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FRM Part II · FRM Exam Part II · Portfolio Construction

A fund is built by a mean-variance optimizer using historical factor return estimates and produces extreme, unstable weights that change greatly each rebalance. Which remedy is most appropriate to improve robustness?

Shrinking the covariance matrix and adding position limits is most appropriate. Optimizers amplify estimation error, creating extreme unstable weights; shrinkage reduces noise and constraints restrain concentration. Removing constraints, shortening the window or adding assets with a fixed sample would increase estimation error.

  1. ARemove all constraints so the optimizer can find the true optimum
  2. BShorten the estimation window to the most recent month
  3. CApply shrinkage to the covariance matrix and add position limitsCorrect
  4. DIncrease the number of assets while keeping the sample length fixed

Explanation

Estimation error in expected returns and covariances is amplified by optimizers. Shrinkage reduces noise and position limits restrain extreme weights. Removing constraints or shortening the window worsens instability, and adding assets with a fixed sample makes the covariance estimate noisier.

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