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b) Volatility Test In the empirical literature, most economic and financial time series and especially conditional stock market volatility has always been studied using the Autoregressive Conditional Heteroskedasticity (ARCH) and the Generalised-ARCH (GARCH) models introduced by Engle (1982) and Bollerslev (1986) respectively.
Fractionally integrated generalized autoregressive conditional heteroskedasticity, Journal of Econometrics 7 A: 3-30.
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.