The document discusses nonlinear models for volatility and correlation in financial data. It introduces the autoregressive conditional heteroscedasticity (ARCH) model and generalized ARCH (GARCH) models, which allow the variance of errors to depend on previous values. Specifically, a GARCH(1,1) model is presented where the conditional variance is a function of the lagged squared errors and lagged variance. The document also discusses testing for ARCH effects and some limitations of ARCH models that GARCH addresses.