Uniswap LP Profitability Update

 Most crypto traders are confused by the Impermanent Loss (IL) generated in Uniswap’s Automated Market Maker (AMM). The question below posed on the Uniswap Reddit page is an earnest query that, unfortunately, generated the same answer quality a Medieval doctor would have offered on how to prevent scurvy. 

Impermanent loss is an unavoidable consequence of the liquidity provider’s (LP’s) negative convexity. If you could avoid this cost via hedging, options would not have an option premium; they would be priced at intrinsic value. Methods to avoid this loss by providing liquidity to pools with two volatile tokens like ETH and WBTC, or pools with three or more coins, make the IL less conspicuous instead of removing it. 

Crypto enthusiasts must understand that LPs generally lose money in AMMs due to IL. It is difficult enough to predict the future, but knowing the present is a lot more straightforward and provides the basis for any prediction. 

Deriving the IL for an AMM

I will start first with a basic derivation of IL for a constant product AMM. I will apply this to the ETH-USD pair, but these formulas apply to any token pair. 

We start with the formulas for ETH and USD pool tokens. Liquidity is both an intuitive and technical term within the AMM, and p here would apply the price of ETH in terms of USD. 

The value of this LP pool position at any time is given as follows.

Substituting for the token amounts given the above pool equations, we get can derive this simply in terms of the price and liquidity for an LP.

Taking the derivative of the LP’s pool value with respect to the price, we get the delta

The derivative of the delta, or second derivative of the pool value, is thus

Given this gamma in the AMM pool, we can apply this to the Black-Scholes formula for convexity cost (gamma/2 times variance), which gives us

The convexity cost equals the option premium via an equilibrium argument where profits are zero: if they were positive, it would not be an equilibrium because sellers would enter the market; if profits were negative, sellers would exit. This argument is used in the famous Black-Scholes equation. 

This method of estimating the convexity cost is to determine the option value of an option. 

 Convexity Cost = option premium

This does not mean a convexity payout equals its cumulative convexity cost in every case. A single option payout represents one observation like a draw from a normal distribution reflects the distribution. 

 ATM straddle payoff = N(optionPremium, s_payout)

For an AMM LP position, which is like an ATM straddle, the convexity cost is the expected IL instead of an actual IL. This is also the IL a good market maker should approximate, in that good LPs hedge their delta risk, and hedging does not change the expected IL, but minimizing its variance reduces capital costs.

 Convexity Cost = E(IL)

With the convexity cost function for an AMM, we can apply the daily volume and liquidity data from Uniswap’s pools and the actual daily volatility for the assets to calculate the average daily profitability for the LPs of these pools. I used daily liquidity and volume data from two of the most prominent Uniswap pools. I calculated the daily variance using the daily price and minute-downsampled price data daily. I then present the monthly average daily data to see if it’s trending. This is in the table below.

Uniswap ETH-USDC Pool Profitability

Monthly data contain average daily values. Average daily volatility was taken from down-sampled minute data. Gross Margin is (revenue-convexity cost)/revenue. Liquidity and volume data are from Uniswap’s Ethereum mainnet. Data through March 21.

 

This table shows that LPs have consistently lost more money via their IL than they made in trading fees. Worse, there is no trend. 

The best explanation for the persistence of LPs despite losing money is that they are not calculating the option cost. This would explain the lack of growth, as smart money is not entering this new market. 

Why are LPs so Stupid?

One reason the LP convexity cost is not appreciated is that it is not a direct cash charge. Instead, it’s cost relative to a pair of assets, as opposed to a simple USD value, which is uncommon. Consider the LP’s pool value, which can be represented as a linear function of the square root of the ETH price. 

In comparison, the value of their initial deposit is a linear function of price, given an initial deposit of x USD and y ETH.

