Most explanations stop at the reward side. This one starts with the obligation side: what you promised, to whom, and what happens when two of those promises come due in the same week.

• Restaking adds obligations, not capital. The same deposit now answers to several slashing conditions at once, and each one can act independently.
• Correlation is the real multiplier. Positions that share an operator, client software or oracle feed tend to fail together, so “diversified across ten services” is often one bet in ten costumes.
• Headline restaking yield is gross. It ignores slashing probability, unbonding queues, the cost of hedging, and the discounts that appear when everyone exits at once.
• The exit path matters more than the rate. If you cannot leave within the window you might need the money, the extra yield is compensation for illiquidity you did not intend to sell.

How restaking turns one deposit into several obligations

A restaking position is a stack of contracts, and each layer can impose its own penalty. Reading the stack from the bottom explains why the marginal yield looks attractive and why the marginal risk is larger than it appears.

The first layer: collateral already at work

The base deposit is staked to a proof-of-stake network under that network’s rules: a bonding period, a slashing condition for provable faults such as double signing, and a reward schedule set by issuance and fees. This layer is already a trade — you accept lockup and fault risk in exchange for issuance, and restaking never removes it.

The important detail is that the base layer usually has the longest unbonding period. When people talk about a restaking position being “liquid”, they are often describing a receipt token that trades at a discount precisely when the underlying queue is longest.

The second layer: operators and the services they run

On top of the base deposit, a restaking arrangement lets an operator commit the same stake to additional services — commonly called actively validated services, or AVSs. Each service defines its own duties and its own penalty schedule. Some require the operator to run specific software, some require price or state attestations, some require uptime commitments.

You are not buying ten independent bets. You are buying one operator’s operational competence, split across ten contracts that each hold a claim on the same collateral. If the operator’s infrastructure fails at 03:00, several services may record a fault from the same root cause.

Where restaking risk actually compounds

Restaking risk compounds at the points where two layers share a dependency. The table below maps the layers against what a single failure can reach.

Read the third row carefully. Operator risk is the only row where a single mistake reaches every column at once, and it is the row most often left out of yield comparisons.

Restaking does not pay you for taking more risk in the abstract. It pays you for taking a specific, named risk that the service needs someone to hold — and the fee is usually priced against the cheapest operator willing to hold it.

Working through a restaking loss

Numbers make the stacking visible. Suppose you deposit 100 units of a staking asset. The base network pays 3.0% a year. You add ten services, each paying an extra 0.2%, for a gross 5.0% — 5 units a year instead of 3.

Now a fault occurs. The service that penalises most severely specifies a 20% penalty on the stake allocated to it. Because you allocated evenly, that service holds 10 units, so the penalty is 10 × 20% = 2 units. Against 2 units of extra annual reward from that service, you have lost a decade of its contribution in a single event.

The worse case is the correlated one. Suppose the fault originates in shared operator infrastructure and three services record it. Exposed stake becomes 30 units, the penalty becomes 30 × 20% = 6 units, and the year nets 5 − 6 = −1 unit before you count the base layer’s own risk. The position has produced a negative return while every published rate on the screen stayed positive.

1. Write down the gross reward from each service you are considering, as an annual number in units of the asset.
2. Write down the maximum penalty each service can apply, and whether penalties are capped.
3. Group services by operator, client software, oracle dependency and data source.
4. Assume the largest group fails together, and sum the penalties across that group.
5. Compare that sum with the total extra reward over your intended holding period.
6. Add the exit cost: queue length, receipt-token discount, and the price impact of unwinding any hedge.
7. If the sum of penalties is larger than several years of extra reward, you are not being paid for the tail — you are donating it.

Questions that decide whether restaking is appropriate

Three questions separate a considered restaking position from a yield-chasing one. None of them appear on a leaderboard.

Who controls the operator set

If a small number of operators serve most of the stake, the network’s fault tolerance is lower than the validator count suggests. Check whether delegation is permissionless, whether operators can opt into services without depositor consent, and whether the service can add new penalty conditions after you deposit.

Consent that can be changed unilaterally after the fact is not consent. In many designs, the depositor approves a service once and inherits every future parameter change.

What the exit path costs

Exit cost has three parts: the protocol unbonding period, the service unbonding period, and the market price of any liquid receipt. In calm markets the third is close to par. In stressed markets it is not, and the discount is the market’s estimate of exactly the risk described above.

Compare the exit path with the reason you hold the asset. If the position exists to be sold during a drawdown, a two-week queue converts a hedge into a liability.

