Principal Components and Independent Components

Extreme events: blending PCA and ICA [27]

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Bullet points include: Doesn’t model index return distributions particularly well. Particularly parts risk managers might be most interested in, i.e. downside tails. Computation gives less weight to tail observations (most observations are in middle of the distribution). Lacks a desirable stability criterion. Applying CF twice can lead to a more extreme distribution

Bullet points include: Doesn’t model index return distributions particularly well. Particularly parts risk managers might be most interested in, i.e. downside tails. Computation gives less weight to tail observations (most observations are in middle of the distribution). Lacks a desirable stability criterion. Applying CF twice can lead to a more extreme distribution

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