The article evaluates different models used to predict financial risk in Asian stock markets during and after the 1997-1998 financial crisis. The researchers compared various Value-at-Risk (VaR) models to see how well they predicted risk levels. They found that some models, like Riskmetrics, performed better in stable times, while Extreme Value Theory (EVT) models were more accurate during crises. Filtering improved some models but not others. The CaViaR quantile regression models were successful in predicting risk levels consistently. Overall, the performance of the VaR models varied depending on the time period being analyzed.