Which issue is commonly associated with non-normality in returns?

The issue commonly associated with non-normality in returns is the assumption of linear correlations. In finance, the traditional models often assume that asset returns follow a normal distribution and that correlations between asset returns are linear. However, when returns are non-normally distributed, it can lead to misleading implications regarding risk and diversification.

Non-normality often indicates the presence of fat tails or skewness in the return distributions, which suggests that extreme outcomes—both positive and negative—are more likely than what a normal distribution would predict. This directly challenges the assumption of linear correlations, as the relationships between assets can become more complex and may involve non-linear dependencies during extreme market conditions or in the presence of events that affect asset performances disproportionately.

Moreover, acknowledging non-normality prompts investors to adjust their risk management strategies and portfolio constructions to account for potential tail risks that conventional models might understate. Understanding and identifying these characteristics in return distributions is crucial for effective alternative investment analysis.