Proposal
We propose to develop DeFi risk models and build summary risk dashboards quantifying Frax risk exposures and measures. While there are a few dashboards on activity and summary analytics of Frax, there are currently no risk models or risk frameworks that exist for any stablecoin, anywhere. These models and frameworks we propose to create will quantify all the various risk exposures so they can be properly identified, managed, and mitigated and open source to give further validation and confidence in Frax.
Overview
We will develop comprehensive beginning to end construction, tooling, and ongoing risk modeling for the Frax ecosystem, setting the standard for other stablecoins, and drastically reducing emerging regulatory risk exposure.
The skills and backgrounds necessary to build proper DeFi risk models and risk management framework requires the intersection of many fields including: financial risk modeling, on-chain analytics, technology development, treasury function including asset liability duration management, economic capital modeling and capital risk bucketing, liquidity risk identification and modeling, scenario analysis and stress testing, investment and collateral risk, monte carlo simulation, and sentiment analysis. The team has extensive experience in all of these areas and is best in class in the intersection of financial risk management and blockchain technology development.
Here is a first phase implementation of the Liquidity Risk Dashboard. This dashboard operates purely against api.frax.finance and eth-mainnet.alchemyapi.io and can be hosted from Docker containers running anywhere at the discretion of Frax governance.
Functional Collateralization Risk Dashboard Metrics
The Frax dashboard summarizes the collateralization metrics and runs a VaR model which estimates the probability of Frax being able to fully meet its liabilities at any point in time for any forward looking horizon.
The initial implementation uses simple stress tests which quantify if there is less than a 1% chance of Frax able to fully meet its liability over a 1 and 3 month time horizons assuming no changes in liability or collateral ratio (which are big assumptions) but can be refined by incorporating a scenario analysis framework in the future. Some of the key risk measures are the overall Frax Collateralization, the Max % Safe Price drop of FXS, and the VaR stress tests which quantify a 99% confidence for the maximum likely price drop of FXS to meet all Frax liabilities over a set time horizon. For these measures, FXS market cap would cover the non-collateralized portion of Frax as part of Overall Frax collateralization.
This sort of risk modeling framework can help guide decisions for the protocol and manage and monitor risk exposures. As an example, it could help determine the Required Collateral Ratio by setting risk tolerance limits and having a data driven approach to strategic decisions and managing overall risk exposures depending on market conditions and risk premia. This is especially important in Crypto given the incredibly high volatility, and the recent blowups of other stable coin chains and protocols which were not implementing proper risk modeling and management. The urgency of implementing sound risk modeling frameworks across DeFi protocols is critical.
This is only a proof of concept dashboard just for collateralization. Our proposed risk dashboards would include many more risk models and measures.
Proposed Engagement Activities and Services
We propose to develop custom risk models and summary risk dashboards for Frax. These will quantify and model various risk exposures to allow for management and risk mitigation.
A summary of the risk dashboards and example metrics along with detailed descriptions follows. These measures are quantified as point in time with customizable lookbacks (1hr, 1day, 1w, 1mo, 1q etc.) where appropriate, and will provide time series charting for the metrics. All the reports and dashboards will be designed to be persistent with little to no ongoing maintenance and allow user customization and risk reporting. Additional requested risk measures can be added interactively as requested.
Detailed Risk Dashboards & Example Risk Metrics
Liquidity Risk Profile Dashboard
- LCR (Liquidity Coverage Ratio) 1day, 1w, 1mo etc.
- LVaR (Liquidity-adjusted VaR)
- Quick and Current Ratios, capital position and collateral ratios
- Structural liquidity metrics and stickiness (Tenor and behavior of collateral investors and redemption activity)
- Liquidity exposure on Ethereum and Fantom by protocol (covering 85+% of the existing liquidity, future upgrades could include other chains and protocols)
- Exposures across all liquidity pools by crypto asset such as USDT, USDC, DAI, and ETH
- Structural correlation analysis between liquidity pools
- Develop contingency funding plan and measures under various pre-identified liquidity stress scenarios
- Scenario Analysis: Withdrawal of deposits, loss of exchange liquidity and volumes
Development Cost: $80,000 USDC
All the tooling will be persistent, open source, and adjustable by the Frax community.
Team Background and Qualifications
David Fake, Chief Data Scientist
Experience
- Data Scientist in Quantitative Finance, 14+ years in Investment Management
- Masters of Financial Mathematics, University of Minnesota
- Three bachelor degrees in Math, Physics & Statistics, University of Minnesota
- Certified FRM (Financial Risk Manager)
- Passed Level II of the CFA Program
- Adjunct Lecturer in Experiential Learning Class for Master of Finance program at Carlson School of Management
Work History
- Chief Data Scientist - Irulast
- Data Scientist Advanced Analytics, Global Research: $510 billion AUM - Columbia Threadneedle Investments
- Director Investment Risk $250 billion of Fixed Income Portfolios - Columbia Threadneedle Investments
- Equity Quantitative Portfolio Analyst - $10b Global High Dividend Yield Equity Strategy - Columbia Threadneedle Investments
- Quantitative Analyst - RBC Global Asset Management
- Quantitative Analyst - Galliard Capital Management
Grant Cermak, Senior Blockchain Developer
Experience
- 20 years experience developing software and leading blockchain based technology companies
- 7 years experience developing smart contracts on Chia, Ethereum, Terra, and EOS
- Bachelor of Science, Computer Science; Mathematics Minor, University of Minnesota
- Graduate Studies in Artificial Intelligence and Computer Security, University of Minnesota
Work History
- CTO, Otomo.ai
- CSO, Exodus
- CISO, Nucleus Health
Adam J. Weigold, Senior Blockchain Developer
Experience
- 20 years experience developing software and leading blockchain based technology companies
- 8 years experience using blockchain technology, DeFi, and smart contracts on Ethereum, Bitcoin, Chia, and Terra
- Extensive experience owning and maintaining open source software
Work History
- CTO, Irulast
- Platform Architect, Watermark Insights
- VP Engineering & Chief Architect, TrustedChoice
- Senior Software Engineer, Vital Images