Heteroskedasticity (or heteroscedasticity) occurs in statistics when the standard deviations of a predicted variable are non-constant when measured over different values of an independent variable or when compared to previous time periods. When it comes to heteroskedasticity, the tell-tale indicator is that the residual errors will tend to widen out with time, as seen in the figure below.

Heteroskedasticity can take two forms: conditional and unconditional heteroskedasticity. Conditional heteroskedasticity reveals nonconstant volatility that is connected to the volatility of a previous period (e.g., daily). Unconditional heteroskedasticity refers to changes in volatility structure that are unrelated to volatility in previous periods. When future periods of high and low are expected, unconditional heteroskedasticity is utilized.

**Heteroskedasticity** refers to situations where the variance of the residuals is unequal over a range of measured values. When running a regression analysis, heteroskedasticity results in an unequal scatter of the residuals (also known as the error term).

When observing a plot of the residuals, a fan or cone shape indicates the presence of heteroskedasticity. In statistics, heteroskedasticity is seen as a problem because regressions involving ordinary least squares (OLS) assume that the residuals are drawn from a population with constant variance.

If there is an unequal scatter of residuals, the population used in the regression contains unequal variance, and therefore the results are inaccurate.