RAVA V1
The Settlement Layer for Tokenized Assets
Abstract
Tokenized assets without continuous pricing do not have real time market prices. They cannot be liquidated instantly and they do not have transparent valuation curves. Their NAV is updated slowly, often monthly or quarterly, and reflects managerial smoothing. NAV cannot be used to size leverage, call margin or determine liquidation value. Traditional lenders do not use NAV for these purposes. Instead they construct an internal settlement value, which is the price they would use to settle a position during stress. This internal value governs collateral schedules, haircuts, advance rates, and margin calls. It shapes entire markets such as the overnight repo market, the private credit funding system, and bank warehouse lines.
RAVA introduces the first public and programmable settlement layer for tokenized assets. RAVA converts reported NAV, public market conditions, and private risk attestations into a dynamic settlement value that protocols can use safely. This settlement value is more conservative than NAV, reacts to market conditions, and behaves like the implied value engines used in institutional finance. RAVA provides the missing valuation infrastructure for scalable on chain credit.
1. Introduction
A large share of economic value sits inside private credit funds, private equity vehicles, real estate portfolios, infrastructure loans and similar structures. These assets do not trade on exchanges and do not exhibit continuous pricing. They cannot be liquidated in real time. They rely on appraisals, internal marks, and infrequent updates. In traditional finance this is not a problem because lenders do not rely on stated NAV. They rely on their own models that calculate what the asset is worth in settlement conditions.
Decentralized finance cannot replicate that behavior because on chain systems require deterministic, transparent, and programmable valuation rules. Without a settlement layer, lending against tokenized assets on chain becomes unsafe, fragmented and unpredictable. NAV oracles alone do not solve this because NAV does not represent liquidation value. Price oracles cannot be used because most tokenized assets without continuous pricing do not have a market price at all.
RAVA solves this problem by reconstructing the missing valuation logic. RAVA defines a transparent settlement function that converts raw NAV and verified risk information into a settlement value that protocols can use to size loans, set leverage, call margin, and manage liquidations.
2. The Problem with NAV and Why It Cannot Anchor Credit
NAV represents the accounting value of a fund. It is smoothed, backward looking, and updated on fixed schedules. It includes internal judgments and sometimes delays recognition of losses.
Three structural issues prevent NAV from supporting on chain lending.
First, NAV does not reflect exit value. Many private assets require weeks or months to sell. Their NAV reflects the present value of cash flows but not the price a lender could obtain if it had to settle the position quickly.
Second, NAV responds slowly to market stress. When credit spreads widen or refinancing conditions tighten, prices for similar public assets can move ten to twenty percent in a short window. NAV barely moves because it is not designed for immediate repricing.
Third, NAV includes appraisal smoothing and internal valuation practices. This produces artificially calm valuation paths that mask underlying volatility. On chain lending requires values that respond to real conditions, not smoothed accounting paths.
Traditional lenders solve this by ignoring NAV for risk purposes. They compute a settlement value instead. This settlement value anchors the loan.
RAVA performs this same transformation, but publicly.
3. How Traditional Finance Computes Implied Value
Private credit funds borrow at advance rates that reflect a lender's internal valuation, not the fund's stated NAV. Fund finance desks look through the portfolio and compute a settlement value based on liquidity, loan structure, borrower quality, covenants, concentrations, and market spreads. The difference between NAV and this internal value is the settlement adjustment.
Private equity NAV loans follow similar logic. Lenders model exit timelines, valuation uncertainty and sponsor behavior. They compute a value far below NAV that reflects downside scenarios.
Structured credit desks use collateral schedules and scenario models. Banks assign internal risk weights and require additional credit enhancements. They compute an implied value that determines the repo advance rate for bonds and structured notes.
Most visible is the repo market. Overnight repos are reset every morning. Their haircuts and funding rates adjust daily based on market volatility, collateral demand, credit events, liquidity conditions, monetary policy, and dealer balance sheets. The repo haircut is the lender's settlement adjustment for that day.
These systems share key traits.
The models are private. The outputs determine leverage and margin. The values adjust quickly to new information. They are the foundation of collateral markets.
RAVA brings this entire category of settlement logic into a transparent and programmable form.
4. The Core Idea of RAVA
RAVA is a public settlement layer that provides a dynamic settlement value for each supported asset. This value is computed using a transparent formula that includes public market signals and private risk inputs verified through privacy preserving attestations.
Let NAV(t) be the reported net asset value at time t. Let A(t) be the settlement adjustment. Then the settlement value is:
V_settlement(t) = NAV(t) × (1 − A(t))
The adjustment A(t) reflects liquidity delays, structural risk, credit spreads, concentration, leverage, redemption rules, appraisal lag, and market volatility. It contains both public and private components. Every term in the computation is defined publicly.
This settlement value is the number protocols use to lend, margin, and liquidate with tokenized assets.
