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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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It has proved very effective for forecasting the volatility of returns over short horizons, and often outperforms the forecasts of more sophisticated models, such as generalized autoregressive conditional heteroscedasticity (GARCH) when the underlying asset returns are normally distributed. 19 Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation |
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