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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AS another test of robustness, we use generalized autoregressive conditional heteroskedasticity (GARCH) and exponential generalized autoregressive conditional heteroskedasticity (EGARCH) models.
Engle, R., 'Autoregressive conditional heteroskedasticity with estimates of United Kingdom inflation', Econometrica, 50, 1982, pp.
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.
Longstaff and Schwartz (1992) present a two-factor general equilibrium model of the term structure.(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).
It is further assumed that disturbances from equation (6) are not autocorrelated and that equation (4) is modeled as a first-order ARCH (autoregressive conditional heteroskedasticity) process.(5)
The diagnostic tests have also been conducted to test the problem of normality, serial correlation, autoregressive conditional heteroskedasticity, white heteroskedasticity and specification of the ARDL bound testing model.
A convenient framework for dealing with time-dependent volatility in financial markets concerns the autoregressive conditional heteroskedasticity (ARCH) model, proposed by Engle (1982), becoming a popular tool for volatility modeling.
Generalized autoregressive conditional heteroskedasticity models (GARCH) are quite popular all over the world.
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.
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.
Fortunately, prior research indicates that Generalized Autoregressive Conditional Heteroskedasticity (Engel, 1982) model of the first order, i.e., GARCH (1,1) is able to explain away the latent stochastic nonlinearity in a wide range of financial time-series (e.g., Brock et al., 1991; Errunza et al., 1994; Hsieh 1993, 1995; Sewell et al., 1996).
This paper, thus, studies extremal phenomena involving financial assets, such as the market linkage effects of the volatility of exchange rates returns, spillover and correlations, by using a multivariate generalized autoregressive conditional heteroskedasticity method (GARCH), recently introduced in the economic literature to estimate dynamic conditional correlations.