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Flexible Tails for Normalising Flows, with Application to the Modelling of Financial Return Data
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We propose a transformation capable of altering the tail properties of a distribution, motivated by extreme value theory, which can be used as a layer in a normalizing flow to approximate multivariate heavy tailed distributions. We apply this approach to model financial returns, capturing potentially extreme shocks that arise in such data. The trained models can be used directly to generate new synthetic sets of potentially extreme returns
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Generative modelling of multivariate geometric extremes using normalising flows
A normalising-flow framework for geometric extremes models directional densities on the hypersphere and estimates probabilities of arbitrary extreme risk regions in up to 10 dimensions.
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