Model Calibration Stability

Model calibration stability refers to the consistency of model parameters over time when fitted to market data. A stable model should produce consistent parameters that do not fluctuate wildly from day to day.

If parameters are unstable, it suggests that the model is over-fitting the data or that the underlying assumptions are incorrect. In crypto, where market regimes shift rapidly, maintaining calibration stability is a significant challenge.

Analysts must use robust estimation techniques and carefully select the data used for calibration. Stable calibration is essential for reliable pricing and risk management, as it ensures that the model remains valid under changing conditions.

It is a key metric for assessing the health of a quantitative trading system. It is vital for long-term model performance.