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Risk aggregation and Extreme Events [55]

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Bullet points include: Relatively simple Generate a vector, x, of correlated Gaussian random variables And then transform as per: Simplest way of generating correlated Gaussians if i,j = for all i j is (as we have seen already) to simulate a Gaussian common factor X and independent random variables 1, ..., m and then form: If i,j vary for i j then use Cholesky decomposition, see e.g. Nematrian website

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