Intuitively, Historical VAR is an approach which assumes that in the past data, we have observed everything that can happen, so we consider the worst case(tail). However, when your equity/instrument has a short time series, this assumption breaks down. It is very unlikely that a time series of 100 days will consist of the whole range of likely returns.

What is the best approach to overcome this in a simple manner (avoiding MC or complex parametric approaches)?

My first thought was to build an index of returns from those equities with a complete time series on a sector by sector basis. Then, we can use the index VAR as a proxy. There are 2 problems I have encountered with this:

  1. Using an index means you are averaging returns, which mean you squash the distribution and dampen the extreme values.
  2. This approach assumes everything in the same sector moves together on average, and therefore doesn't contain any idiosyncratic risk. I cannot see how to add in this idiosyncratic risk with such a short time series & such short data.

I have seen EVT mentioned in places. This could possible be suitable for equities which have a moderate Time series length(?), but the problem still holds for equities whose series is too short.



1 Answer 1


The best approach is awfully subjective but by characterizing your data set with EVT (e.g. Generalized Pareto Distribution) you could extrapolate into the tail which will give you more risk levels than your data empirically allows.

It appears you want to create more data points out of the ether but that would require MC (which you don't want to do).


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