Generalized Autoregressive Conditional Heteroskedasticity (GARCH) is a statistical model used to analyze time series data, particularly in finance. It helps to understand and predict the volatility of asset returns over time. GARCH models account for changing levels of volatility, meaning that periods of high volatility can be followed by periods of low volatility, and vice versa. The GARCH model builds on the Autoregressive Conditional Heteroskedasticity (ARCH) model, which was introduced by Robert Engle in 1982. By incorporating past variances into the model, GARCH provides a more flexible approach to capturing the dynamics of volatility, making it useful for risk management and financial forecasting.