We present two methods based on functional principal component analysis (FPCA) for the estimation of smooth derivatives … decomposition to a) the dual covariance matrix of the derivatives, and b) the dual covariance matrix of the observed curves. To …

, conditional quantiles are specified via hierarchical Archimedean copula. The parameters and structure of this copula are …

conditions in different locations. For that purpose copula methods are employed that allow an adequate description of stochastic …

Archimedean opulas and allows for general non-exchangeable dependency structures. We show that the structure of the copula can be … uniquely recovered from all bivariate margins. We derive the distribution of the copula value, which is particularly useful for …

promising class of models are the hierarchical Archimedean copulae (HAC) that allow for non-exchangeable and non … for stock indices the copula parameter changes dynam- ically but the hierarchical structure is constant over time …

This paper make an overview of the copula theory from a practical side. We consider different methods of copula … Gaussian copulae but also Hierarchical Archimedean Copulae. Afterwards we provide an empirical part to support the theory. …

. Nevertheless it is not very often consistent with the real data. Copulae allows for an extension of the classical time series … models to nonelliptically distributed residuals. In this paper we apply different copulae to the calculation of the static … and dynamic Value-at-Risk of portfolio returns and Profit-and-Loss function. In our findings copula based multivariate …

. In order to describe such situations, copula-based models have been studied during the last year. In this paper, we …

We introduce a copula-based dynamic model for multivariate processes of (non-negative) high-frequency trading variables … multiplicative error model we map the resulting residuals into a Gaussian domain using a Gaussian copula. Based on high … proposed copula-based transformation is supported by the data and allows disentangling (multivariate) dynamics in higher order …