GARCH Processes

Generalized Autoregressive Conditional Heteroskedasticity, or GARCH, is a statistical model used to estimate and forecast the volatility of financial time series. It models current volatility as a function of past squared residuals and past volatility levels.

This makes it highly effective at capturing the phenomenon of volatility clustering, where periods of high turbulence are followed by more high turbulence. In cryptocurrency markets, GARCH processes are used to estimate the risk parameters needed for margin requirements and liquidation thresholds.

It provides a rigorous way to quantify conditional risk, which is essential for dynamic hedging strategies. By understanding the persistence of volatility, traders can adjust their position sizing to maintain a consistent risk profile.

It is a foundational quantitative tool for navigating the high-variance environment of digital assets.