GARCH Models in Crypto

Generalized Autoregressive Conditional Heteroskedasticity models are statistical tools used to estimate and forecast volatility in financial time series, including cryptocurrencies. In crypto markets, volatility is not constant; it tends to cluster, meaning periods of high volatility are often followed by more high volatility, and quiet periods by quiet periods.

GARCH models capture this phenomenon by modeling the variance of the current error term as a function of past squared error terms and past variances. By understanding these volatility clusters, traders and risk managers can better price derivatives, manage portfolio risk, and set margin requirements.

These models are essential because they allow market participants to quantify the probability of extreme price movements, which are frequent in digital assets. Unlike standard models that assume constant variance, GARCH accounts for the time-varying nature of crypto risk.

This makes them foundational for building robust trading strategies that adapt to changing market regimes.