⚙️Risk Parameters

The Calibration of Risk Parameters to Protect Protocol Solvency

Each asset in the FluidNFT protocol has specific values related to their risk, which influences how they are supplied and borrowed. The calibration of parameters is reviewed and optimised daily based on the real-time inference of our Risk Model.

The tables below shows summaries of the latest values.

Collateral Risk Parameters

Fixed-term loans

Fixed-term loan LTVs are dependent on duration and do not incur forced liquidations.

Collateral Tier	7 Day LTV	30 Day LTV	90 Day LTV	Grace Period	Liquidation Bonus	Reserve Factor
Blue Chips	85%	75%	70%	3 Days	10%	10%
Common	65%	55%	50%	3 Days	10%	20%
Exotic	45%	35%	30%	3 Days	10%	30%
Long Tail	25%	15%	10%	3 Days	10%	35%

Open-ended loans

Open-ended loans may be liquidated if debt rises above the Liquidation Threshold.

Collateral Tier	Loan To Value	Liquidation Threshold	Liquidation Bonus	Reserve Factor
Blue Chips	70%	85%	10%	10%
Common	50%	75%	10%	20%
Exotic	30%	65%	10%	30%
Long Tail	10%	55%	10%	35%

Currency Risk Parameters

Currency Types	Risk Rating	Reserve Factor Multiplier
Native Currencies	A+ A A-	x1
Stable Coins	A+ A A-	x1
Other Coins	B+ B B-	x1.25

Risk Parameters Change

When market conditions change, risks change, and so we continuously monitor assets integrated into the protocol to quickly adapt the risk parameters when required.

Risk Parameters Analysis

The risk parameters allow the protocol to mitigate market risks of collaterals and currencies supported by the protocol. Each borrowing is guaranteed by a collateral that may be subject to volatility. Sufficient margin and incentives are needed for the position to remain sufficiently collateralised in adverse market conditions. If the value of collateral falls below a threshold, an auction may be triggered to repay the outstanding and avoid the accumulation of bad debt.

Collateral Tiers

The collateral tiers are based on our risk quantification criterion.

Tier Name	Tier Number	Risk Rating
Blue Chip	1	A+ A A-
Common	2	B+ B B-
Exotic	3	C+ C C-
Long Tail	4	D+ D D-

Loan To Value

The Loan To Value (LTV) ratio defines the maximum amount of an asset that can be borrowed with a specific collateral. It is expressed as a percentage. For example, at LTV=75%, for every 1 ETH worth of collateral, borrowers will be able to borrow 0.75 ETH worth of the corresponding asset. Once a borrow is taken, the LTV evolves with market conditions.

For each wallet the maximum LTV is calculate as the weighted average of the LTVs of the collateral assets and their value:

$Max LTV = \frac{ \sum{Collateral_i \: in \: ETH \: \times \: LTV_i}}{Total \: Collateral \: in \: ETH \:}$

Liquidation Threshold

Liquidation Thresholds are relevant for open-ended loans only. Borrowers of fixed-term loans do not risk potential forced liquidations due to market conditions.

The liquidation threshold is a percentage at which a position is defined as undercollateralised. For example, a Liquidation Threshold of 80% means that if the value rises above 80% of the collateral, the position is undercollateralised and could be liquidated.

The delta between the Loan To Value and the Liquidation Threshold is a safety cushion for borrowers.

For each wallet the Liquidation Threshold is calculate as the weighted average of the Liquidation Thresholds of the collateral assets and their value:

$Liquidation \: Threshold= \frac{ \sum{Collateral_i \: in \: ETH \: \times \: Liquidation \: Threshold_i}}{Total \: Collateral \: in \: ETH \:}$

Liquidation Bonus

Bonus on the price of the collateral when a bidder purchases it as part of a liquidation auction of a defaulted borrow position; this is one that has passed threshold (or passed its maturity).

Health Factor

Similar to Liquidation Thresholds, Health Factors are relevant for open-ended loans only.

For each borrow, these risk parameters enable the calculation of the health factor: $H_f= \frac{ \sum{Collateral_i \: in \: ETH \: \times \: Liquidation \: Threshold_i}}{Total \: Collateral \: in \: ETH \:}$

When$H_f < 1$ the position may be liquidated via a liquidation auction to protect the reserve against bad debt.

