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How to use standardized residuals from GARCH for?
If you want to just do diagnostics in R, it is quite easy with fGarch package. Summary gives you Standardised Residuals Tests. If you want to do other test you can get standardized residuals with: I think that the same principle works also for rugarch package.
How to fit an Arma-GARCH model to a linear dependence?
Fitting an ARMA-GARCH model, I checked the Weighted Ljung-Box test on standardized residuals and squared residuals to verify if the model is adeguate in describing the linear dependence in the return and volatility series. Combining different orders of the ARCH and GARCH part, for example a GARCH (1,1), GARCH (2,1), GARCH (2,2),
Can a GARCH model have no autocorrelation?
GARCH model assumes that standardized residuals are $i.i.d.$, hence no autocorrelation and no [remaining] ARCH effects. Now the answers to your three questions: (1) You cannot reject “No autocorrelation”, which is a good property to have and characterizes the conditional mean.
Which is better a GARCH model or a mean model?
This is happening assuming that innovations follow a Skew Student- t distribution. Using just a GARCH model without the mean specification seems better in terms of the Ljung-Box test on residuals, and a GARCH (1,1) model fits well the data.
Where to find ACF of GARCH model residuals?
The ACF of the squared standardized residuals appears in the ACF (GARCH_MARKET)2 figure window. Arrange the histogram, quantile-quantile plot, ACF, and the ACF of the squared standardized residual series so that they occupy the four quadrants of the right pane.
How to test for conditional heteroscedasticity in GARCH model?
Assess whether the residual series has lingering conditional heteroscedasticity by plotting the ACF of the squared standardized residuals: In the Models pane, select GARCH_MARKET. Click the Econometric Modeler tab. Then, in the Diagnostics section, click Residual Diagnostics > Squared Residual Autocorrelation.
How to check the autocorrelation of a GARCH model?
Assess whether the standardized residuals are autocorrelated by plotting their autocorrelation function (ACF). In the Models pane, select GARCH_MARKET. On the Econometric Modeler tab, in the Diagnostics section, click Residual Diagnostics > Autocorrelation Function.
What are the functions in the fGARCH package?
The Rmetrics “fGarch” package is a collection of functions to analyze and model heteroskedastic behavior in financial time series models. .
How to check the parameter estimates in fGARCH?
The parameter estimates are checked by several diagnostic analysis tools including graphical features and hypothesis tests. Functions to compute n-step ahead forecasts of both the conditional mean and variance are also available.