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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Standard autoregressive conditional heteroskedasticity tests (not reported here) conducted on the standardized residuals cannot reject the null hypothesis that all heteroskedasticity effects have been removed after fitting with a GARCH(1,1) process.
Currency instability: regime switching versus volatility clustering
This approach can be considered a particular case of the generalized autoregressive conditional heteroskedasticity (GARCH) model, and according to Jorion (2001, p.
Value at risk (VaR) using volatility forecasting models: EWMA, GARCH and stochastic volatility/Valor em risco (VaR) utilizando modelos de previsao de volatilidade: EWMA, GARCH e volatilidade estocastica
When we consider the short-run dynamics of the demeaned variables through a vector autoregression (VAR) analysis, we show that the error covariance of this VAR model is significantly conditionally heteroskedastic and go on to specifically account for this phenomenon with a VAR-generalized autoregressive conditional heteroskedasticity (GARCH) model.
Twin sons of different mothers: the long and the short of the twin deficits debate
Because the homoskedasticity assumption of the traditional market-model approach may be violated, we also utilize generalized autoregressive conditional heteroskedasticity (GARCH) and exponential generalized autoregressive conditional heteroskedasticity (EGARCH) models to ensure robustness.
Market reaction to announcements to invest in ERP systems
They documented a positive conditional volatility--volume relationship in models with Gaussian errors and Generalized Autoregressive Conditional Heteroskedasticity (GARCH)-type volatility specifications.
Return, volume, and volatility analysis in Indian stock market
In regard to the latter, LJB is the Lomnicki-Jarque-Bera test of normality; ARCH denotes the statistic of no autoregressive conditional heteroskedasticity with four lags; and BCH is Ocal and Osborn's (2000) test of business cycle heteroskedasticity.
Cyclical asymmetries in unemployment rates: international evidence
Thus, to help understand certain aspects of petroleum price volatility, we utilized the generalized autoregressive conditional heteroskedasticity (GARCH) model for estimating the conditional variance of returns, which allows the conditional variance to be time-variant.
Volatility relationship between crude oil and petroleum products
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.
Persistence, Excess Volatility, and Volatility Clusters in Inflation
In this study, we examine the short-run dynamic information transmission between the Chinese A and B share markets using a Bivariate Generalized Autoregressive Conditional Heteroskedasticity (GARCH) framework, which simultaneously models the return transmission and volatility spillover across the two markets.
Short-term dynamic transmission and long-term foreign share discount: evidence from the Chinese stock markets
This study considers several residual diagnostic tests: the Jarque-Bera statistic test for normality, the Ljung-Box Q-statistic test for serial correlation, the Lagrange Multiplier statistic test for autoregressive conditional heteroskedasticity (ARCH), and the White test for heteroskedasticity (with cross terms).
The demand for cigarettes in Taiwan: domestic versus imported cigarettes
1) The discrete-time approximation of the continuous-time specification is then formulated as a generalized autoregressive conditional heteroskedasticity (GARCH) framework introduced by Bollerslev (1986).
Examining changes in reserves using stochastic interest models

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