Overcollateralized lending has become one of DeFi’s most enduring primitives.
Morpho alone owns approximately $7 billion in TVL, according to DeFiLlama, with distribution through Coinbase, Kraken and other front-ends. Apollo Global Management has committed to acquiring up to 9% of MORPHO’s token supply over four years, and the Ethereum Foundation has put nearly $19 million into the protocol’s vaults.
But a quantitative analysis published Sunday by Dirt Roads, a DeFi research publication authored by Luca Prosperi, has sparked a debate over whether the savers fueling that growth are being systematically undercompensated, or whether primitive lending is working exactly as it should.
Bear case: savers sell puts they don’t understand
Prosperi’s analysis adapts the Black-Cox first-pass framework – a refinement of Merton’s 1974 structural credit model – to DeFi-backed debt positions. In this context, deposit $USDC in a Morpho vault backed by $ETH collateral is equivalent to holding a risk-free bond and simultaneously selling a put option on that collateral, with the liquidation loan-to-value (LLTV) serving as the strike price.
Calibrated to $ETHWith a realized volatility of approximately 75% annualized, a jump intensity of 1.5 events per year with an average jump size of -8.3%, and an LLTV of 86% versus a starting LTV of 70%, the model shows that the appropriate credit spread ranges from 250 to 400 basis points above the risk-free rate, in this case the Fed’s Secured Overnight Financing Rate (SOFR).
Observed savings interest in flagship Morpho $USDC the markets are roughly 2-4% APY – thin margins above the SOFR, which is currently 3.65%.
Crypto investor Santiago Roel endorsed the findings, arguing that $11.7 billion in Morpho vaults is retail capital funding crypto-collateralized loans “on the assumption that it is a savings account.” No institution, he says, would accept bringing nearly risk-free interest rates to the market. He pointed to a structural shift from early DeFi — when triple-digit APYs at least offset risk — to a present where vaults with vastly different risk profiles offer the same meager returns, and savers simply choose the highest number.
“This past cycle we saw many retail savings invested in algo stablecoins that promised ‘risk-free’ returns,” Roel wrote. “These cycle vaults are in high demand, but they are mispriced due to the level of risk.”
Bull Case: It’s a repo, not a put option
The backlash came quickly from practitioners with deep involvement in the game, challenging not only the model’s input but also its fundamental analogy.
The ADCV of Steakhouse Financial, whose company manages the main Morpho vaults through which Coinbase routes retail deposits, argues that on-chain lending is structurally closer to a repurchase agreement than a put option sale.
In a repo, one party temporarily exchanges an asset for cash with an obligation to repurchase, and, crucially, the lender retains full possession of the collateral throughout the transaction. On Morpho, the collateral is secured in smart contracts and can be atomically seized and liquidated if the value drops towards the LLTV threshold. The lender’s exposure is not limited by the theoretical option payoff on the full collateral volatility distribution, but by the limited residual risk that the liquidation mechanism will fail to restore the lender to health.
This reformulation leads to the central empirical objection of adcv: the loss-given-default (LGD) parameter. Prosperi’s model pegs the LGD at around 5%, derived from Morpho’s liquidation incentive formula. But the liquidation penalty is a cost borne by borrowers – not a loss absorbed by lenders. For liquid crypto-native collateral in primary markets, on-chain liquidation has historically resulted in virtually zero bad debt for depositors, because the overcollateralization buffer, continuous oracle monitoring, and open competition among trustees are working as intended.
Steakhouse’s own data supports this claim. During the sharp sell-off in late January and early February, when BTC fell 17% $ETH fell 26% in one week, Morpho processed approximately $238 million in liquidations. Users of Steakhouse’s vaults did not absorb any bad debt and maintained full withdrawal liquidity throughout the period.
“If you set the LGD parameter to a few basis points above 0% instead of about 5%, the model results fall right in line with observed rates of about 3-30 basis points,” adcv wrote.
Hasu, a strategy lead at Flashbots, made the same point bluntly.
“Great model, but bad data in, bad data out,” he wrote. “If you use the historically observed level of bad debt in the Morpho-prime markets, even with a large safety buffer, the result changes: now savers should demand an additional return of only 3-30 basis points, which is in line with rates observed in the wild.”
The real risk is fundamental, not market risk
MonetSupply, a contributor at Spark, offered a third perspective that broadly aligns with the curators’ position but completely redirects the risk conversation. Most of the risk in on-chain prime repos, he argued, comes not from market price appreciation risk – the variable on which Prosperi’s model focuses – but from fundamental and technical risks embedded in collateral assets and oracle mechanisms.
