A historical simulation approach to VaR estimation relies on the availability of historical data. What do we do when there is no data (say, spot price and implied volatility surface) as, for example, in cases of new equity issues or new bond issues? ("New" in the previous sentence does not necessarily mean "recent" as for SVaR one would probably need data from 2007-2008).
So, what can we do to "fill that missing data"? What are the best-industry-practice methodologies for that? I can envisage approaches like "proxying" and regression, but these seem somewhat crude and primitive. At the other end, I can also envisage a nonlinear autoregressive neural network with exogenous inputs (NARX), but not sure if these are actually used in practice for data-filling.
I would have thought that this is a very common problem and expected to find a lot of literature on this topic, but alas my search did not reveal anything.