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.
