EGARCH: Exponential Generalized Autoregressive Conditional Heteroskedasticity
The aim of the research is: after analyzing stock price volatility factors and specifics of generalized autoregressive conditional heteroskedasticity models as a tool of volatility modelling, to create a classification system of stock price volatility factors and also practically to apply a set of "GETIP" models to the Lithuanian stock market.
correlation analysis, static and dynamic prognostication, various unit root tests (ADF, PP), ARCH-LM--heteroskedasticity test, autocorrelation, partial autocorrelation, ARMA (1,1), calculated "LADSH" model suitability selection criterions, various prognostication accuracy estimation parameters, applied set of general autoregressive conditional heteroskedasticity models "GETIP", descriptive statistics, regression analysis, time series.
Furthermore, there is no consistency in the measures of volatility used ranging from unconditional estimates such as standard deviation in the early literature to conditional ones such as generalized autoregressive conditional heteroskedasticity (GARCH) estimates in more recent times (McKenzie 1999).
These include the autoregressive conditional heteroskedasticity (ARCH)-related models and the more recent model-free aggregated based procedures underpinned by the theory of power variation.
Autoregressive conditional heteroskedasticity
was proposed by Engle (1982) to explain the tendency of large residuals to cluster together.
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.
To capture the dynamic behavior of cross-market information transmission between Chinese A and B shares, we carry out the analysis using a Bivariate Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model for the sample period from January 1994 to May 1999.
Specifically, by employing asymmetric generalized autoregressive conditional heteroskedasticity
in mean models (AGARCH-M), Markov switching models, and a simple theoretical equilibrium framework, we explore how these three related issues--excess stock returns, volatility, and risk aversion--are affected by business cycles.
t] as an ESTAR(4) process indicated substantial autoregressive conditional heteroskedasticity
(ARCH; Engle ) in the innovations.
8) Hsieh (1989, 1991) reports that a generalised autoregressive conditional heteroskedasticity
(GARCH) model explains a large part of the nonlinearities found in major foreign currencies and U.