Let me, as it often happens, disagree with everyone ;)
There is a flame taking place now between LeCun and Musk about what science is. Keeping it simple, science is, among other things, about finding efficient ways of recording the observed quantities. Compressing them, so to speak. PCA, is a wonderful example of it.
As ws already mentioned, denoising correlation matrix is already a good thing. However using such matrix to justify variance/covariance "market risk" under the jointly Gaussian assumption is not good. Variance/covariance is bad, even though FRTB SA attempts to legitimise it in the most brutal way.
Doing nonparametric VAR/ES on the proncipal factors is perhaps better than on the factors themselves, if PCA indicated lower dimensionality. Practically speaking, the confidence interval of VAR (which, being a statistic does have a confidence interval) will be less. That said, in the world where AR(n) is routinely estimated by regressions, who cares?
The true value of lower dimensionality is in the long-term risk.
For the nonlinear pricing problems up to and including XVA gives you much better control of the model specification. You will be usually using a low-dimensional HJM/Cheyette, so you need both sensible historical correlation matrix and just the count of the factors for dynamics.
For long-term risk problems, e.g. counterparty risk, model risk, composite stohastic/deterministic scenarios and trading strategy design, you need to balance the richness of the model so as not to miss out the essential components, with keeping as few factors as possible for performance reasons.
Finally, I'd say modelling rate curves is, perhaps, the easiest multifactor problem. This is because processes are more or less stationary, so PCA is more of a nice additional tool.
PCA as a percieved panacea for the dimensionality reduction in modelling the families of the asset-like quantities results in researches rushing into modelling dependency between returs (with the goal of PCA'ing their correlation matrices) and possibly missing out on stuff like cointegratiok or Garch. Now this is myopic and bad.