In recent months I've come to the conclusion that there are not only certain regimes in the markets (like bear or bull) but phases where all models fail because we are in uncharted territory. The former are pockets of predictability, as I like to call them, the latter are phases where it is best to stay out of the markets altogether.
Another observation is that there seem to be variables that in and of themselves have very little forecasting power but seem to be useful in differentiating between different regimes. I haven't tested that rigorously but an idea why this could be the case would be that the relationship is highly non-linear, but there nevertheless.
As a very crude example lets take the VIX as the so called barometer of fear. It doesn't seem to be that good at forecasting returns, yet it seems that different levels show different regimes, i.e. a certain tendency in the market (low -> bull, high -> bear).
But when we have extreme readings the swings are extreme too, i.e. markets falling like a stone but sometimes very pronounced swing-backs too. That would be an example of complete unpredictability. There could also be a region between a real "low" and a real "high" reading where things are unpredictable too (even in probabilistic terms).
As another, more elaborate example take a look at this quant trading model from UBS
How would you proceed in building a model that identifies different regimes in the data and meta-models its own limits? Which mathematical ansatz (approach) would you choose? How would you find the brackets (barriers/limits)? How would you test it?