GARCH (Generalized Autoregressive Conditional Heteroskedasticity)

GARCH (Generalized Autoregressive Conditional Heteroskedasticity) is a statistical model used to analyze and predict the changing variability or volatility in time series data, like financial returns. It assumes that periods of high volatility tend to cluster together, followed by calmer periods. GARCH models the current volatility based on past errors and past volatility, helping investors and analysts understand and forecast risks more accurately. Essentially, it captures the idea that market unpredictability isn't constant but varies over time, enabling better risk management and decision-making.