Frax Finance

Summary

Development of Liquidity Risk Profile Dashboard for Frax Finance This dashboard includes:

• 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 Ongoing Cost: $1,500 USD per month (Credmark fees)

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.

The analysis and on-chain risk modeling will be implemented with Credmark protocol. Credmark is a full stack Defi financial modeling platform that runs natively in Python which allows us to move beyond simple analytics and develop sophisticated forward looking financial risk models. We run our own database of indexed Ethereum data, python based dev-tooling, and cloud infrastructure for building and executing complex financial models. This enables DAOs and financial institutions to use comprehensive and accurate on-chain financial metrics.

The Credmark team has published numerous research reports including a detailed post-mortem of the Terra ecosystem collapse Additionally, Debt DAO leverages Credmark’s technology and expertise in order to evaluate counterparties cash flow and treasury in order to determine appropriate loan terms. One such loan proposal was recently passed by the Frax community via governance vote.

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 an example Frax analytics and trading dashboard recently completed with Credmark: Example (Frax/FXS/FPIS distribution and inflows/outflows).

Functional Collateralization Risk Dashboard Metrics

The Frax Collateralization Metrics uses Credmark on-chain data and risk modeling framework. The dashboard below 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 also 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. As below the 90day Horizon stress test fails because the VaR of -88% is greater than the max Safe Price drop of -76.6%.

As a comparison and validation, the collateralization metrics and VaR models as of Apr 20, 2022 (before the Terra UST collapse) are detailed below to determine how the risk exposures have changed.

As can be seen from the collateralization metrics comparing July to April, there was a significant deterioration in the Overall Frax Collateralization from 175% to 124%, and the Frax treasury as well which was dominated by FXS. The other change is the decrease in Max % Safe Price drop of FXS from 85% to 76%, and the massive increase in VaR in July driven by the 85% price drop of FXS from April into May. In response the collateral ratio was also increased.

In summary overall risk exposures have increased, although Frax is still in a very healthy risk position with many levers to manage risk.

Another potential area of further risk analysis is the high Beta of FXS relative to the Frax market cap (overall liability) and the high percentage of Frax treasury dominated in FXS. The overall Frax market capitalization declined from $2.6b to $1.4b which is almost a 50% drop, yet the FXS price declined from $35 to $6 which is about an 80% drop, indicating a Beta of around 1.6.

As part of a contingency funding plan framework for market stress scenarios that might be needed to cover the liability, diversifying some portion of treasury into other assets that are not correlated to the Frax liability (market cap) would strengthen the overall risk profile of Frax if it’s ever needed to intervene during times of market stress.

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 Ongoing Cost: $1,500 USD per month (Credmark fees)

Asset Liability Mismatch Dashboard

• Summary of Assets vs Liability exposures & leverage ratios
• Long Term Funding Ratio (LTFR) (Contractual maturity mismatch)
• Term structure of funding assets and liabilities & duration gaps
• Average asset duration, average liability tenor
• Surplus funding capacity

Collateralization Risk Dashboard

• Collateralization measures
• VaR models
• Counterparty exposures and measures
• AMO risk exposures
• De-Centralization ratio
• Pool exposures and trading activity

Scenario Analysis & Stress Test Dashboard

• Identification of several stress test scenarios and modeling their impact on risk measures and exposures
• Ability to tweak and customize the scenarios and run user defined stress testing
• De-pegging scenario analysis impact across all aggregated Frax Liquidity pools
• Analogous UST like de-degging scenario
• Individual & aggregate Liquidity pool stress testing
• Funding withdrawal redemptions
• Tolerance Stress tests - Identification of limits that would de-peg Frax and how far away those are from current market conditions.
• In all of these scenarios, quantification of how all the Liquidity, ALM and other risk metrics change

All the tooling will be persistent, open source, and adjustable by the Frax community.

*** For more details you can check Frax Governance Forum . . .