The value of the initial deposit and the LP function are equal initially. However, as the LP value function is concave, and the initial portfolio value is linear, we know the LP’s future pool value will always be less than the initial portfolio pairs. The value of the LP position as a function of the ETH price is an increasing concave line tangent to the value of the LP’s initial deposit, as seen in the figure below. In equilibrium, fees should compensate for this predictable loss. 

The daily IL is imperceptible without proper accounting. For example, the daily ETH price volatility is around 4%, and half of an LP’s pool value is from ETH, while the IL averages around 0.03%. Further, the benchmark is ambiguous. When the price of ETH rises, the LP’s pool value also rises (segment D in the figure below), just less than it would have if not in the pool (segments C+D). When ETH prices decline, the pool position declines (A+B), but so would their original portfolio (A), though less so. To appreciate the option they are implicitly selling, they would have to look at their position relative to an initial position that most only remember at its USD value. 

IL Cost Superficially Ambiguous

LPs ignorance is not remedied by more academic studies of LP profitability. Almost all focus on the realized IL of actual LPs assuming none of them hedged. This is like testing option returns by looking at the returns on options independent of the hedge, which no institutional option market maker does (I used to work for one). For example, a significant study by Topaze Blue (Loesch et al., 2021) found that Uniswap pools generated $199.3M in fees over a period that incurred $260M in IL, and 49.5% of LPs lost money. Such a takeaway obscures the profound fact that the LPs lose money because it is tempting to think that those LPs with clever tactics were among the half that made money, and all one has to do is figure out what those tactics are. 

Realized ILs will equal the convexity cost over many years, but the realized IL will have much greater volatility in small samples. This is related to why option sellers hedge their positions: reduced volatility reduces risk, which reduces required capital. If option market makers hedge their portfolios, those researching option expenses should use estimates as if the option was hedged. That is implicit in comparisons of implied to future volatility.

Why Realized IL is a Bad Metric 

To help see the relative efficiency of these two approaches for estimating IL, I estimated the small-sample properties of both approaches using a Monte Carlo simulation. As I present about 600 daily Uniswap pool observations in my Uniswap LP profitability table above, I generated the price paths over 20 periods of 30 days to get a sense of the sample volatility. I recorded the mean and volatility of the two ways of measuring IL: realized IL based on starting and ending price, and convexity cost based on daily volatility. 

Monthly Realized IL in Monte Carlo 

I assumed fixed liquidity and had an initial price of 100. As I am interested in comparing one approach to the other, the specific numbers are irrelevant as long as they are the same for both approaches. We are looking at the relative differences generated by these IL estimation methods. The monthly realized IL in the samples can be simplified to the following (they are mathematically identical).

Monthly Convexity Cost

For the convexity cost approach, I used the basic formula derived above, that is:

Applying this using time series of price and volatility generated the following formula for each monthly estimate. 

These two formulas were applied to one million simulations to estimate means and standard 

Monte Carlo Estimation of Uniswap LP Costs

ETH minute-down-sampled data from July 2021-March 2023 were used to estimate daily volatility. These daily volatility estimates generated a heteroskedastic random price time series. 20 sets of 30-day realized IL and convexity costs. Both approaches assumed a liquidity=1000 and an initial price of 100.

The absolute numbers do not matter, but the relative ones do. The results above show these approaches have approximately equal mean estimates for the IL, as expected. However, the sample IL approach had a three times larger standard deviation. Intuitively this makes sense because the convexity cost approach uses daily prices to estimate the next day’s price variance, information any hedger would use when managing their convex positions. In contrast, the realized IL approach uses only the start and end prices. As with many options results, many ways exist to prove and intuit these findings. results. The bottom line is that the convexity cost formula dominates the sample IL approach to estimating expected IL (something a good hedge can lock in). 

There should not be much doubt that LPs are consistently losing money. Those LPs fortunate not to lose money in the TopazeBlue study were simply random draws that were below average instead of clever. 

