The discussion initiated by Block Analitica and RiskDAO delved into the conservative nature of Aave v3 liquidation thresholds, comparing them to Compound v3 configurations. The authors explored Loan-To-Value (LTV) for different lending pairs as a time-dependent protocol parameter and provided tools for active risk management. They highlighted the significant role that DEX liquidity plays in the selection of LTV values¹.

The authors presented a formula for calculating liquidation thresholds, which takes into account market parameters and a single confidence level factor. This factor aims to represent all the implicit and explicit assumptions with a single number. The authors extended this research and applied it to Aave markets, comparing the confidence level to Compound and showing how these values changed over time¹.

BlockAnalitica conducted an analysis of the volatility of different assets, including ETH, WBTC, UNI, DAI, and LINK, using the Parkinson Volatility over a 360-day period. The results showed that lower market cap assets like UNI and LINK have greater volatility due to factors like liquidity. However, using the Parkinson Volatility reduced noise in the sample and standardized the volatility into a tighter range¹.

BlockAnalitica then revisited the LTV formula and established the LTV ranges based on the maximum and minimum confidence level factors. The post noted that the inverse relationship between the confidence level factor and LTV implies that the maximum confidence level factor will determine the lowest LTV, and vice versa¹.

BlockAnalitica compared Aave v3 Ethereum and Compound v3 markets using the LTV formula based on empirical data. The comparison showed that the c-factors are mostly aligned across the two protocols, with Aave taking a slightly more conservative approach with LINK and WETH, and a much more conservative approach with UNI. The research suggests a need for more dynamic and proactive monitoring and risk management of the protocol to improve user experience and revenue¹.

Gauntlet responded to the analysis, noting that the methodology makes several assumptions about market behavior to create an automated mechanism for constructing liquidation thresholds. They argue that this approach may not capture important market behaviors that affect the risk profile of a many-to-many lending platform like Aave, potentially creating downstream risk. They also pointed out that the proposed "confidence level factor" c, which is based on community perception, may not accurately reflect an asset's actual quality as collateral. Gauntlet also highlighted the complexities of the relationship between lending parameters and liquidation dynamics when multiple assets are introduced².

In response, BlockAnalitica thanked Gauntlet for their feedback and clarified that their post was meant to demonstrate how a quantitative approach could be used to reason about the risk level of liquidation threshold configuration. They also mentioned that they plan to present their full framework and methodology in future posts³.

In conclusion, the discussion highlighted the complexities of determining liquidation thresholds and the need for dynamic monitoring and risk management. The authors acknowledged the limitations of their methodology and promised to provide more details in future posts. The discussion also emphasized the importance of considering market behaviors and the quality of assets as collateral in determining liquidation thresholds.