GARCH Forecasting Models

GARCH, or Generalized Autoregressive Conditional Heteroskedasticity, is a statistical model used to estimate and forecast the volatility of financial time series. It is particularly effective because it captures the tendency of volatility to cluster, meaning large price changes are likely to be followed by more large changes.

In the context of crypto and derivatives, GARCH helps in pricing options more accurately by predicting the variance of returns. By accounting for past variance and past squared residuals, the model provides a more realistic view of risk than simple moving averages.

It is widely used by risk managers to set capital requirements and assess the probability of extreme market events. However, it requires careful parameter calibration to remain effective in volatile markets.