Contents
When to use dynamic conditional correlation in Stata 12?
I am testing the co-movement between 2 exchanges by using the dynamic conditional correlation (DCC) developed by Robert Engle (2002). I want to apply this method in stata 12 and used this command:
Which is an example of a Dynamic correlation?
You will find the following example of computing the dynamic correlations and their forecasts. which produces the graph of the dynamic correlations between the three individual stocks. If you are only interested in the in-sample fits of the conditional correlations, then it becomes really simple, and you can drop the tsappend command:
How to calculate dynamic correlations in mgarch DCC postestimation?
You need to read the help file for -mgarch dcc postestimation-. You will find the following example of computing the dynamic correlations and their forecasts. which produces the graph of the dynamic correlations between the three individual stocks.
When to drop tsappend for conditional correlations?
If you are only interested in the in-sample fits of the conditional correlations, then it becomes really simple, and you can drop the tsappend command: The option t>1600 asks that the in-sample predictions be plotted starting at “time period” 1600 (this dataset has a time index that runs from 1 to 2015).
Which is a dynamic conditional correlation ( DCC ) model?
A new class of multivariate models called dynamic conditional correlation (DCC) models is proposed. These have the flexibility of univariate GARCH models coupled with parsimonious parametric models for the correlations. They are not linear but can often be estimated very simply with univariate or two step methods based on the likelihood function.
How are time varying correlations estimated in multivariate models?
Time varying correlations are often estimated with Multivariate Garch models that are linear in squares and cross products of the data. A new class of multivariate models called dynamic conditional correlation (DCC) models is proposed.