Autoregressive Conditional Heteroskedasticity


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Related to Autoregressive Conditional Heteroskedasticity: GARCH

Autoregressive Conditional Heteroskedasticity (ARCH)

A nonlinear stochastic process, where the variance is time-varying, and a function of the past variance. ARCH processes have frequency distributions which have high peaks at the mean and fat-tails, much like fractal distributions. The ARCH model was invented by Robert Engle. The Generalized ARCH (GARCH) model is the most widely used and was pioneered by Tim Bollerslev. See: Fractal Distributions.

Autoregressive Conditional Heteroskedasticity

A statistical measure of the average error between a best fit line and actual data that uses past data to predict future performance. General Autoaggressive Conditional Heteroskedasticity is the most common way of doing this. See also: Fractal Distribution.
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A Generalized Autoregressive Conditional Heteroskedasticity Model of the Impact of Macroeconomic Factors on Stock Returns: Empirical Evidence from the Nigerian Stock Market.
The empirical evidence of diagnostic checks shows no indication of serial correlation, autoregressive conditional heteroskedasticity, white heteroskedasticity and functional form of short run model is well specified.
Value at Risk-Exponential Generalized Autoregressive Conditional Heteroskedasticity (VAR-EGARCH) methodology) to examine the lead-lag relationship between stock and futures markets of France, Germany, and the UK, and confirm that futures markets lead spot markets.
Bollerslev's Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model (1986) is our choice of methodology in the present study.
In other words, this suggests the presence of autoregressive conditional heteroskedasticity, i.
Second, to measure the effects of both expected and unexpected inflation and inflation uncertainty, we employ generalized autoregressive conditional heteroskedasticity (GARCH)-type models to obtain expected and unexpected components of inflation and conditional variance as a proxy for inflation uncertainty.
Finally, we jointly model the impact of the expiration of these contracts on the returns to the market index and the volatility of these returns, using generalised autoregressive conditional heteroskedasticity (GARCH) models.
They documented a positive conditional volatility--volume relationship in models with Gaussian errors and Generalized Autoregressive Conditional Heteroskedasticity (GARCH)-type volatility specifications.
Following an innovation in the inflation rate, short periods of increased volatility are indicated by the presence of autoregressive conditional heteroskedasticity (ARCH) in the regression residuals.