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  • Andréas Heinen, Alfonso Valdesogo, Kjersti Aas, Luc Bauwens, Lorán Chollete, Claudia Czado +5 others
  • 2009
We propose a new dynamic model for volatility and dependence in high dimensions, that allows for departures from the normal distribution, both in the marginals and in the dependence. The dependence is modeled with a dynamic canonical vine cop-ula, which can be decomposed into a cascade of bivariate conditional copulas. Due to this decomposition, the model(More)
We propose a number of diagnostic methods that can be used whenever multiple outliers are identified by robust estimates for multivariate location and scatter. Their main purpose is to determine whether the outliers form a separate cluster or whether they are randomly scattered. We make use of Mahalanobis distances and linear projections, in order to(More)
We use the conditional distribution and conditional expectation of one random variable given the other one being large to capture the strength of dependence in the tails of a bivariate random vector. We study the tail behavior of the boundary conditional cumulative distribution function (cdf) and two forms of conditional tail expectation (CTE) for various(More)