Tailoring tails within the mixture model framework















































Tailoring tails within the mixture model framework – Risk.net



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Introducing a data generator mixing Gaussian and Student-t distributions to capture fat tails


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Realistic models for multi-dimensional asset distributions are vital for stress testing and risk management. Many statistical learning tools need large datasets and lack transparency. Extending his 2022 and 2025 contributions, Joerg Kienitz introduces stretched Gaussian and Student t mixture models. By incorporating fat tails while keeping semi-analytic tractability

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