Dynamic Loan-To-Value

The Loan to Value (LTV) ratio defines the maximum amount of currency that can be borrowed with a specific collateral. It’s expressed as a percentage: at LTV=30%, for every 1 ETH worth of NFT collateral, borrowers will be able to borrow 0.30 ETH worth of the corresponding currency.

At the time the only currency available is ETH but more tokens may be added in the near future.

With the latest update to our underlying model, users can now avail themselves of loans amounting to up to 75% of the actual value of their NFTs. This enhancement is coupled with our steadfast commitment to upholding the highest standards of financial security, positioning us as a leading NFT-backed protocol in terms of advanced safety measures.

Dynamic Loan-To-Value

Unlockd's Data Science team has successfully devised a dynamic Loan-To-Value (LTV) model that effectively balances the user's risk in an optimal manner.

This model takes into account various factors, including the abundance of data available for asset valuation, the level of volatility observed in the market, and other pertinent variables that will be detailled later. Essentially, when there is a substantial volume of data for appraisal and minimal recent volatility, the LTV allowance is higher.

This is because the valuation algorithm's reliability is significantly enhanced when abundant data is accessible and uncertainties in the market are minimized. As such, the model ensures that the LTV accurately reflects the dependability of the valuation algorithm by considering these crucial factors.

As a result, it is plausible for two NFTs to possess an equivalent value based on our appraisal methodology yet have a significant difference in their LTV.

This distinction arises due to the dynamic nature of our LTV model, which incorporates factors beyond the appraisal value. Elements such as data availability, recent volatility, and other pertinent considerations contribute to the determination of the LTV offered for each specific NFT.

Therefore, even with similar appraised values, the LTV ratio may diverge based on the comprehensive assessment of these additional factors.

Adapting to Market Conditions

A prominent aspect of Unlockd's dynamic Loan-To-Value (LTV) model is its adaptability to changing market conditions. This model possesses the capability to modify the LTV in response to various factors, including market volatility, collection volatility, and an asset's volatility. By incorporating these considerations, the LTV remains current and precise, even when confronted with unforeseen fluctuations in the market.

To illustrate, let's consider a scenario where a particular NFT collection encounters a period of heightened volatility within the market. In such a case, the dynamic LTV model would automatically make adjustments to the LTVs assigned to the NFTs within that specific collection. These adjustments would aim to mitigate the increased uncertainty, resulting in reduced LTVs that align with the prevailing market conditions.

Risk Mitigation

Unlockd's dynamic Loan-To-Value (LTV) model goes beyond its primary function of determining loan collateral ratios. It also serves as a proactive safeguard, taking into account potential risks that could impact the protocol, particularly during significant liquidation events and associated selling pressures.

One of the ways the model accomplishes this is by monitoring the number of assets held in reserves and observing the activities of other lending protocols. When reserves increase, the model intelligently discourages the issuance of new collection-based loans. This strategic measure effectively limits selling pressure on the protocol.

By proactively managing the lending activities based on reserve levels and external lending protocol activities, Unlockd maintains the robustness and security of the protocol. This prudent approach minimizes the potential negative consequences that could arise from excessive selling pressure, ensuring the protocol's stability and long-term viability.

You can learn about all the aspects of this model here:

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