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FRM Part II · FRM Exam Part II · Portfolio Construction

Which statement best describes a key practical advantage of the Black-Litterman model over traditional mean-variance optimization using sample mean returns?

It produces more stable, diversified weights. By anchoring on equilibrium returns and tilting only where the investor holds views, scaled by confidence, it reduces the extreme, input-sensitive allocations typical of mean-variance optimization with sample means, though it does not remove estimation error.

  1. AIt removes the need for a covariance matrix
  2. BIt guarantees returns above the benchmark
  3. CIt produces more stable, diversified weights by anchoring to equilibrium returns and tilting only for viewsCorrect
  4. DIt eliminates estimation error entirely

Explanation

Mean-variance optimization with sample means is highly sensitive to inputs and gives concentrated, unstable portfolios. Black-Litterman anchors on equilibrium and adjusts only where the investor has views, scaled by confidence. It still needs a covariance matrix and does not eliminate estimation error or guarantee outperformance.

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