I don’t want to pick on TopazeBlue. Still, if one of the most prominent studies of ILs is misleading, it is understandable most LPs, who are not quants, will not see that LPs lose money outside of random shocks that obscure this loss when not hedged. The lack of LP profitability also explains why well-capitalized groups are not adding liquidity to these AMM pools. 

Solutions Are Generally Lame

One hope for these AMMs is that volume increases substantially. We can see both the 0.05% and 0.30% Uniswap ETH-USDC pools generate losses, but the lower-fee pool losses are considerably greater. Thus, one potential solution would be to eliminate the 0.05% pool, which would drive the ETH-USDC traders to the 0.30% pool. That might help, but I do not see how it could be mandated.

Most other standard solutions are less promising. Note that given these pools lose money, no amount of capital efficiency helps, which includes yield stacking. If you can take your LP tokens as collateral elsewhere, that doesn’t make them profitable; it just means you are leveraging your money-losing investment. A stock that loses 12% a year is not turned into a good buy at 10-to-1 leverage. 

The more common lousy solution is to use the reasoning Sam Bankman-Fried outlined in his interview with Matt Levine. These AMMs add payout via convolution, such as bonus points paid in a new token which get multiplier points when staked for a year. Staking reduces the selling pressure, allowing insiders to maintain a price that does not reflect the total market value of the tokens. These are classic crypto grifter tactics: the 2017 Bitconnect Ponzi scheme took in bitcoin and paid out insane yields in its own coin and promised even higher returns if users staked their funds for a year. Surprise! Once new users stopped coming and bitcoin stopped rising, Bitconnect disappeared and left everyone with its worthless coins (eventually, $17MM of $2.4B was recovered!). 

Many people in crypto have gotten wealthy using crypto-Ponzi tactics, and these aspiring SBFs fund many crypto projects, researchers, and journalists. These serial entrepreneurs are typical hucksters who know the right buzzwords and can hide behind the inherently complicated nature of blockchains and smart contracts. They do not drive serious innovation, just the next Dentacoin, Celsius, or Augur. 

Tactics that are either foolish (leverage) or scammy (worthless rewards to pump usage metrics) try to elide the existential LP problem in AMMs, which are the basis for DeFi. People in crypto need to know about persistent LP unprofitability before they can and will address the underlying problem. 

Why Trusted Blockchains Cannot Support CLOBs

Today's most efficient and liquid exchanges are central limit order books (CLOBs). It is a computerized system that aggregates and matches buy and sell orders for a particular financial asset, such as a stock, currency pair, or commodity, in real time. In a CLOB, market participants can place their orders to buy or sell a specific asset, and these orders are then listed in the book according to their price and time priority. The system automatically matches buyers and sellers at the best available price, filling limit orders that arrive first (i.e., price-time priority). Orders outside the current market price sit on the book as resting limit orders, available for traders until they are canceled.

A Limit Order Book

While these represent the gold standard, they require a degree of low latency (i.e., speedy) that is only attainable because they are centralized (ergo, CLOB and not LOB). This allows market makers to physically place servers running their market-making algorithms next to the exchange servers, allowing communications within milliseconds. Any decentralized trading platform cannot directly compete with these platforms as a price discovery mechanism.

To understand why a CLOB cannot work on a blockchain, it is helpful to understand the economics that drives its equilibrium. A CLOB has three types of traders: uninformed, informed, and market makers. The informed invest in data, models, and hardware to identify temporary mispricings and use their comparative advantage to snipe stale limit orders. The uninformed are ignorant for rational and irrational reasons: they may want to buy a car (liquidity traders) or are delusional and trading on irrelevant information (unintentional noise traders). As informed and uninformed traders do not show up simultaneously, market makers arise to provide liquidity continually by posting resting limit orders to buy and sell.

In equilibrium, all groups generate benefits equal to their costs. The informed trader’s costs—investments in hardware and statistical algorithms—are balanced by revenue from adversely selecting stale market maker limit orders. Uninformed traders pay the market maker by crossing the spread, benefiting from convenient, quick trading. Market makers post knowing they will trade with both types of traders, setting limit orders such that the revenue from uninformed balances that they lose to the informed.

