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Autoregressive Conditional Heteroskedasticity |
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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. How to thank TFD for its existence? Tell a friend about us, add a link to this page, add the site to iGoogle, or visit webmaster's page for free fun content. |
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Bollerslev (1986) generalized the ARCH models to the GARCH models, which are capable of describing the feature of volatility clustering and other characteristics of financial time series. His ARCH models have become indispensable tools not only for researchers, but also for analysts on financial markets, who use them in asset pricing and in evaluating portfolio risk. Bollerslev extended this idea into Generalized Autoregressive Conditional Heteroskedastic (GARCH) models which give more parsimonious results than ARCH models (Bollerslev, 1986). |
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