Explanation:
Principal Component Analysis (PCA) is a dimensionality reduction technique that takes correlated variables (like interest rates at various maturities across a yield curve) and linearly transforms them into a set of uncorrelated variables known as principal components. The primary advantage is that it decomposes the yield curve movements into independent, uncorrelated components (typically level, steepness/slope, and curvature) that explain the majority of the variance, greatly simplifying the identification of risk exposures and the construction of hedging portfolios.
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Q.68 A financial analyst is exploring principal component analysis (PCA) for constructing a hedging portfolio. What primary advantage does PCA offer when identifying risk exposures within a portfolio of fixed-income instruments?
A
PCA assumes perfect correlation across all rates, simplifying analysis.
B
PCA assumes perfect correlation across all rates, simplifying analysis.
C
PCA guarantees a constant yield curve slope, assisting consistent interpretations.
D
PCA decomposes the yield curve changes into uncorrelated components that capture the most variance.