How you would try convincing a non-technical audience that applying DCC GARCH for correlation estimation is better than Pearson's correlation?
The task becomes even more challenging since, as seen in the below image, the GARCH-based correlation follows quite closely the Pearson's correlation calculated with a rolling window.
I am considering the following:
- In the above graph, in the beginning of 2016 the 'GARCH' correlation spikes under zero while the Pearson's rolling window doesn't react equally. It's true that there was a huge diversion between the two timeseries at this point so I could argue that, in this example, the 'GARCH' one is much faster to react.
- The 'GARCH' model includes the necessary mathematical framework to react to extreme conditions (the implementation I am using assumes a t-distribution for the posterior probabilities)
- Pearson's correlation was established around 1900 while Engle's paper regarding DCC-GARCH was published around 100 years later. There has been significant research activity in the meantime so the underlying maths are bound to be more advanced.