Skip to content

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

A bank wants to reduce the dimensionality of its interest rate risk model by using PCA instead of modeling every key rate separately. Which is the most significant practical advantage of this approach?

PCA captures most of the curve's co-movement with a few uncorrelated factors, which simplifies hedging and simulation. It still needs a covariance matrix, does not guarantee stable or predictive factors, and the level, slope and curvature labels are interpretations.

  1. AIt guarantees that the retained factors will remain stable and forecast future rate moves accurately
  2. BIt eliminates the need to estimate any covariance matrix
  3. CIt captures most of the co-movement of the curve with a few uncorrelated factors, simplifying hedging and simulationCorrect
  4. DIt ensures that the factors have fixed economic labels determined by the data provider

Explanation

PCA still requires a covariance or correlation matrix. Its benefit is that a few uncorrelated factors explain most variance, making hedging and Monte Carlo simulation simpler. It does not guarantee stability or forecasting ability, and labels are interpretive.

Did you get it right without looking?

One question tells you little. A timed set on Regression Hedging and Principal Component Analysis shows your real accuracy, how long you take and where you lose marks.

Practise Regression Hedging and Principal Component AnalysisFree diagnostic mock

More Regression Hedging and Principal Component Analysis questions