Which problem of time-series price data pertains to returns not being independent?

The issue of returns not being independent is closely related to the phenomenon known as path dependency and serial correlation. In financial time series, path dependency refers to the idea that the values of a time series today can be affected by the values in previous time periods. This indicates that past price movements can influence future movements, leading to dependencies in the returns over time.

Serial correlation, on the other hand, specifically addresses the relationship between a variable's current value and its past values. If returns are serially correlated, this means there is a pattern or trend in the historical returns that can predict future returns to some extent. As a result, independence of returns is compromised, which can have important implications for statistical modeling and risk management in trading strategies.

Understanding this concept is vital for analysts and traders because most traditional models assume that asset returns are independent and identically distributed (iid). When this assumption fails, it can affect the validity of forecasting, risk assessments, and ultimately trading strategies.

In contrast, the other options like heteroscedasticity, non-normality of returns, and self-correcting markets deal with different issues related to statistical properties and market behavior but do not inherently address the problem of return independence in the same way. Heterosced