ripped off–is an old one, and ironically was a talking point of Hillary in 1992-1993. The policy piece I wrote in 1993 (“Political Rhetoric and Stock Price Volatility”) addressed this very issue. (Ironically, it was that piece that put my on Hillary’s Hit List, the figurative one at least; it didn’t cost me my life, but did jeopardize my career).
Defenders of this pricing structure claim that it is purely economic conduct by pharma, specifically, price discrimination. More elastic demanders pay a lower price than inelastic American ones.
Yes, this might be why some drugs are dirt cheap in Africa, say, but it is hardly the entire story. Drug companies have to offer big price cuts to high income countries (European countries, Canada, etc.) whose demand is likely about as inelastic as the US’s, so that their national health services will buy them. One interpretation is that these countries are free riding off the innovation paid for by US consumers who pay full fare.
Which raises another issue. Presumably Trump believes that the MFN rule he has outlined will result in drug makers dropping US prices to match foreign ones. But no doubt some of the movement will go in the other direction–the companies will raise prices in other markets, and in some cases will decide to forego some smaller markets altogether rather than have their prices drive those in the really big market. Meaning that the ultimate effect on US price levels is hard to determine.
One part of the EO actually addresses this issue. It directs the U.S. Trade Representative and the Secretary of Commerce to ensure that foreign governments are not taking actions to undercut US prices. (The order incorrectly states that such undercutting raises US prices). That is, this is really a trade issue, and in particular a non-tariff barrier/IP expropriation issue that is more properly addressed in the context of trade negotiations.
The EO–as is the case with most policy proposals directed at drug prices–ignores the real drivers: patent protection, R&D costs, and third-party payments. If you think drug prices are too high, that is, the prices are in excess of what is required to induce the efficient level of innovation, then go after these issues. Examine whether drug patent protection should be attenuated (not extended as it was in recent years). Whether regulatory burdens unnecessarily inflate R&D costs. Cross-country price comparisons tell you nothing about these issues.
Insofar as third-party payment is concerned, the EO does address that issue to some degree. Specifically, the direct purchase option (subject to MFN provisions) is intended to “cut out the middleman,” namely, the Pharmaceutical Benefit Mangers (Caremark Rx, LLC (CVS), Express Scripts, Inc. (ESI), and OptumRx, Inc. (OptumRx) being the Big 3). These are accused of (a) charging high markups on drugs, and (b) excluding many drugs from their formularies.
The first issue is again related to relative prices, not price levels. That is, it is really about who captures the monopoly rent that ultimately derives from patent protection. The main concern is double marginalization–the inefficiency that results from back-to-back monopolists. Double monopolization can lead to prices that are higher than monopoly prices. Another concern could be that the PBMs exercise monopsony power, thereby reducing the returns to R&D and suppressing innovation. (This makes PBMs analogous to foreign governments).
I am always skeptical of “cut out the middleman” arguments because they ignore Chesterton’s Fence: why do the middlemen exist in the first place? If they bring cost but no benefit, how can they survive?
I don’t know the answer in this case. But (a) I am sufficiently skeptical based on long experience so as to not fully credit the seductive cut-out-the-middleman argument (which is almost always wrong, unless the middleman is a government creation), and (b) relatedly, would not be surprised if PBMs are ultimately the creation of the baroque (and broken) regulatory structure in the US. If my suspicion (b) is correct, going after middlemen attacks the symptom, not the cause, and the underlying problem will persist.
All that said, just as the case with foreign-benchmarked MFN pricing, it is naive to think that the direct purchase mandate will lead drug companies to reduce direct-to-consumer prices to the level they charge PBMs. The rules would totally change the negotiating dynamic between them. And again–and this is crucial–this is mainly about how monopoly rents will be distributed/split, not the amount of the rent (and thus not the final price to the consumer), which is driven by the underlying market power inherent in patent protected medicines.
In sum, I think this EO’s significance has been vastly overstated, because (a) it is essentially a “mission order” that sets out a broad objective, and the effects of which will be driven by the tortuous politicized rulemaking process required to carry it out, and (b) does not touch the underlying drivers of pharma prices.
Another major issue of the past few weeks is the (in Trump’s words) “Big Beautiful [budget] Bill” working its way through the House.
First off, let me say that it is almost certainly the case that nothing Big in government is really beautiful. Hell, who even knows what’s in it, precisely because it is so big? It be like “Yeah the chick in the burkha is really beautiful! And she’s actually a chick! Take my word for it!”
Its main attribute is that it will–if the legislative legerdemain works–mitigate the logrolling and secret last minute deals that corrupt the standard budgeting process. Mitigate, not fix.
The “beautiful” bill has some real warts. Hell, cancers, maybe. A sample.
An elevated state and local tax (SALT) exemption. Elevated so high as to be virtually non-binding. A sop to high income, high tax states which seriously undermines their incentives to control spending and taxation. If California, New York, Illinois, etc., want to raise taxes and spend more, let them fuck their own citizens, not the rest of us. Maybe that will force their citizens to get their minds right, and set their governments straight.
Serious backsliding on the rollback of Biden’s grotesque, and grotesquely named, “Inflation Reduction Act.” It should be terminated in its entirety, with extreme prejudice. But this bill–which mind you cannot bind future Congresses–delays implementation of subsidy cuts and mandates until four years from now, at the earliest.
Gee, why four years, I wonder?
The renewables subsidies and mandates make energy excessively costly in return for no meaningful environmental benefits. They are also unaffordable given America’s parlous fiscal situation.
Notably, Rumpublicans are among those pushing to save these monstrosities.
Virtually noting meaningful on entitlement reform. Even extremely modest changes to Medicaid have elicited shrieks and wails, and not just from Democrats.
The US fiscal situation is far and away the greatest danger our to economic prosperity and national security. But nobody–least of all Trump–is taking it seriously. Like Scarlett O’Hara, they will think about that tomorrow.
Except, apropos CCR, that day never comes.
So we seem destined–doomed–to go bankrupt like Hemingway’s Mike Campbell, “Gradually and then suddenly.”
DOGE and Musk held out the (false) promise of preventing this fate by attacking “waste and inefficiency.” Even if such massive inefficiencies exist, DOGE didn’t go through them like a buzzsaw: instead, DOGE has been buzzsawed and Elon has slunk away, achieving a dime on the promised dollar at the very most.
And at the outset, I expressed skepticism–though not enough–that the supposedly “easy” route of restoring fiscal sanity through attacking waste would actually work. The brutal fact is that without addressing entitlements we are on the highway to fiscal hell.
The most depressing takeaway is that the supposedly transformative and revolutionary Trump presidency is at most tinkering with an unsustainable status quo, and even that elicits frenzied, and indeed unhinged, opposition. The nation is as unserious about addressing its existential fiscal problems as was France in the 1780s, and just as politically poisonous.
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).
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.
