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I am looking for some papers related to the value-at-risk theory. I would like to focus on mathematical aspects of VaR. I would like to read something about modern approaches to VaR (maybe using Machine Learning methods of something like that).

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My first thought was that machine learning is quite exaggerated for a rather simple (but often controversial) measure like VaR.

However, after some research I found an interesting paper re the improvement of VaR by combining different approaches under the use of artificial neural networks to balance the downsides of the methods out. So maybe you find this interesting as well: Value-at-Risk Model Combination Using Artificial Neural Networks by Yan Liu

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I have read the article "Computation of the corrected Cornish–Fisher expansion using the response surface methodology: application to VaR and CVaR" by Charles-Olivier Amédée-Manesme, Fabrice Barthélémy, Didier Maillard (Annals of Operations Research, 2018, https://doi.org/10.1007/s10479-018-2792-4 ). You definitely should read this in order to keep up with the current state of progress, but don't turn off your critical thinking skills when you see keywords like "response surface" and "machine learning".

Also this talk https://cornell.mediasite.com/Mediasite/Play/0db1659795f6472cbc8fb6030f7eac041d by Jonathan Schachter is a very good summary of what people actually use in practice for VaR calculations.

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