SMARC

Structured Market Adjusted Risk Classes

SMARC Yield Bonds

Interest rate volatility risk mitigation using debt-based derivatives.

Currently, the decentralized financial system is primarily offering variable-rate annuities. However, the ability to structure yield into fixed rates will come in the form of locked collateral with a maturity on repayments, or bonds, as well as fixed-rate yields with no maturity, or annuities. We don’t believe this to be a novel idea & we believe naturally that these types of products will come to DeFi over time. However, the types of derivatives & complexity reduction in financial planning you’ll be able to structure and implement with the existence of fixed yield in smart contracts will be mind-blowing to traditional financial markets.
Decentralized financial instruments are showcasing the power that a trustless financial industry can wield. Powerhouse projects in the DeFi space like MakerDAO, Synthetix, AAVE, Compound, Curve, and others are producing yields for users that have none of the constraints and rent-seeking of tradFi instruments by replacing bookkeepers, escrow, and various overhead with algorithms, trustless oracles, and decentralized ledgers. Different market-driven yields can be found on numerous decentralized platforms, but there is nothing out there that services & pulls together all of the different decentralized protocols & allows for a normalized risk curve and derivatives for risk mitigation. Furthermore, efficiencies across lending protocols are non-existent in the current DeFi markets. The ability to pull yield from numerous protocols and tranche them into higher and lower yield buckets is something that exists in traditional financial markets but is more efficient in decentralized financial markets, assuming an acceptable level of liquidity.
Our first structuring will not only allow DeFi users to get access to fixed yield but also pools yield from numerous protocols across the ecosystem creating a more efficient market, again, smoothing out the yield curve across the entire industry.
While we expect singular lending protocols to introduce concepts around fixed income on their platform, a major differentiation of a cross protocol-based approach to fixed income is the diversified assets & diversified platform risk. By algorithmically pooling interest generating digital assets on a number of lending platforms, we will create greater efficiencies by spreading risk & normalizing the industry risk curve. Since NEPRIV will not lend money directly off a native platform, & instead pools lending across the industry, it allows us to be platform agnostic & digital asset agnostic which in turn will allow for more complex structuring and bond rating systems downstream.
The person doing the bundling / securitization is banking on the fact that this structure will pull more funds towards the supply side of lending markets, which in turn will lower borrowing rates (a mechanic to incentivize borrowing), which will lead to more loans being taken out. More loads = higher supply interests, which means the “CLO” will make even more returns, and there will be even more incentive for investors to create new pools.
Risk and Loss Scenarios.
Pooled collateral would be deposited into lending protocols or yield generating contracts, and the yield will be bundled up into different tranches and tokenized. So, you could buy exposure to the most senior tranche and get a lower yield but have a much lower risk profile. SMARC bonds are a way to buy and sell risk on yield with all of the pricing driven purely by the market.

SCENARIO 1:

SCENARIO 2:

SMARC PHARAON Bonds

Market Price Exposure Risk Mitigation using tranched volatility derivatives.

SMARC PHARAON product
The SMARC PHARAON bonds will not be structured via traditional yield tranches but instead with various levels of market price exposure, which we will call risk scores. The idea is that every bucket or tranche of price exposure does not need to be flat across the entire risk curve, meaning the first $100 of price exposure does not need to deserve the same upside and downside volatility.
This is similar to having fractional ownership but with different risk/reward for the fractions.
How it works
For example, if the current price of 1 ETH is expected to be $1000, and moves to $900, the first tranche (the riskiest tranches) takes a higher percentage of the loss. Conversely, if the current price of 1 ETH is expected to be $1000, and moves to $1100, the first tranche (the riskiest tranches) takes a higher percentage of the gain.
How these gains and losses are measured & allocated across tranches can be done algorithmically with smart contracts. Each tranche can be traded as a unique digital asset. For example, jETH (a junior tranche of ETH price exposure), mETH (a mezzanine tranche of ETH price exposure, and sETH (a senior tranche of ETH price exposure). The tranches will exist as risk scores in which users of various risk appetites can gain price exposure to digital assets.
The SMARC PHARAON product will make way to build tranches of single asset and multi-asset pools that generate yield and where lower risk ramps get lower returns when the underlying assets rise & lower losses when they drop. However, we can build this without needing yield attached at all. The opportunity for downstream opportunities to use various risk ramps for differing collateral obligations is a logical progression these risk ramps will create.

SMARC SWAPS - one loan broken into 4 instruments

Coming soon...

SMARC Prediction Hedge - Derivatives hedging fluctuations in prediction market odds

Coming soon...

NUPRIA Point Index - Market-Driven Ratings Oracle

More details coming soon...
Leveraging the wisdom of the crowd we can create an index that works as a rating system providing an oracle mechanism that can be used by any platform in DeFi. A Moody’s for the decentralized future if you will.
The risk assessment framework used to rate the tranches could be used to determine market sentiment. Driven by the markets that form behind the tokenized tranches, the ratings that determine the tranche formation would become a ‘fear gauge’. In short, if the riskier tranches are more popular then it could offer an early sign that the underlying components are less risky. Similarly, if the usage of the lower yield, safer tranches see an increase in volume, this could be an early warning sign that a vulnerability was found and an attack is potentially imminent.