Fractionally integrated generalized autoregressive conditional heteroskedasticity, Journal of Econometrics 7 A: 3-30.
What does ARCH stand for?
ARCH stands for Autoregressive Conditional Heteroskedasticity
This definition appears very frequently and is found in the following Acronym Finder categories:
- Science, medicine, engineering, etc.
See other definitions of ARCH
We have 64 other meanings of ARCH in our Acronym Attic
- Arches National Park (US National Park Service)
- ARgonne CHicago (ARCH Development Corp.)
- Association for Radio Controlled Helicopters
- Association of Registered Clinical Hypnotherapists
- Auto Rétro Club Herblaysien (French vintage car club)
- Automatic Remote Cassette Handler
- Architecture Doctorate (University of Hawaii)
- Archives of Microbiology
- Alberta Reined Cow Horse Association (Spruce Grove, Alberta, Canada)
- Avon Registered Care Homes Association (UK)
- Multimedia Development In Archaeology and Tourism
- Akron Roman Catholic Home Educators (Akron, OH)
- Ashland Richland Christian Home Educators (Toledo, OH)
- Atlanta Regional Council for Higher Education (Georgia; formerly Atlanta Regional Consortium for Higher Education)
- Center for Applied Research in Communication and Health (Lugano, Switerland)
Samples in periodicals archive:
Generalised Autoregressive Conditional Heteroskedasticity (GARCH) models are specifically designed to model and forecast conditional variances.
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.
We propose three alternative specifications of expected future beta based on the past information on realized beta using autoregressive, moving average, and generalized autoregressive conditional heteroskedasticity (GARCH)-in-mean models to obtain time-varying conditional betas for each stock.
There is some evidence for remaining autoregressive conditional heteroskedasticity (ARCH) effects, although marginally, in the equation for the short-term interest rate change and in the equation for the change in the output gap.
This study uses the Autoregressive Conditional Heteroskedasticity (ARCH) models and its extension, the Generalized ARCH, EGARCH and TARCH models was used to find out the presence of the stock market volatility on Indian stock market.
Similarly, Cheung and Ng (1990) analyse price changes over fifteen-minute periods for the S&P 500 index using a Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model.