There are many scenarios where low latency is costly, but one applied to a market maker providing resting limit orders should suffice. A market maker places a two-sided order to buy or sell 100 shares of XYZ stock trading at a bid-ask price of $20.17-$20.18, its current bid price, $20.17. Assume the stock will move up or down $0.05 before you can cancel that order. If you get filled, it will only be because it is now trading at $20.12-$20.13, meaning you bought at $20.17 and can sell now at $20.12; you lost $0.05; if the price went up $0.05, you would probably not be filled on your $20.17 order to buy. This is called ‘adverse selection,’ where conditional upon getting filled, you paid too much or sold too low. It generates a loss profile for market makers like for those selling straddles or a liquidity provider’s impermanent loss.

The effect of higher latency on a central limit order book is a classic example of Akerloff’s ‘Market for Lemons’ (Quarterly Journal of Economics, 1970). [1] In that paper, he analyzes markets where parties with asymmetric information separate, so the only viable transactions are those with a negative value, and the market collapses (i.e., no trades).

The lemon’s problem applied to limit order books is the following. High latency leads to market makers suffering higher adverse selection as it amplifies the relative speed advantage of informed traders, causing market makers to increase their spreads. Higher spreads discourage uninformed traders. With fewer uninformed traders, the market maker widens his bid-ask spread further to protect against adverse selection by the informed traders by making more profit per uninformed trade. This discourages more uninformed traders, creating a positive feedback loop until none are left.

Another way to think about the necessity of liquidity traders focuses on the zero-sum nature of trading without them. With no liquidity traders, the remaining participants are then playing the unattractive game of trying to outsmart and out-speed others who have made the same commitment. The Milgrom-Stokey ‘no-trade theorem’ (Journal of Economic Theory, 1982) states that if all the traders in the market are rational, all the prices are rational. Thus anyone who makes an offer must have valuable and accurate private information, or else they would not be making the offer. Similarly, Grossman and Stiglitz’s ‘Impossibility of Informationally Efficient Markets’ (American Economic Review, 1982) shows how without liquidity traders, no one has an incentive to put information into markets because the other rational traders infer what he knows via his market demand. There are no trades because every order is presumed to be informed and thus unprofitable for the other side.

A deficiency of liquidity traders creates a positive feedback loop that causes markets to ultimately unravel. There will not be enough liquidity traders to support an active set of market makers, who need the uninformed retail flow to offset their losses to the informed traders. The high spreads and meager volume on decentralized limit order book exchanges are consistent with this result (e.g., Augur).

One can imagine the layer 2 blockchains will eventually become fast and secure, preventing this problem. However, even in this case, miners or validators can sequence transactions with some discretion. It takes 60 milliseconds for light to travel from Tokyo to San Francisco, creating a large lower bound to this discretionary time window. Successful market makers on modern CLOBs have reaction speeds of 5 milliseconds, implying the feasibility of front-running such a system with impunity (see Aquilina, Budish, and O’Neill, 2020).

A CLOB has price-time priority, so it fills limit orders first by price, and within a given price using first-in-first-out logic. Even without a minimum tick size, the sequencers could front-run limit orders by posting orders conditional upon the price in the newer orders. There is no way for the layer 2 validators to agree on the time sequence of transactions if it is configured to prevent censorship, which would require a globally distributed set of validators. Given the disproportionate advantage of being first on limit order books, the unavoidable sequencing discretion makes transparent competition impossible, enabling and encouraging corruption.

