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Explanation:
Principal Component Analysis (PCA) is indeed very useful in the construction of empirically-based hedges for large portfolios. PCA is a statistical procedure that uses an orthogonal transformation to convert a set of observations of possibly correlated variables into a set of values of linearly uncorrelated variables called principal components. This transformation is defined in such a way that the first principal component has the largest possible variance, and each succeeding component in turn has the highest variance possible under the constraint that it is orthogonal to the preceding components. The resulting vectors are an uncorrelated orthogonal basis set.
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Q.1614 What makes the Principal Component Analysis (PCA) highly practical and doable in the scope of hedging and risk metrics?
A
The PCA is very useful in the construction of empirically-based hedges for large portfolios.
B
The PCA is very useful in the construction of empirically-based hedges for small portfolios.
C
The PCA is very useful in the construction of theoretically-based hedges for large portfolios.
D
The PCA is very useful in the construction of theoretically based hedges for small portfolios.