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FRM Part II · FRM Exam Part II · Regression Hedging and Principal Component Analysis

Which is a key advantage of PCA-based hedging over a hedge based on regressions of each key rate on a single benchmark rate?

PCA summarizes many correlated rate movements into a few uncorrelated factors that explain most variance, such as level, slope and curvature. This gives a parsimonious hedge, though it is not perfect and still relies on historical data.

  1. AIt uses orthogonal factors that capture the dominant common movements with few variablesCorrect
  2. BIt guarantees a perfect hedge against all curve movements
  3. CIt requires no historical data
  4. DIt produces correlated factors that make the exposures easier to interpret

Explanation

PCA reduces many correlated rate changes to a few uncorrelated factors that explain most variance, giving a parsimonious hedge. It does not give perfect hedges, since omitted components remain, and it still needs historical data.

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