Autoregressive conditional heteroskedasticity (ARCH) is a statistical model for time series data that describes the variance of the current error term as a function of the sizes of previous periods' error terms. It was introduced by Robert F. Engle in 1982 in his work modeling the variance of United Kingdom inflation. ARCH models capture volatility clustering, the tendency of financial time series to alternate between periods of large swings and periods of relative calm, and they are widely used to model time-varying volatility in financial and economic data.
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In econometrics, the autoregressive conditional heteroskedasticity (ARCH) model is a statistical model for time series data that describes the variance of the current error term or innovation as a function of the actual sizes of the previous time periods' error terms; often the variance is related to the squares of the previous innovations.
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In econometrics, the autoregressive conditional heteroskedasticity (ARCH) model is a statistical model for time series data that describes the variance of the current error term or innovation as a function of the actual sizes of the previous time periods' error terms; often the variance is related to the squares of the previous innovations.
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