Risk Modeling

Analyzes the security and reliability of on-chain code. This involves static analysis of the source code, historical exploit review, and monitoring for reentrancy, oracle manipulation, and access control vulnerabilities. The goal is to quantify the probability of a bug or exploit leading to a loss of funds.

Evaluates the economic design and governance parameters that dictate a protocol's stability. Key metrics include:

• Collateralization ratios and liquidation penalties
• Fee structures and tokenomics (e.g., emission schedules, governance power)
• Governance centralization and proposal execution risk
• Upgradeability mechanisms and admin key control

Measures exposure to volatile market conditions and the availability of liquidity. This includes:

• Impermanent Loss (IL) calculations for liquidity providers
• Slippage and price impact analysis on decentralized exchanges (DEXs)
• Liquidity depth across different market conditions
• Correlation risk between assets in lending pools or yield strategies

Assesses risks stemming from reliance on external protocols, oracles, and entities. This is critical in DeFi's composable "money Lego" system. It involves mapping protocol dependencies (e.g., a lending platform using a specific DEX for liquidations), evaluating oracle reliability and latency, and identifying centralized points of failure in otherwise decentralized systems.

Applies mathematical and statistical models to price risk. Common approaches include:

• Value at Risk (VaR) and Expected Shortfall (ES) for portfolio loss probabilities
• Monte Carlo simulations to model complex, multi-variable scenarios
• Option pricing models (e.g., Black-Scholes variants) for valuing protocol fees or governance rights
• Stress testing against historical and hypothetical market shocks

Leverages transparent blockchain data to model user and capital behavior. Analysts track wallet concentration, capital flow trends, whale activity, and governance participation. This data feeds into models predicting events like bank runs, governance attacks, or strategic accumulation that could destabilize a protocol.

A statistical technique that estimates the maximum potential loss of a portfolio over a specific time frame at a given confidence level (e.g., 95%). It is a cornerstone of market risk assessment.

• Example: A 24-hour, 95% VaR of $1M means there is a 5% chance the portfolio will lose more than $1M in a day.
• Widely used to set collateral requirements and liquidation thresholds in lending protocols.

Also known as Conditional Value at Risk, this metric calculates the average loss exceeding the VaR threshold. It provides a more comprehensive view of tail risk than VaR alone.

• Addresses VaR's limitation by quantifying the severity of losses in the worst-case scenarios.
• Critical for stress testing liquidity pools and assessing extreme volatility events.

The estimated likelihood that a borrower will fail to meet their debt obligations within a given horizon. A core component of credit risk modeling.

• In DeFi, PD models assess overcollateralized and undercollateralized lending.
• Factors include collateral volatility, loan-to-value (LTV) ratios, and on-chain transaction history.

Estimates the proportion of an exposure that will be lost if a default occurs, factoring in recovery processes like collateral liquidation.

• In DeFi, LGD is heavily influenced by liquidation mechanisms, oracle accuracy, and market depth for the collateral asset.
• A key input for calculating Expected Loss (EL = PD x LGD x Exposure).

A computational technique that uses random sampling to model the probability of different outcomes in a process with uncertain variables, such as asset prices or network congestion.

• Used to simulate thousands of potential future states for portfolio values or liquidation events.
• Helps model complex, non-linear risks that are difficult to solve with analytical formulas.

The process of evaluating a system's resilience under extreme but plausible adverse conditions, known as stress scenarios.

• Examples: A 50% drop in ETH price, a 300% spike in gas fees, or the failure of a major stablecoin.
• Used to identify vulnerabilities in smart contract logic, liquidity provisions, and governance mechanisms.

A statistical measure quantifying the maximum potential loss in value of a portfolio over a defined period for a given confidence interval. It is a cornerstone of traditional finance risk management adapted for DeFi.

• Example: "A 24-hour 95% VaR of $1M" means there is a 5% chance of losing more than $1M in a day.
• Used to set capital requirements and assess the risk of lending protocols and treasury management.

A simulation technique that evaluates how a protocol or portfolio performs under extreme but plausible adverse market conditions, known as stress scenarios.

• Scenarios include black swan events, flash crashes, or the correlated depegging of major stablecoins.
• Unlike VaR, it is not probabilistic but examines tail risks and system resilience under specific, severe shocks.

A key risk parameter in lending protocols that determines the maximum amount a user can borrow against their deposited collateral.

• Collateral Factor (e.g., 75% for ETH) is the borrowing limit as a percentage of collateral value.
• Loan-to-Value (LTV) is the ratio of the loan amount to the collateral value. A lower LTV or collateral factor creates a larger safety cushion against liquidation.

The automated mechanism that seizes and sells a borrower's undercollateralized assets to repay their debt, protecting lenders from losses.

• Triggered when a position's health factor falls below 1 (e.g., due to collateral value drop or debt value rise).
• Involves liquidation thresholds, bonuses for liquidators, and is a critical component of insolvency risk management in protocols like Aave and Compound.

The risk of financial loss due to bugs, vulnerabilities, or exploitable logic in the immutable code governing a DeFi protocol.

• Assessed through audits, formal verification, and bug bounty programs.
• Distinct from financial/market risk, it is a foundational layer of risk modeling that asks, "Will the code perform as intended under all conditions?"

The risk that the external data feeds (oracles) providing price information to a smart contract are inaccurate, delayed, or manipulated.

• A primary attack vector; incorrect prices can trigger faulty liquidations or allow undercollateralized borrowing.
• Risk modeling assesses oracle latency, data source diversity, and the security of the oracle network itself (e.g., Chainlink).