Reserve Factor

The reserve factor allocates a share of the protocol's interests. This is to sustain protocol activities and pay protocol contributors. These include creators of listed collaterals who receive half of all protocol fees in the form of royalty payments.

From Risks to Risk Parameters

Market risks have the most direct impact on the risk parameters:

Liquidity

Liquidity is based on the volume in the markets, which is key for the liquidation auction process. This can be mitigated through liquidation parameters: the lower the liquidity, the higher the incentives.

Volatility

Volatility of price can negatively affect the collateral which safeguards the solvency of the protocol and must cover the liabilities. The risk of the collateral falling below the borrowed amounts is mitigated through the level of coverage required, the Loan To Value. It also affects the liquidation auction process as the margin for liquidators needs to allow for profit.

Market Capitalisation

Market capitalisation represents the size of the market, which is important when it comes to liquidating collateral. This can be mitigated through the liquidation parameters: the smaller the market cap, the higher the incentives.

Overall Risk

The overall risk rating is used to calibrate the Reserve Factor with factors ranging from 10% for the less risky assets to 35% for the riskiest.

Liquidity Risk

Introduction

FluidNFT is a liquidity protocol that enables borrowers to use their NFTs as collateral for a loan of digital currencies, and lenders to earn yield by depositing their currencies in lending pools.

Lenders receive fTokens, in exchange for digital currency deposits, and exchange these fTokens for the underlying currency to recover their initial deposit plus any accrued yield.

If at any point in time, there is insufficient liquidity within the protocol to redeem fTokens for the underlying currency asset this would affect business-as-usual operations, and lenders would have to wait for loans to be repaid before being able to retrieve their capital.

Borrow Interest Rate

Liquidity Risk is mitigated through the borrow interest rate model

FluidNFT's interest rate model is calibrated to manage liquidity risk and optimise utilisation. The borrow interest rates come from the Utilisation Rate $U$:

$U = TotalBorrows / TotalLiquidity$

$U$ is an indicator of the availability of capital in the pool. The interest rate model is used to manage liquidity risk through user incentivises to support liquidity:

• When capital is available: low interest rates to encourage loans.

• When capital is scarce: high interest rates to encourage repayments of loans and additional deposits.

Interest Rate Model

Liquidity risk materialises when utilisation is high, its becomes more problematic as $U$ gets closer to 100%. To tailor the model to this constraint, the interest rate curve is split in two parts around an optimal utilisation rate $U_{optimal}$. Before $U_{optimal}$the slope is small, after it starts rising sharply.

The interest rate $R_t$ ​follows the model: ​ $if \hspace{1mm} U < U_{optimal}: \hspace{1cm} R_t = R_0 + \frac{U_t}{U_{optimal}} R_{slope1}$

$​ if \hspace{1mm} U \geq U_{optimal}: \hspace{1cm} R_t = R_0 + R_{slope1} + \frac{U_t-U_{optimal}}{1-U_{optimal}}R_{slope2}$

​In the borrow rate technical implementation, the calculateCompoundInterest method relies on an approximation that mostly affects high interest rates. The resulting actual borrow rate is:

$Actual APY = (1+Theoretical APY/secs per year)^{secsperyear}-1$

• Open-Ended Loans use variable interest rate models

• Fixed-Term Loans use stable interest rate models

Both the variable and stable interest models, are derived from the formula above with different parameters for each asset.

• When $U < U_{optimal}$ the borrow interest rates increase slowly with utilisation
• When $U \geq U_{optimal}$ the borrow interest rates increase sharply with utilisation to above 100% APY if the liquidity is fully utilised.

Variable loans see their rate constantly evolving with utilisation. This means they are not ideal for financial planning.

Hence stable loans, that maintain their interest rate at issuance and only change on rebalancing.

Model Parameters

Interest rate parameters have been calibrated per Collateral Tier. More volatile assets, such as the Tail End, require a low Optimal Utilisation rate typically calibrated around 45%.

It is also key to consider market conditions: how can the borrowed asset be used in the current market? FluidNFT's borrowing costs must be aligned with market yield opportunities or there would be a rate arbitrage with rational users being incentivised to borrow all the liquidity on FluidNFT to take advantage of higher yield opportunities.

When market conditions change, the interest rate parameters can be adapted. These changes must adapt to utilisation on FluidNFT's market as well as incentives across DeFi.