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Autoregressive Conditional Heteroskedasticity
(redirected from Autoregressive conditional heteroscedasticity)

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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.
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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.
Fat--tails and VaR estimation using power EWMA models by Lee, Hsien-Feng / Journal of Academy of Business and Economics
19 Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation
Research and Markets - Derivatives Pricing: The Classic Collection by Business Wire
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