What is Driving the USDT/USDC Rate Spread?
An analysis of the main drivers of the USDT/USDC spread and how to trade it.
USDT and USDC, the two largest stablecoins by market capitalization, were at the center of attention in recent weeks following the bank run on Silicon Valley Bank (SVB) and the temporary depegging of USDC, after the stablecoin issuer (Circle) disclosed its $3.3bn exposure to the bank.
USDC traded below $0.90 for some time following the news, but the market was quite quick in accurately pricing the risks involved. As the information about the recovery rates for the cash held with SVB was processed, the stablecoin eventually restored its peg.
The price action in DeFi money markets was certainly also not for the fainthearted.
DeFi rates for stablecoins tend to spike when there is a demand to borrow to short. In this particular occasion — and since the maximum downside for the depegging event was quickly priced — the market started playing the repegging of USDC with an elegant implementation. That included borrowing USDT to buy USDC or DAI, which was at some point trading at the same levels as USDC, although it is only partially collateralized with USDC.
USDT traded intermittently at a decent premium above $1, while money market rates for USDT exploded with borrowing costs surpassing 50% per year. At the same time, USDC rates dipped; demand for borrowing was fairly weak as market participants were looking to derisk.
In the remainder of this article, we take a look at the historical relationship between the money market rates for the two most popular stablecoins, try to analyze their drivers, and look at how IPOR swaps allow shrewd traders to play the money market rate spread.
USDT/USDC Spread Drivers: The Intuition
The money market rates for USDT have historically traded at a premium to USDC rates.
Intuitively, the spread is a reflection of the market‘s perception of the idiosyncratic risks of the two stablecoins and the liquidity of the respective money markets in DeFi.
Interest rate curves in AAVE and Compound are designed to manage liquidity risk and optimize utilization.
The borrow interest rate is a function of the utilization rate or in simple terms, the availability of capital in a pool.
When liquidity is ample, interest rates are set lower to encourage borrowing, whereas when liquidity is scarce, rates are set higher to incentivize repayment of loans and attract more deposits.
The exclusion of USDT from eligible collateral status by the blue-chip protocols like Aave & Compound exacerbates the issue, as the utilization rate of the respective pools tends to move to levels beyond the “optimal rate”, a lot more often.
There are overall fewer lenders for USDT and therefore a much smaller supply and higher rate volatility. At the same time, USDT tends to be in high demand for yield farming, occasionally pushing rates to elevated levels.
Apart from the liquidity of USDT in DeFi protocols, the lack of transparency around Tether‘s reserve assets and the concerns about the company‘s ability to process withdrawals has led to never-ending FUD about USDT depeg risks.
Money market rates of stablecoins tend to spike when there is elevated depeg risk pricing, due to increased “borrowing to short” demand.
Fiat-backed USDC had established itself in recent years as the safest among stablecoins. Liquidity is significantly better compared to USDT in DeFi money markets, creating a virtuous cycle for the stablecoin. Idiosyncratic events aside, the USDC/USDT spread has historically been trading in line with risk sentiment.
The temporary panic about potential regulatory action against the stablecoin’s issuer led to a moderate repricing of rates last month.
The rapid depegging of USDC as the SVB drama was unfolding did not trigger further demand for shorts and money market rates stress in USDC; the downside was well-priced thanks to the transparency about its collateralization. Circle‘s decision to move its cash reserves held with SVB to BNY Mellon should also somewhat help restore confidence in the stablecoin after the depeg drama.
Spread Drivers — Does the Data Confirm Our Intuition?
We performed the following analysis on the historical time series of IPOR index data (daily changes) for USDT, USDC, and the spread between the two. The data extends back to the end of 2020.
Applying the Lasso method* as a feature selection algorithm to a pool of 15 factors** for different periods and time windows (1 month, 3 months, 6 months) we reduced the original list of factors to a smaller set that better explains the variability of the rates in question (USDC, USDT & spread) consistently across different historical periods.
(*) Lasso is effectively a modification of linear regression that introduces a penalization term for the sum of absolute values of model weights. The method can be used for feature selection as it shrinks the coefficient values of some of the predictors to zero and removes them from the model. Lasso is particularly appropriate when the target pool of timeseries for feature selection consists of similar time series exhibiting multicollinearity.
(**)The complete list of factors for our analysis: (daily returns) BTC, ETH, SOL, VIX, S&P 500, NASDAQ, $ Index, UST 10yr bond, US Money Market ETF, US HY ETF, US Fed Funds TR, US IG ETF, Gold, USDCNY
Results
The results of the feature selection exercise were overall consistent with our intuition about the main drivers of the IPOR rates and the USDT/USDC spread, as presented in the first part of this article.
The factors identified as significant were fairly unstable across experiments; the importance of factors fluctuate quite a bit between data samples and for different time windows. The IPOR Index data set for the last few years exhibits quite a few structural breaks; TradFi and DeFi rates and cross-asset correlations underwent several regime changes amidst tectonic shifts in macro since 2020. The quality of the rolling regressions performed for the selected subset of factors and different periods was also average to poor (more below).
USDC factor selection results were consistent with the view that the broader level of DeFi money market rates is mainly driven by risk sentiment.
One could also argue that the relationship with TradFi yields is also of relevance; some market participants operate on assets on both worlds and tend to adjust their DeFi credit markets allocation based on the opportunity set in TradFi. In periods, like 2022 where higher TradFi rates are the culprit for poor market sentiment, the inverse relationship between DeFi and TradFi yields is reinforced further.
USDT factor selection results were broadly in line with our original assumption that USDT is a higher beta market, more sensitive to risk sentiment. The presence of US Investment Grade (IG) bonds in the significant factors list could be related to the market’s concerns about the reserve assets that are backing USDT.
The results of the features selection exercise for the USDT — USDC spread were in line with the expectation that the spread is mainly driven by the overall market sentiment and USDT idiosyncrasies.
The plots of residuals for the rolling regressions illustrate nicely the effect of idiosyncratic risks and extreme market events (e.g. see Terra collapse & FTX fiasco in 2022, USDC depegging in 2023 in the following chart).
The quality of the regression results was average across markets and experiments. Adjusted R² of the rolling regressions was mostly in the 0.2 to 0.6 range.
What is the Тrade?
Tactical Trades
IPOR swaps facilitate the implementation of tactical trades playing the compression or decompression of the spread while neutralizing the exposure to the overall level of DeFi money market rates.
The decompression trade (pay fixed USDT vs receive fixed USDC) can be an effective way to position for a temporary deterioration of risk sentiment, while compression (receive fixed USDT vs pay fixed USDC) is a nice way to fade risk-off, idiosyncratic events, or liquidity-induced spikes in USDT. The cross-market spread trade is expected to remain in vogue, as long as the environment of regulatory and macro uncertainty persists.
Medium-term / Thematic / “Carry” Trades
The spread can also be an expression of longer-term thematic plays. If, for example, USDT were to be accepted as collateral in Aave or Compound, we could expect a sustained compression of the spread compared to historical levels.
The introduction of IPOR swaps with longer maturities in the coming months will enable interesting yield enhancement strategies; traders will be able to gradually build a position by accumulating spread compression trades at attractive levels.
Thanks for reading!
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