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Quantitative Analysis Flashcards

7 cards from real FRM practice questions. Tap to flip, then mark Knew It or Still Learning — missed cards come back until you master them.

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  1. OLS (Ordinary Least Squares) regression minimizes which of the following?

    Answer: The sum of squared residuals

    OLS minimizes the sum of squared residuals (differences between observed and predicted values), making it sensitive to outliers but analytically tractable.

  2. An R-squared of 0.85 in a regression model indicates:

    Answer: 85% of the variation in the dependent variable is explained by the model

    R-squared (coefficient of determination) represents the proportion of variance in the dependent variable explained by the independent variables — 0.85 means 85% is explained.

  3. Heteroskedasticity in regression analysis refers to:

    Answer: Non-constant variance of the error terms across observations

    Heteroskedasticity occurs when the variance of error terms is not constant across observations, violating an OLS assumption and affecting the efficiency of estimates.

  4. The Durbin-Watson statistic is primarily used to test for:

    Answer: First-order autocorrelation in residuals

    The Durbin-Watson statistic tests for first-order serial correlation in regression residuals, with values near 2 indicating no autocorrelation.

  5. A GARCH(1,1) model is primarily designed to capture:

    Answer: Volatility clustering in financial return series

    GARCH(1,1) models capture volatility clustering — the well-documented tendency for high-volatility periods to be followed by high volatility and low by low.

  6. Multicollinearity among predictors in a regression model causes:

    Answer: Inflated standard errors of the affected coefficients

    Multicollinearity inflates the standard errors of correlated predictors' coefficients, making it hard to isolate individual effects, though OLS estimates remain unbiased.

  7. Which statistic is used to test the overall significance of a multiple regression model?

    Answer: F-statistic

    The F-statistic tests overall model significance by comparing explained variance to unexplained variance; a significant F indicates the model explains meaningful variation beyond the intercept.