Study suggests banks may be better off with simpler VAR models
Non-parametric VAR models perform well in calm markets, but miss the mark in volatile periods
Non-parametric models that do not rely on normal distributions are all the rage in finance these days – especially among quants experimenting with big data and machine learning techniques. But research published in the Journal of Risk Model Validation suggests banks might want to stick with old-fashioned parametric models for calculating value-at-risk.
University of Warsaw economists Mateusz Buczyński and Marcin Chlebus tested VAR calculations for equity indexes from six countries using a
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