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Compound Finance Update #2: Risk Simulation Overview

This update serves as an overview of the various test scenarios planned for the Economic Risk Simulation Engine being developed for Compound v3. The simulations aim to demonstrate the engine's capabilities in modeling & predicting economic risks within the decentralized finance (DeFi) ecosystem, specifically focusing on extreme market conditions & their impact on the Compound protocol.

The scope of the simulations includes multiple scenarios that replicate significant market events & their effects on the Compound v3 protocol. These simulations are designed to:

  • Test the protocol's resilience to sudden & severe market changes.
  • Evaluate the effectiveness of risk management strategies under extreme conditions.
  • Identify potential vulnerabilities & areas for improvement within the protocol.

The scenarios include both historical market events & hypothetical situations to cover a wide range of potential risks.

Black Thursday Simulation

The Black Thursday Simulation aims to replicate a significant price drop for ETH, akin to the Black Thursday event, within the USDC market. The primary objective is to assess the protocol's resilience under extreme market conditions.

The simulation will focus on a dramatic 43% price decline for ETH over a period of 14 hours within the USDC market on the Ethereum blockchain. The USDC market in the Ethereum Chain has a total value locked (TVL) of approximately $1.3 billion, with a significant share of this value held in wrapped Ether (WETH), approximately $500 million.

Simulation Process

The Chainrisk Cloud platform will target the Compound V3 Price Oracle, which relies on Chainlink’s Price Feed. By altering the return values of this oracle, the simulation will mimic the dramatic price drop associated with a Black Thursday event for the ETH asset. Key steps in the simulation process include:

  1. Setting initial market conditions on the Chainrisk Cloud platform.
  2. Introducing the price drop scenario by manipulating the oracle return values.
  3. Monitoring the impact on the USDC market, particularly focusing on liquidity, collateralization, & liquidation events.


This simulation will reveal how well the Compound V3 protocol can withstand extreme market conditions & its effectiveness in mitigating risks associated with volatile asset prices. Key metrics analyzed will include:

  • Changes in market liquidity.
  • Number of liquidation events triggered.
  • Overall impact on the USDC market’s stability.

Black Thursday with Varying Parameters (Extended Black Thursday)

This simulation extends the Black Thursday scenario by systematically varying key parameters to assess their impact on the protocol's ability to manage risk. The goal is to identify the optimal configuration for enhanced resilience during extreme market volatility.

Similar to the initial Black Thursday scenario, this simulation focuses on a 43% price decline for ETH over 14 hours within the USDC market on the Ethereum blockchain. The USDC market’s total value locked (TVL) is approximately $1.3 billion, with a significant portion held in wrapped Ether (WETH) at around $500 million.

The simulation will vary the following parameters:

  • Borrow Collateral Factor
  • Liquidation Collateral Factor
  • Liquidation Factor
  • Supply Cap
  • Target Reserves
  • Store Front Price Factor
  • Interest Rate Curves

Simulation Process

  1. Establish the initial market conditions & baseline parameter values.
  2. Sequentially vary each key parameter while maintaining the Black Thursday scenario conditions.
  3. Measure & record the impact of each parameter change on market stability, liquidation events, & overall protocol resilience.


By varying key parameters like the borrow collateral factor & liquidation factor, this simulation will provide insights into the protocol’s resilience under different configurations. The goal is to identify the optimal settings that enhance risk management & stability during periods of extreme market volatility.

LST Depeg Scenario (wstETH / ETH Depeg)

This scenario simulates a severe depeg event for wstETH within the ETH Market in the Compound V3 protocol. The goal is to analyze the impact of such an event on market stability & the protocol’s ability to handle the depeg event.

The simulation will focus on a severe 20% depeg of wstETH over 14 hours within the WETH market on the Ethereum network. Compound V3’s WETH market has a total value locked (TVL) of approximately $122.9 million, with Lido Wrapped Staked ETH (wstETH) holding a significant share of around $122.1 million.

Simulation Process

  1. Set up initial market conditions reflecting the current state of the WETH market.
  2. Introduce the depeg scenario by manipulating the exchange rate between wstETH & ETH.
  3. Monitor & analyze the impact on market liquidity, collateral values, & the frequency of liquidation events.


This simulation will stress test the Compound V3 market’s resilience to a severe depeg event. It will analyze how such an event affects market stability & the protocol’s overall functionality. The key outcomes will include:

  • Changes in market liquidity & collateral values.
  • Number & severity of liquidation events.
  • Overall impact on the ETH market’s stability within Compound V3.


By simulating scenarios such as the Black Thursday event, varying key parameters, & analyzing severe depeg events, we can better understand the potential vulnerabilities & strengths of the protocol. These simulations are essential for stress-testing the system & identifying optimal configurations that enhance risk management & stability. It is important to note that these scenarios are intended for showcasing the capabilities of the Chainrisk Cloud Simulation Platform & should not be considered as definitive risk mitigation advice until further statistical rigor is applied by the Chainrisk Team. Through continuous testing & refinement, we aim to develop a more secure & resilient DeFi ecosystem.

Embracing AI in the workplace: 5 ways to overcome resistance and maximize opportunities

Forget the science-fiction scenario

where machines rule the world. In reality, we've got generative AI stepping in for something a little less ‘Black Mirror’ and a little more ‘The Office’: taking care of those mundane yet time-consuming tasks in the workplace.

So, instead of relying on sci-fi for answers, let's dive into some down-to-earth, real-world examples.

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