Large changes up or down, also called fat tails, are more likely than what one would calculate using a Gaussian distribution with an estimated standard deviation.
[1][2] Quantitative risk analysis and its modeling have been under question in the light of corporate scandals in the past few years (most notably, Enron), Basel II, the revised FAS 123R and the Sarbanes–Oxley Act, and for their failure to predict the financial crash of 2008.
[1][3][4] Rapid development of financial innovations lead to sophisticated models that are based on a set of assumptions.
Jokhadze and Schmidt (2018) propose practical model risk measurement framework based on Bayesian calculation.
Jon Danielsson argues that risk forecasts are very inaccurate, especially in typical sample sizes, and is concerned about their use in regulations.