Skip to content

FRM Part II · FRM Exam Part II · Portfolio Construction

A practitioner complains that mean-variance optimization using sample means yields extreme, concentrated weights. Which feature of Black-Litterman most directly addresses this problem?

Black-Litterman blends equilibrium-implied returns with investor views, so the optimizer starts at a diversified market-weight portfolio and tilts only where views exist. This reduces the extreme, concentrated weights that arise from noisy sample mean estimates.

  1. AIt forces all weights to be non-negative
  2. BIt blends equilibrium-implied returns with views, giving a diversified starting point that deviates only where views existCorrect
  3. CIt eliminates the need for a covariance matrix
  4. DIt maximizes expected return subject to a tracking error of zero

Explanation

Black-Litterman anchors expected returns to equilibrium, so unconstrained optimization returns market weights absent views and tilts only for assets involved in views. It still needs a covariance matrix and does not impose non-negativity.

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