Low-latency chains like Solana, meanwhile, are centralized, which invariably leads to corruption via Acton’s Law. This centralization is not obvious, as many have more validators than Bitcoin or Ethereum (e.g., EOS has 21), but this Nakamoto coefficient is meaningless because the validators on low latency blockchains have to work together, and they are invariably controlled by a central agent. When a blockchain representative proclaims a bald-faced lie about a foundational crypto principle, its developers fall down the slippery slope, leading to more lying and, ultimately, a cesspool of deception. In markets dominated by unaccountable insiders, we should expect every sort of malicious trading tactic (e.g., FTX pumped its Serum token via its low-latency Project Serum exchange on Solana). This leaves blockchain CLOBs only for tokens with no alternatives, like in markets for NFTs and shitcoins.

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[1] In this application, the market maker trading with informed traders generates a loss, like a lemon car, hoping to offset this with his trades with ‘peach’ cars (gains). In 70’s slang, a ‘lemon’ is a bad type, as opposed to a good type, e.g., a ‘peach.’ 

Perp Funding Rate Farce Continues

 

Recent 20% crypto movements have demonstrated again that perp markets continue to gouge their customers. I have written about this several times before, but as it is economically significant, corrupt, and continues unabated, I figured it would be worth revisiting. Alas, reading my old posts, I can see how I muddled my point, perhaps explaining why I am a lone wolf on his issue, so I hope this is clearer. 

The funding rate mechanism used to link perp prices with spot prices is a farce in that it does not and cannot tie a synthetic price with a spot price via arbitrage, and in practice, it is used to defraud its users. As a practical matter, the perp price is a Schelling point in that its obvious target is the spot price, and the funding rate is just there to make traders feel comfortable that it is not merely a Schelling point. The fact that there is a vague relation to an equilibrating mechanism is good enough for most traders, as they are happy to use centralized platforms like BitMex and DyDx. As in those cases, many users are happy to have access to perps as long as it seems fair.

One can forgive the perp funding rate scam as its foundational white lie facilitated a much-needed market. In 2016, short or leverage bitcoin was impossible, but there were no exchanges to do this. All one could do was swap one token for another and generate an unleveraged long position. There were no stablecoins or wrapped Bitcoin. BitMex, a centralized unregulated exchange that only took bitcoin deposits, created the first popular perpetual swap, aka ‘perp.’

Instead of an expiration date and settlement in a perp market, a perp anchors its price to the spot via a funding rate mechanism. When the perpetual contract’s price exceeds the spot price, the story is that this implies longer than short demand. The long traders pay short traders a fee proportional to this price premium to equilibrate the market. Crypto funding rates prevent continuing divergence in the price in perp and spot markets.

The perp premium is the percent difference between the perp and spot prices. The spot price could be from external markets like Coinbase, or for centralized perps, from spot markets on their exchange:

PerpPremium = PerpPrice/SpotPrice - 1

The funding rate is like the future expiring once daily, as this premium is applied to 24 hours based on the perp premium. One can apply it to 8-hour windows or anything else, but the standard is to apply the simple premium above and divide it by the number of periods within a day.

For example, suppose you short a BTC perpetual future trading 10% above the underlying index all day. In that case, it’s perp premium—then you will receive a total funding payment of 10% over that day to compensate for the fact that, unlike traditional futures markets, there is no expiry or settlement, as it is perpetual. This sounds reasonable, but to understand why this is not, you must first understand the theory of how funding rates work in swap markets or how basis rates work in futures markets.

The basis in futures markets acts as a funding rate in swap markets, defined as the difference between the futures and spot prices. The chart below shows the horizontal time axis moving from a current futures price to its delivery/expiry date if the black line represents the current spot price.

The basis is the difference between the futures and spot price. It can be positive or negative depending on whether the futures price is above or below the spot price. The funding rate is implicit in the amortization of the basis over time, in that, at expiration, the spot price equals the futures price, so the basis is sure to be zero at that time.

There is no basis for swap markets; a funding rate is applied daily, acting precisely like the basis in futures markets. Swap accounts trade at spot prices, facilitated by broker margin. Here the basis goes from being implicit to explicit.

