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FRM Part II · FRM Exam Part II · Portfolio Construction

Which change to a mean-variance optimization would most directly reduce the extreme, concentrated weights it tends to produce?

Adding constraints such as position limits and a no-short-sales rule most directly curbs extreme weights, because it restricts the optimizer from exploiting estimation errors in the inputs. Shorter samples increase noise, and ignoring covariances discards diversification information.

  1. AAdding constraints such as position limits and no short salesCorrect
  2. BUsing only the most recent month of returns to estimate inputs
  3. CRemoving the covariance matrix and using only expected returns
  4. DIncreasing the target return to the highest asset return

Explanation

Position limits and short-sale constraints cap the optimizer's ability to exploit estimation errors, which stabilizes weights. Using a shorter sample increases estimation noise, and dropping covariances removes diversification information.

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