Most blue-chip collateral in Ethereum DeFi consists of tokenized Bitcoin (WBTC, cbBTC) or liquid staking tokens (wstETH, weETH). These issuers have a long track record, but remain subject to custodial and key management shortcomings, smart contract vulnerabilities and business continuity risks. Oracle providers introduce an additional layer of dependency. The probability of incidents on these vectors is low, MonetSupply argues, but the losses in a failure event could be as high as 100% of the exposure – a fat-sided distribution that Merton-style market risk models fail to capture.
He pointed to the most recent major DeFi loss events – the Resolv exploit and the Drift Protocol vault drain – as evidence. Both were driven by fundamental risk factors, not market volatility. “As a DeFi lender, these fundamentals are the most important risk factor, rather than jump risk,” he wrote.
MonetSupply also offered the most rigorous version of the structural premium argument, framing it through the lens of liquidity premiums and convenience returns. For traditional financial investors, prime money market funds and government bonds are the most important liquid assets, and they would never accept sub-SOFR returns. But for crypto-native actors, the relevant measure of liquidity is not speed to bank account, but speed to on-chain execution. A directional crypto fund that faces even an hour’s delay between requesting a redemption from a money market fund and receiving a transfer to its exchange account could miss a 5-10% move in a volatile asset, he argued, wiping out years of excess risk-adjusted returns versus on-chain repos.
Convenience returns – the implied return on keeping supplies at your fingertips – offers the same logic from a different angle. If actors along the chain derive a meaningful benefit from capital being immediately deployable within the crypto ecosystem, even if that benefit is not often realized, it may be entirely rational to accept risk-adjusted returns below the SOFR on prime repo.
Spark’s proprietary USDT savings vault, MonetSupply noted, has more than $700 million in available withdrawal capacity against $885 million in total deposits, far exceeding that of typical on-chain credit markets, which already offer a significant liquidity advantage over off-chain cash equivalents.
The structural benefits of DeFi
A separate thread in the debate argues that the risk-free interest rate equation itself is flawed on even simpler grounds.
Pseudonymous trader MilliΞ claims that DeFi interest has structural properties that traditional fixed income does not: compossibility that allows permissionless derivatives applications, censorship-resistant access without administrators who can “play silly games with you,” and instant withdrawals versus the 30-day redemption period typical of money market instruments.
“This may not matter to most of us First Worlders,” they wrote, “but it certainly matters to the rest of the planet.”
What both parties agree on
No one disputes that the vast majority of retail savers flocking to Morpho via the exchange do not understand the credit exposure they are taking on, and that the risk profiles of the vault vary dramatically, even if overall returns appear similar.
Likewise, no one disputes that the track record supporting the trustees’ optimistic loss assumptions is short and only tested under generally favorable circumstances; a point underlined by the Resolv exploit that spilled over fifteen Morpho vaults in March, and the collapse of Stream Finance that hit the credit markets in November 2025. Steakhouse’s own vaults avoided those losses, but other trustees’ depositors weren’t so lucky.
Prosperi’s analysis also highlights concerns outside the LGD debate. Leveraged looping strategies, such as recursive wstETH/WETH or sUSDe-loops with effective leverage of 7-10x, do not behave like credit products, but as leveraged carry trades on mean-reverting basis spreads, where a 5% depeg at 10x leverage triggers liquidation. And the growing push to onboard non-crypto-native collateral simultaneously breaks every assumption in the framework: unobservable volatility, discrete oracle monitoring, multi-week liquidation delays, and law enforcement risk.
The real test
The core of the disagreement centers on which risk measure matters: the structural exposure embedded in the position, or the platform’s empirical loss history. Prosperi and Roel argue the first; Hasu, adcv, MonetSupply and the curator ecosystem argue the latter – adding that the model is looking entirely at the wrong risk, and that rational actors may have good reasons to accept thin or even negative spreads on SOFR.
Structural models can overestimate market risk by assuming passive borrower behavior and ignoring the efficiency of liquidation mechanisms in the chain, which have performed as advertised even under tough conditions. But they may be underestimating the fundamental risks that MonetSupply identifies, which are completely outside the analysis framework. Meanwhile, empirical models can underestimate risk by extrapolating from a short, favorable sample.
As institutional allocators increase credit exposure to the chain, the issue may ultimately be solved not by models, but by the next sustained downturn, or the next fundamental failure.
“The mispricing will become visible when the market turns around,” Prosperi wrote. The trustees are betting that won’t be the case, and the vault keepers agree, at least for now.
This article was written using AI workflows. All of our stories are curated, edited, and fact-checked by a human.