If you wander about the theoretical result of fitting parameters, the book GARCHModels, Structure, Statistical Inference and Financial Applications of FRANCQ and ZAKOIAN provides a step-by-step explanation.
This vignette does not use qrmtools, but shows how Value-at-Risk (VaR) can be fitted and predicted based on an underlying ARMA-GARCH process (which of course also concerns QRM in the wider sense).
GARCHmodel is developed in 1982 by Robert F. Engle, an economist and 2003 winner of the Nobel Memorial Prize for Economics. Describing the behavior of a time series is challenging for a...
Arguments model a list of GARCHmodel parameters, see section ‘Details’. The default model=list() specifies Bollerslev's GARCH (1,1) model with normal conditional distributed innovations. presample a numeric three column matrix with start values for the series, for the innovations, and for the conditional variances. For an ARMA (m,n)-GARCH (p,q) process the number of rows must be at least ...
This is where a GARCHmodel (Generalized Autoregressive Conditional Heteroskedasticity) comes into play. The GARCHmodel describes the variance of the current error term as following an ARMA process, instead of being constant.
The appgarch function computes RMSE and MAE of the all possible combinations of GARCH type model and distribution, and forecast value. Based on the lowest RMSE and MAE, we can find the best model and distribution combinations of the particular data.
The appgarch function computes RMSE and MAE of the all possible combinations of GARCH type model and distribution, and forecast value. Based on the lowest RMSE and MAE, we can find the best model and distribution combinations of the particular data.
Joint estimation of ARMA-GARCH type models can be handled with functions from the rugarch package. Apart from the documentation of the package, there is a worked out example here and more examples on the package author’s blog.
We will discuss the underlying logic of GARCHmodels, their representation and estimation process, along with a descriptive example of a real-world application of volatility modeling.
We will discuss the underlying logic of GARCH models, their representation and estimation process, along with a descriptive example of a real-world application of volatility modeling.