What is Egarch model?
An EGARCH model is a dynamic model that addresses conditional heteroscedasticity, or volatility clustering, in an innovations process. Volatility clustering occurs when an innovations process does not exhibit significant autocorrelation, but the variance of the process changes with time.
Which Garch model is the best?
In general, for the normal period (pre and post-crisis), symmetric GARCH model perform better than the asymmetric GARCH but for fluctuation period (crisis period), asymmetric GARCH model is preferred.
What is a time series regression?
Time series regression is a statistical method for predicting a future response based on the response history (known as autoregressive dynamics) and the transfer of dynamics from relevant predictors.
What are time series operators?
The Time Series operator forecasts values of a numerical data field for a future period in time. The output of the operator is a Time Series model that contains the original data sets and the forecast predictions for each set. The model can be visualized using the visualizer operator.
What does mgarch stand for in order Stata?
ORDER STATA. MGARCH stands for multivariate GARCH, or multivariate generalized autoregressive conditional heteroskedasticity. MGARCH allows the conditional-on-past-history covariance matrix of the dependent variables to follow a flexible dynamic structure.
Which is better EGARCH model or GARCH model?
The empirical results suggest that EGARCH model fits the sample data better than GARCH model in modeling the volatility of Chinese stock returns. The result also shows that long term volatility is more volatile during the crisis period. Bad news produces stronger effect than good news for the Chinese stock market during the crisis.
What does mgarch stand for in multivariate GARCH?
Multivariate GARCH. MGARCH stands for multivariate GARCH, or multivariate generalized autoregressive conditional heteroskedasticity. MGARCH allows the conditional-on-past-history covariance matrix of the dependent variables to follow a flexible dynamic structure. Stata fits MGARCH models.
How is the EGARCH p q model chosen?
An EGARCH p q model assumes that: The best model ( p and q) can be chosen, for instance, by Bayesian Information Criterion (BIC), also known as Schwarz Information Criterion (SIC), or by Akaike Information Criterion (AIC). The former tends to be more parsimonious than the latter.