LongSwapPnL  = Notional (p(t+1)/p(t) - 1 - FundingRate)

Funding rates in prime broker swap accounts are charged daily and determined independently of the spot prices, like how a bank sets interest rates. For equity swap accounts common among hedge funds, they are generally a fixed markup to the Fed Funds rate, such as adding 25 basis points when a customer borrows USD to go long and subtracting 25 basis points when a customer goes short (which lends USD to the broker).

Thus far, the similarity of swap funding rates and the futures’ basis to the perp funding rate seems plausible. Two academic articles are generally referenced when presenting perps. The first is by Gehr (1988), which describes how gold was traded at the Chinese Gold and Silver Exchange Society of Hong Kong (CGSE) in the 1980s. This was when trading was not possible around the clock, and there was no internet, so a price had little volatility outside the trading day. The CGSE was unique because its futures market was undated, i.e., perpetual. The market settled daily and held a 30-minute auction to determine the funding rate. Those long gold compare the cost of paying storage and interest on the spot vs. the funding rate; those short gold futures take delivery if they feel the funding rate is too low. This funding rate was added to the spot price to create a new closing price used in the subsequent day’s pnl.

The effect on prices and cash flows in the CGSE futures market was as follows. If the market price closed at 100.00, and the funding rate was determined to be 0.01% over the next day, the cost basis for the next day’s PNL is 100.01. If the price stayed constant at 100.00 each day, the longs would lose 0.01 because the daily pnl would be 100.00 – 100.01 on a long position, where 100.00 is the spot close, and 100.01 is the previous day’s futures close. The traded price would never be 100.01; it would just be used in the daily calculation of the trader’s pnl on the next trading day.

Nobel laureate Robert Shiller (1993) proposed a perpetual futures contract for single-family homes. Unlike a stock index or a commodity, the underlying asset, housing, is challenging to create into a futures commodity because it is not homogeneous like a commodity. Quality varies considerably by location and structure, creating a lemons problem. Shiller proposed a rental index to create a rental return proxy for a housing price index. He proposed a statistical model that correlated with real estate’s average rental return, net of depreciation. This rental return would then be paid by the short to the long.

s(t+1) = f(t+1) - f(t) + d(t+1) – r×f(t)

In the equation above, s is the daily margin change in a trader’s account;  f is the perpetual futures price, r is an interest rate adjustment, and d is determined outside the market. While this is interesting, the difficulty in generating a robust rental index for d is probably why this has never been implemented. The market was supposed to trade at a spot price that did not reflect the daily funding charge, d, only its present discounted value. However, rental income, like macroeconomic profit, is challenging to estimate via macroeconomic indicators, and most macroeconomic models work poorly out-of-sample, generating considerable uncertainty for potential traders.

Nonetheless, the estimation method implied that this funding rate would move slowly, like interest rates. There was never the suggestion that the market price reflects the spot price and the funding rate, as there is no spot price in this hypothetical, never-realized market.

The perp premium funding rate charge is like Gehr’s funding rate charge added to the market’s spot price after trading. It is also like the d(t+1) term in Shiller’s model. Thus, at 30k feet, the connection between the perp premium and the funding rate seems consistent. However, the average synthetic/spot price ratio is not determining the funding rate in either of these mechanisms, as it is for perps.

In crypto perps, the modal daily funding rate and perp premium is 0.03%, which annualizes to 11%. This is a significant funding rate compared to interest rates that have been near zero over the period where perps have existed. A 0.10% perp premium would imply a massive 36.5% funding rate paid by longs to shorts. The average transaction costs for the most liquid US equities, which are more efficient than any crypto market, are estimated at around 0.1%. This is consistent with Gemini tic data that show a 0.15% standard deviation in the price change from one trade to the next (reflecting a bid-ask bounce).

The perp premium incenting trades at any instant is below the transaction cost, given not just the fees but gas and the effective bid-ask spread, which is paid twice over a round trip. If one were frequently trading, as the price-setting arbitrageurs tend to do, extreme funding rates would be less than a round-trip in transaction costs. For example, a 50% funding rate would imply a 0.006% funding payment for a one-hour position, considerably less than their transaction costs.

