Behind the Scenes: Collateral Parameterization

This week we wanted to share with you the latest insight into the RWG and our efforts in optimizing and securing FiRM. Our team has an ongoing directive to analyze deployed market parameters within FiRM, and ensure that any changes in collateral risk profile and liquidity environments are managed appropriately. To that end, we’d like to present… “Behind the Scenes : Collateral Parameterization

The RWG has created detailed spreadsheets (v1.0) for each collateral type on FiRM that intricately map out how various market parameters interact with one another. This tool allows us to simulate and analyze the interplay between different parameters, such as Supply Ceiling, Collateral Factor, Liquidation Factor, and others. The interaction between these is complex and often non-linear. By doing so, we can assess the current settings’ effectiveness in maintaining a healthy ecosystem for both borrowers and liquidators, and fine-tune the parameters for optimal performance when necessary.

A key aspect of our analysis is the focus on liquidation dynamics. We have examined the potential for liquidation cascades under various scenarios, making a conservative assumption that on-chain liquidity represents the total liquidity available for each asset. We also assume that the entirety of the market’s available liquidity (i.e. supply ceiling) is in the hands of a single borrower. While we recognize that this is not always the case, this approach allows us to model scenarios under the most stringent conditions, thereby ensuring robustness in our risk assessments and recommendations.

Below are screenshots of one such model (dated February 23rd) built for our upcoming wBTC FiRM market. With this we hope to offer a transparent view of our methodologies and the rationale behind our parameter settings.

  • We start with the proposing parameter settings for the wBTC market (blue cells) and a user with health factor 1.0 that has borrowed the entirety of the market’s liquidity. Next we assume this user is liquidated. To know whether a profitable liquidation can occur requires us to simulate the ensuing price impact of said wBTC sell order. A linear regression is run with data collected for wBTC sell orders ranging between $500,000 and $20,000,000 (S/O AWG) and fit to this data point. Finally, we can determine that, given the proposed parameter settings and simulated price impact, a profitable liquidation can indeed occur, and the borrower’s new position post-liquidation is healthy. For more on the interaction between different parameter settings for markets on FiRM: Balancing Act: An Insight into FiRM Market Parameter Setting.

  • To obtain the optimal liquidation factor for the wBTC market, we first assume gas price ($), ETH price (GWEI), and minimum debt ($) to be constant. Then, we make use of Tenderly to simulate total gas spent by a liquidator, split between three steps (ETH->DOLA, Liquidation, wBTC-> ETH). Finally, we convert this amount to $, factor in the liquidation incentive for the market, and utilize all the obtained variables and plug them in the formula to determine liquidation factor. For more on the methodology employed to determine liquidation factor and minimum debt amounts per market: FiRM’s New Guard: Minimum Debt Amounts.

Key Findings:

  • Favorability for Liquidators: Our models indicate that the current settings are generally favorable for liquidators. This is crucial for the health of the protocol, as active liquidator participation is essential for mitigating risk of the protocol incurring bad debt and maintaining market stability.

  • Liquidation Cascade Potential: The analysis suggests that even under certain extreme conditions, liquidation cascades are not immediately possible. However, these scenarios are highly contingent on specific market behaviors and external factors.

The RWG is committed to continuous monitoring and analysis. We understand that the DeFi landscape, namely liquidity (more on real-time liquidity alerts from AWG: Inverse.Watch: A Game-Changer for the DAO), is ever-evolving, and our models and strategies must adapt accordingly. We welcome feedback and discussions from the community on our findings and methodologies. Your insights are invaluable in helping us refine our approach and ensure the long-term success and security of FiRM.