Model Recalibration

Model recalibration is the periodic process of updating a model's parameters to reflect the latest market data and structural changes. Financial markets, particularly crypto, are dynamic, and a model that worked yesterday may be obsolete today.

Recalibration ensures that the model remains aligned with current volatility levels, liquidity conditions, and market behavior. This involves re-running the estimation process with the most recent data and adjusting coefficients to maintain predictive accuracy.

Failure to recalibrate can lead to significant drift in model performance, increasing the risk of losses. It is a necessary maintenance task for any professional quantitative desk.