Extract principal component factors from historical return data using eigendecomposition of the covariance matrix. The first PC explains parallel shifts; subsequent PCs explain tilt and curvature.
Extract principal component factors from historical return data using eigendecomposition of the covariance matrix. The first PC explains parallel shifts; subsequent PCs explain tilt and curvature
Each component has a specific meaning:
Note: Interpret the pca factor extraction result against the thresholds and context described above.
Enter the historical return data for the scenario you are assessing. Extract principal component factors from historical return data using eigendecomposition of the covariance matrix. The first PC explains parallel shifts; subsequent PCs explain tilt and curvature. Use the pca factor extraction result to inform your calculation.