5. RAVA Settlement Adjustment Model
The settlement adjustment is constructed from two categories.
A baseline adjustment that reflects the structural risk of the asset class. Dynamic adjustments that respond to real time signals.
Let A_base(c) be the baseline adjustment for asset class c. Let f_i(t) be normalized risk factors. Let α_i be factor weights.
Then
A(t) = A_base(c) + Σ_i α_i × f_i(t)
Risk factors include credit spread widening, rate volatility, drawdown probabilities inferred from correlated public markets, appraisal lag measures, cash flow stability, fund leverage, concentration measures, redemption risk and verified encumbrance information.
The model is transparent and versioned. All calibrations are published and every update is auditably logged.
6. Inputs to the Settlement Engine
RAVA uses three classes of inputs.
NAV reports. Public market data. Private risk attestations.
NAV enters directly but with conservative weighting. Market data includes spreads, volatility, liquidity indices, short term funding conditions and macro stress signals. Private risk attestations include encrypted proofs confirming leverage thresholds, concentration limits, lien status, redemption windows, gate structures, side letter terms and other internal conditions.
RAVA never sees raw private data. Only the verified outcome enters the model.
This preserves institutional confidentiality while enabling open settlement logic.
7. Privacy Preserving Attestations
Private credit funds and private equity funds cannot expose internal positions, concentrations or covenants. RAVA uses encrypted attestations and trusted computation to verify risk information without revealing it.
These proofs certify statements such as: Leverage below a threshold No undisclosed encumbrances Concentration within limits Redemption structure unchanged Covenant compliance
The proof returns a normalized factor. No sensitive data leaves the fund or manager.
This architecture allows RAVA to reflect real credit risk without compromising institutional confidentiality.
8. Output: Public and Deterministic Settlement Values
The final output is a settlement value V_settlement. This value is published on chain along with model metadata. Each update includes:
The computed adjustment Market factors Proof metadata Model version
The entire calculation can be reproduced by anyone. There are no discretionary overrides or hidden rules.
Protocols rely on this value to set borrowing limits, margin requirements, liquidation triggers and solvency logic.
9. Margining and Liquidation Under the Settlement Layer
Traditional credit markets call margin when implied values deteriorate. When the settlement adjustment increases, the advance rate tightens. Borrowers must provide more collateral or reduce exposure.
RAVA reproduces this behavior. If A(t) rises, LTV_max falls. If a borrower exceeds allowable leverage, margin is called automatically by the protocol. If margin is not met, settlement occurs at V_settlement.
This system aligns DeFi with how credit markets actually function.
10. Behavior Across Asset Classes
Each asset class receives a tailored baseline adjustment and sensitivity.
Private credit adjusts based on spread behavior, cash flow collection, borrower defaults and loan structure.
Private equity reflects exit timelines, valuation dispersion and market volatility.
Real estate reflects appraisal lag, refinancing conditions and local market risk.
Invoice finance reflects payer risk and concentration.
RAVA expands over time as additional classes and calibrations are added.
11. Governance and Model Evolution
The settlement model is a public good. RAVA governance controls model updates through transparent and verifiable processes. Any change must publish new definitions, weights and calibration data. All historical versions remain available for audit.
This prevents manipulation and ensures model credibility.
12. Strategic Context: Settlement Layers in Traditional Finance
Large institutions such as BNY Mellon, State Street and Clearstream act as settlement agents for collateral markets. They enforce consistent valuation, margin and collateral rules across counterparties. They maintain proprietary collateral schedules that set the settlement value for a wide range of assets.
The repo market is the most direct analogy. Overnight repos reset rates and haircuts every morning based on real conditions. This is the daily settlement mechanism that anchors trillions of dollars of secured funding.
RAVA brings this settlement infrastructure on chain, with transparency instead of opacity and shared access instead of institution specific models.
13. Why RAVA is Necessary for On Chain Tokenized Asset Credit
Without a settlement layer, each protocol invents its own approach to valuing illiquid assets. This produces fragmentation, inconsistent leverage and systemic risk. NAV oracles do not solve the problem because NAV cannot anchor credit. Price oracles cannot be used because most tokenized assets without continuous pricing have no market price.
RAVA provides the missing component. A neutral, transparent, verifiable settlement layer that every protocol can rely on.
This is what lets tokenized assets scale safely on chain.
14. Conclusion
Tokenized assets without continuous market pricing cannot be valued with real time prices and cannot be liquidated instantly. Traditional finance solves this by constructing implied values and adjusting them dynamically. DeFi requires this logic to be public, deterministic and programmable.
RAVA is the settlement layer that provides this missing function. It transforms NAV into a dynamic settlement value using transparent rules and privacy preserving inputs. This settlement layer becomes the foundation for lending, margining and liquidation across the on chain tokenized asset ecosystem.
RAVA is the valuation standard that allows private assets to function safely in open financial systems.