Skip to content

FRM Part II · FRM Exam Part II · Portfolio Construction

A portfolio manager uses mean-variance optimization with historical returns for 40 assets and obtains a portfolio with extreme long and short positions that change sharply whenever the inputs are updated slightly. Which explanation best describes the problem?

Mean-variance optimization is highly sensitive to estimation error in expected returns and covariances. Small input changes are magnified into extreme, unstable long and short weights, which is why practitioners use constraints, shrinkage or Bayesian approaches to stabilize portfolios.

  1. AOptimization is sensitive to estimation error in expected returns and covariances, so it amplifies input noise into extreme weightsCorrect
  2. BMean-variance optimization requires that returns be uniformly distributed, which fails for equities
  3. CThe optimizer ignores expected returns and minimizes only the number of holdings
  4. DMean-variance optimization assumes investors are risk-seeking, producing leveraged weights

Explanation

Mean-variance optimizers act as error maximizers: small errors in expected returns, especially, produce large changes in weights. Constraints, shrinkage, or Bayesian methods are used to stabilize results. The other options misstate the assumptions of the framework.

Did you get it right without looking?

One question tells you little. A timed set on Portfolio Construction shows your real accuracy, how long you take and where you lose marks.

Practise Portfolio ConstructionFree diagnostic mock

More Portfolio Construction questions