FRM Quantitative Analysis 2

Question 1

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

Accepted Answer

The sum of squared residuals

Answer

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

Question 2

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

Accepted Answer

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

Answer

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.

Question 3

Heteroskedasticity in regression analysis refers to:

Accepted Answer

Non-constant variance of the error terms across observations

Answer

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

Question 4

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

Accepted Answer

First-order autocorrelation in residuals

Answer

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

Question 5

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

Accepted Answer

Volatility clustering in financial return series

Answer

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.

FRM Practice Test

FRM Practice Test

FRM Quantitative Analysis 2