GARCH Parameter Estimation

GARCH parameter estimation involves using statistical techniques to find the best-fit values for the coefficients in a GARCH model based on observed market data. These parameters define how much weight is given to past shocks and past variance in predicting future volatility.

The estimation is typically performed using Maximum Likelihood Estimation, which finds the parameter values that maximize the probability of observing the historical data given the model. Accurate parameter estimation is critical because incorrect values can lead to poor volatility forecasts and mispriced derivatives.

In the context of crypto, where data can be noisy and subject to structural breaks, estimation techniques must be robust to outliers and non-normal return distributions. Proper estimation ensures the model captures the specific dynamics of the asset being analyzed.