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Borrowers usually pick a number by looking at the maximum the market allows and subtracting a little. That is not a method. A workable LTV comes from the volatility of the collateral, the length of time you plan to hold the debt, and the cost of being closed out at the worst possible moment.

• The maximum is a limit, not a recommendation. It is set to protect the lender, and it assumes the collateral can be sold quickly.
• Your real buffer is the gap to liquidation, not the gap to the maximum. Those are different numbers on almost every market.
• Interest moves the ratio. An LTV that looks stable can drift upward for a year without any price change.
• Volatility sets the number. Two assets with the same maximum loan to value can deserve very different debts.

What loan to value measures

Loan to value compares debt to collateral at current prices. It is a snapshot, recalculated as either side moves, and it says nothing about your ability to repay.

The arithmetic

Divide the outstanding debt by the collateral’s market value. A 9,000 debt against 20,000 of collateral is a 45% LTV. Add 500 of accrued interest and the same collateral now supports a 47.5% ratio. Nothing about the asset changed; the debt grew.

Note what the ratio leaves out. It ignores the interest rate, the expected holding period, the liquidity of the collateral and any tax or exit cost. It is one number describing one relationship at one instant.

Why the ratio is not the whole risk

Two positions can share an LTV and carry completely different risk. One holds a large-cap asset with deep spot markets; the other holds a thin token trading a few hundred thousand dollars a day. The first closes at a predictable price. The second may gap through the liquidation level and leave bad debt for the protocol to socialise.

That asymmetry is why markets assign different parameters per asset rather than one global number. The ratio is comparable only within a single collateral type.

Why the maximum loan to value is a ceiling

The maximum exists so the lender has room to seize and sell collateral before the debt exceeds it. It is calibrated for the protocol’s protection, and it is deliberately set close to the point where the position becomes dangerous for you.

The gap between maximum and threshold

Most markets publish two numbers. The maximum loan to value caps what you can borrow at opening. The liquidation threshold is the higher ratio at which the position becomes eligible for closure. The space between them is the protocol’s margin, not yours.

Borrowing at the maximum means you start with that entire margin already consumed. A small adverse move puts you at the threshold immediately. Borrowing well below the maximum is the only way to own any of that space yourself.

What the ceiling assumes

The maximum assumes continuous pricing and available liquidity. In a fast market both weaken at once: prices gap, books thin, and the liquidation meant for the threshold lands somewhere below it.

A maximum loan to value is the point at which the protocol stops trusting the position. It is not the point at which you should stop trusting your own.

Choosing a loan to value you can live with

Start from a drawdown you consider plausible, not from a percentage someone else uses. The question is not how much you can borrow, but how much you can borrow and still be solvent after a bad month.

The drawdown test

Pick a fall in collateral price that you would not call a catastrophe — for a large-cap asset, a third is a reasonable stress case; for a smaller one, half. Then solve backwards for the debt that survives it with margin to spare.

If the collateral can lose 40% and you want to sit comfortably below the threshold afterwards, your starting loan to value should leave that much room plus a buffer for interest. On most markets that lands a volatile asset in the 30–50% range, and stablecoin collateral higher.

Correlated collateral

Diversifying collateral does not help if everything in the basket falls together. Correlated assets behave as one larger position, and the ratio computed across them looks calmer than the risk actually is. Count correlated collateral as a single exposure when you size the debt.

Running the numbers

Take 20,000 of ETH at 3,000 per unit, a maximum loan to value of 80%, a liquidation threshold of 82.5% and a 5% liquidation penalty.

Borrow 12,000 — a 60% LTV. The position reaches the threshold when collateral value falls to 12,000 divided by 0.825, or 14,545. That is a price of about 2,182, roughly 27% below the entry price. A single bad week can get there.

Borrow 9,000 — a 45% LTV. The threshold is reached at 10,909 of collateral value, a price near 1,636, or 45% below entry. The position survives a drawdown that would have closed the first one.

Borrow 6,000 — a 30% loan to value. The threshold sits near 1,091, a 64% fall. This is the version that survives a bear market rather than a correction.

Now add time. At 7% annual interest on the 9,000 debt, a year of no repayments adds 630. The threshold then arrives at 11,673 of collateral value, a price near 1,751 instead of 1,636. Interest quietly consumed about 7% of the buffer, and the effect compounds the longer the position runs.

Loan to value by collateral type

Parameters differ by venue and by asset, and the ranges below are typical rather than fixed.

A stablecoin at 85% loan to value is not safer than a volatile asset at 45%. The stablecoin has no volatility to absorb, so the only thing that matters is the peg holding — and when it does not, the buffer is already gone.

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