heteroskedasticity

  1. Nicole Seaman

    P1.T2.20.19. Regression diagnostics: omitted variables, heteroskedasticity, and multicollinearity

    Learning objectives: Explain how to test whether a regression is affected by heteroskedasticity. Describe approaches to using heteroskedastic data. Characterize multicollinearity and its consequences; distinguish between multicollinearity and perfect collinearity. Describe the consequences of...
    • Nicole Seaman
    • Thread
    • explanatory-variable heteroskedasticity irrelevant-regressor multicollinearity omitted-variables perfect-collinearity regression
    • Replies: 1
    • Forum: FRM® Practice Questions
  2. M

    Homoscedasticity vs heteroscedasticity

    Dear all, I now try to calculate the factor VAR for my fixed income portfolio. The factor VAR assumes that each and every asset in the portfolio has an exposure on a set of the same factors. It’s greatest advantage is no need to calculate too many volatilities and correlations ( I have some 70...
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