Impact of Extreme Events – Classic risk management models used for risk management in investment management underestimate the impact of extreme events and struggle to recognize the dynamic nature of market volatility. There are several fundamental assumptions underlying these traditional risk models that do not align with the volatile nature of today’s financial markets.

Normal Distribution Assumption – The most widely used assumption that asset returns follow normal distribution contradicts the reality of financial markets which is prone to fat tails – extreme returns that could not be accurately predicted using the normal distribution assumption.

Volatility Clustering – Another flaw of the traditional risk models is that they ignore volatility clustering, which refers to the periods of high volatility following each other in bursts, which leads to the failure to model the changing risk dynamics in the financial markets accurately.

Asset Correlations – During normal conditions, using asset correlations helps manage risk for which classical risk models do a good job. However, traditional risk management tools struggle to anticipate increased portfolio risks in times of market stress when asset correlations change dramatically.

Extrapolating Past Performance – Classical models also assume a degree of predictability in the financial markets implying that past patterns can continue in the future. However, the global economic landscape changes constantly due to economic, technological, financial, natural, and environmental factors which makes financial markets inherently non-stationary, meaning that historical averages such as mean and variance are unreliable predictors.