Research Summary
The report provides an update on the Arcadia Collateral Risk Simulation project, focusing on the empirical borrower model and its implications for the simulation’s realism. It outlines the progress made, the goals for the next week, and a preview of the next update. The report also presents an analysis of borrower behavior in digital asset lending markets, which informs the simulation models.
Key Takeaways
Empirical Borrower Model
- Understanding Borrower Behavior: The report presents an empirical borrower model that provides insights into borrower behavior in digital asset lending markets. This model informs the simulation’s key assumptions and parameters, creating a more realistic simulation.
Progress and Goals
- Progress Made: The team has made significant strides in expanding the simulation model to include a broader array of single assets and LP tokens. They have also developed detailed borrower models based on retrieved borrowing data and have partially carried out validation and verification testing.
- Goals for the Next Week: The team aims to finalize all remaining tasks from the previous week, refine and optimize the simulation’s parameters, begin coding the market impact simulations, and generate the first set of outputs from the simulations.
Methodologies for Borrower Behavior Analysis
- Method 1 – Total Borrow vs Collateralization Ratio: This method involves a comparative analysis of total borrow amounts versus collateralization ratios for assets akin to collateral and numeraire token pairs at launch. It offers insight into lending behaviors of the reference protocol and acts as a proxy for Arcadia’s future user base.
- Method 2 – Empirical Distributions of Collateral for a Given Borrow Position: This method applies a filter to identify borrow positions that involve a specific numeraire collateral asset pair, anticipated with the launch of Arcadia. It examines the patterns and preferences related to collateral among borrowers across various borrowing tiers within the lending platform.
Next Week’s Post
- Upcoming Simulation Results: The next post will present the first results of the simulation conducted on empirical borrower data, providing an analysis of the simulation’s output.
Actionable Insights
- Refining Borrower Models: The development of detailed borrower models based on retrieved borrowing data can help in accurately simulating a variety of risk profiles, offering a more nuanced understanding of borrower behaviors.
- Expanding Simulation Coverage: Expanding the simulation model to include a broader array of single assets and LP tokens can provide a more comprehensive understanding of the digital asset lending market.
- Improving Statistical Modeling: The development of statistical models to predict the asset holdings of generic accounts and to determine market scenarios for simulation can enhance the accuracy and reliability of the simulation.
