Hands-On Machine Learning for Algorithmic Trading

Heteroskedasticity

GMT assumption 5 requires the residual covariance to take the shape , that is, a diagonal matrix with entries equal to the constant variance of the error term. Heteroskedasticity occurs when the residual variance is not constant but differs across observations. If the residual variance is positively correlated with an input variable, that is, when errors are larger for input values that are far from their mean, then OLS standard error estimates will be too low, and, consequently, the t-statistic will be inflated leading to false discoveries of relationships where none actually exist.

Diagnostics starts with a visual inspection ...