The article focuses on testing how well different mathematical models fit real-world financial data, especially when calculating Value-at-Risk (VaR). The researchers used statistical distances to compare the fit of three distribution models to actual data. They found that the Normal distribution did not fit well, but the Generalized Hyperbolic and NIG models did. This means that for VaR calculations, using the right distribution model is crucial for accurately predicting financial risk.