Additionally, the perp premium applied to longs and shorts is based on the average perp premium in the future. Even if one could know exactly one’s perp premium at the time of trade and transaction costs were zero, it would tell the long-term traders little about what it would be in the future. If one targets positions held for a month, the current perp premium at the trade time is irrelevant.

Supposedly, with all the perp premium’s economic insignificance for motivating short- and long-term traders, we expect the market to determine the funding rate by inspiring people to buy and sell perps based on current perp/spot premium movements of 0.02%. This is why it is a farce; it is absurd.

This leads to why the perp premium is consistently positive (payments from longs to shorts) and frequently rises to 40% after crypto prices jump, as it did this week. Market makers dominate price setting, and all perp markets are effectively centralized and run by unidentified and unaccountable coalitions of insiders. They can target 0.03% or 0.13% above the current spot index. No independent auditors regulate an immutable tape of trades with objective time stamps (as the once-perceived compliance-oriented FTX demonstrated). Anything that can be gamed will be gamed, and perp markets can be gamed.

On average, market makers on standard CLOBs have net zero positions on their assets. On perp exchanges, however, the market makers are generally short because it is much easier for these insiders to hedge their short exchange positions with long positions off the exchange [exchanges have different options depending on the nature and size of other markets on their exchanges, so exceptions exist]. A short hedge would require large amounts of capital on another exchange, generating significant operational risk from regulatory attack surfaces and hackers. This allows the perp market makers a significant extra return on the capital needed for market making.

Theoretically, the perp funding rate should be insignificant, if not zero. Neither USDC nor ETH has a dividend on the blockchain. The cost of carry for USDC and ETH are identical. ETH may have an interest rate if one considers the benefits of staking, but this rate is stable and around 4%, implying a negative funding rate (i.e., longs would get paid to compensate for forgone interest). There are no supply shocks in tokens to generate option value, such as when oil tankers are full. There is nothing like a draught destroying a corn harvest that generates a convenience yield for those with corn inventories. To the extent there is hedging pressure generated by natural long or short ETH, the only natural positions are stakers and miners who are perforce long, implying a negative funding rate (they would pay traders to take their naturally long risk).

Nothing in the theory of futures basis rates or funding rates implies the large and variable funding rates we observe in perp markets. Standard efficient markets theory, the law of iterated expectations, implies current sentiment is reflected in spot prices, not forward curves. This is why funding rates are generally independent of asset prices, as with equity swap markets or auto loans.

To the extent there is a correlation between the basis and price movements on standard exchanges, it is unlike the perp markets. In crypto, perp funding rates are generally high when the price has risen, as they did this week, but generally do not go much below zero when the price has declined. This is the opposite of what occurs in commodity markets, where jumps in oil prices correspond to negative funding rates, and big declines correspond to positive funding rates.

Crypto perp funding rates are best explained by insider manipulation. When prices generate windfall profits to long perp traders, they do not mind 50% annualized funding rates the following day, which amounts to a mere 0.14% daily charge. It’s like how big winners in Vegas often give the dealer a big tip: house money. The 50% funding rate premium on perps relative to the regulated and more transparent CME in February 2021 reflects insiders taking what they can from abused customers. Market makers, generally short, receive the funding rate windfall; the game is rigged as heads-they-win big, tails-they-win-a-little.

The theory that explains the positive return/funding-rate correlation is a typical story that is clear, simple, and wrong. It makes no sense when you get into the details. Like the explanation that price increases come from 'more buyers than sellers,' the idea that long demand shows up in futures price premiums has never made sense.

The perp funding rate reflects insider manipulation of customers, a crypto-crypto cost that, if eliminated, would create a superior exchange for those wanting leverage. Some of these exchanges are under quasi-regulatory control, which would be a good issue for regulators to rectify, as it is indefensible, and there is a lot of data out there.