GARCH Modeling in Crypto

Generalized Autoregressive Conditional Heteroskedasticity, or GARCH, is a statistical model used to estimate volatility clustering in financial time series. It assumes that current volatility is dependent on past volatility and past shocks to the market.

In the crypto market, GARCH is frequently used to quantify the risk of sudden price spikes or crashes that often follow periods of relative stability. By accounting for the fact that large price changes tend to be followed by more large changes, it provides a structured way to forecast risk.

While it is a foundational tool, its reliance on historical data means it sometimes struggles with the extreme regime shifts common in digital assets. Traders use these models to adjust their margin requirements and to set parameters for automated trading strategies.

It serves as a benchmark for comparing more complex machine learning approaches.