Risk Model Sensitivity Analysis is a quantitative technique used to determine how the output or predictions of a risk model respond to variations in its input parameters or underlying assumptions. It assesses the stability and reliability of model results.
Mechanism
The process involves systematically altering one or more key inputs to a crypto risk model—such as asset volatility, correlation coefficients between digital assets, or liquidation thresholds in DeFi protocols—while holding others constant. The system then measures and quantifies the corresponding changes in the model’s output metrics, like Value-at-Risk or expected credit losses. This is typically executed through controlled simulations.
Methodology
The strategic application of Risk Model Sensitivity Analysis is crucial for validating model robustness, identifying critical drivers of risk, and understanding the potential impact of uncertain parameters on risk assessments. It informs the calibration of models and provides insight into their limitations. This practice is essential for building resilient risk management frameworks in institutional crypto investing and options trading.
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