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In my research, VaR in risk reporting has regularly come under criticism for not capturing tail-risk adequately and for creating a false sense of confidence when taken literally as "the maximum value that can be erased at a 1% event".

Since I have been asked to provide that metric now, I was wondering: Has the finance community already converged on a best-practice alternative?

What are measurements that I could offer as a more sensible alternative?

EDIT:

I know about Expected Shortfall (https://en.wikipedia.org/wiki/Expected_shortfall) also called CVaR, so I would be grateful to hear about how widespread its usage is. I haven't looked at EVaR yet, but I'm generally looking for any opinions and a debate about what measure has emerged to be considered robust and where the intuitive interpretation corresponds to it's mathematical properties.

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  • $\begingroup$ This site it not meant to express opinions and host debates (this might be more for forums and this is a Q&A site), but your question can be answered in a factual manner so I leave it open. $\endgroup$ – SRKX Feb 8 '17 at 9:51
  • $\begingroup$ Thanks, I was indeed trying to formulate this as a question about methodology and best practices, not opinion. I will actually edit my first paragraph to reflect that. On a side-note: I saw that you removed tags that could be considered redundant, what are the rules for tagging? $\endgroup$ – instant Feb 8 '17 at 10:29
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    $\begingroup$ Have a look at this in depth comparison: arxiv.org/abs/1312.1645 Expected shortfall is the prescribed risk measure for the Swiss Solvency Test(finma.ch/en/supervision/insurers/cross-sectoral-tools/…), the Canadian Capital Adequacy for Life Insurers (osfi-bsif.gc.ca/Eng/Docs/LICAT.pdf) and used in Principle Based Reserving in the United States (naic.org/cipr_topics/principle_based_reserving_pbr.htm) $\endgroup$ – g g Feb 11 '17 at 22:11
  • $\begingroup$ Amazing, thank you very much for the docs, this is super helpful! Why didn't you put it into an answer below? Would have loved to see this stand out more. $\endgroup$ – instant Feb 12 '17 at 21:58
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In the banking sector, people is getting more and more concern about this topic. We are listening a lot about Espected Shortfall and TailVaR.

But there are also methods to take more into account the tails. For example, you can change your distribution to a T-Student.

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  • $\begingroup$ Thanks for your answer. I was actually wondering about just swapping out the underlying normal distribution for something else, and it seems like it's easy enough to try and compare. $\endgroup$ – instant Feb 8 '17 at 13:11
  • $\begingroup$ Did it work? The t-Student distribution will take more into account the tails, it makes them wider. that was the point of my idea $\endgroup$ – arodrisa Feb 9 '17 at 9:43
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Regarding your "false sense of confidence" statement: most of it has nothing to do with VaR as a framework itself, it's about how you implement it. Naive HS-VaR fails you? Sure thing, but who's to blame in the situation except yourself?

Most of the pratitioners use VaR in its simplest form, which yields predictably poor results. For the sake of illustration let's consider one of the many ways of improvement: GARCH specification for conditional volatility paired with a distribution that allows for higher order (3rd and 4th) conditional moment dynamics (e.g. Normal Inverse Gaussian, Skewed Generalized t etc). This solves two problems: accounts both for heterosedastic structure of our series (volatility clustering) and fat tails. How great is the improvement? Time for backtesting.

VaR is flawed in a sense that it is not a coherent risk measure (it is not subadditive), but it is still massively preferred in practice over Expected Shortfall, which is a coherent measure, due to great backtesting properties, and that already tells a lot. I will not cover the tests themselves, you can easily look them up (unconditional coverage test, conditional coverage test, Berkowitz tail test etc). What you will notice (here, for example) when backtesting models based on normal distribution vs NIG, SGT etc, is that the former barely pass any tests while the latter perform fairly well (i.e. loss exceedances really occur at the specified confidence level, they do not cluster and fat tails are accounted for properly).

tl;dr: VaR is still one of the best practices out there when implemented properly.

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  • $\begingroup$ Nice, thanks for the detailed pointers, that'll give me some material to read up on! $\endgroup$ – instant Feb 8 '17 at 15:20

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