This is termed autoregressive conditional heteroskedasticity.

Autoregressive Conditional Heteroskedasticity (ARCH) effect test.

In econometrics, AutoRegressive Conditional Heteroskedasticity (ARCH) models are used to characterize and model observed time series.

The Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model is another popular model for estimating stochastic volatility.

See also autoregressive conditional heteroskedasticity (ARCH) models and autoregressive integrated moving average (ARIMA) models.

More general models include autoregressive conditional heteroskedasticity (ARCH) models and generalized ARCH (GARCH) models.

Examples of these are autoregressive moving average models and related ones such as autoregressive conditional heteroskedasticity (ARCH) and GARCH models for the modelling of heteroskedasticity.

A fellow of the Econometric Society, Bollerslev is known for his ideas for measuring and forecasting financial market volatility and for the GARCH (generalized autoregressive conditional heteroskedasticity) model.

Engle's LM test for autoregressive conditional heteroskedasticity (ARCH), a test for time-dependent volatility, the Breusch-Godfrey test, and Durbin's alternative test for serial correlation are also available.

Engle developed new statistical models of volatility that captured the tendency of stock prices and other financial variables to move between high volatility and low volatility periods ("Autoregressive Conditional Heteroskedasticity: ARCH").

Following the pioneering work of N.Y.U.'s Robert Engle during the nineteen-eighties, a new econometric technique was invented to deal with this problem: GARCH (if you must ask, the acronym stands for Generalized Autoregressive Conditional Heteroskedasticity).

Vetenskapsakademien (The Royal Swedish Academy of Sciences) (2003), Time-series econometrics: Cointegration and autoregressive conditional heteroskedasticity, Advanced information on the Bank of Sweden Prize in Economic Sciences in Memory of Alfred Nobel.

These models represent autoregressive conditional heteroskedasticity (ARCH) and the collection comprises a wide variety of representation (GARCH, TARCH, EGARCH, FIGARCH, CGARCH, etc.).

Integrated Generalized Autoregressive Conditional Heteroskedasticity IGARCH is a restricted version of the GARCH model, where the persistent parameters sum up to one, and therefore there is a unit root in the GARCH process.

In recent years time series models have become more sophisticated and attempt to model conditional heteroskedasticity with models such as ARCH (autoregressive conditional heteroskedasticity) and GARCH (generalized autoregressive conditional heteroskedasticity) models frequently used for financial time series.