Simplified vine copula models: Approximations based on the simplifying assumption

Vine-copula Based Models for Farmland Portfolio Management

Vine Copula Models with GLM and Sparsity

Vine copula provides a flexible tool to capture asymmetry in modelling multivariate distributions. Nevertheless, its flexibility is achieved at the expense of exponentially increasing complexity of the model. To alleviate this issue, the simplifying assumption (SA) is commonly adapted in specific applications of vine copula models. In this paper, generalized linear models (GLMs) are proposed fo...

Assessing the VaR of a portfolio using D-vine copula based multivariate GARCH models

We develop Bayesian inference of a multivariate GARCH model where the dependence is introduced by a D-vine copula on the innovations. A D-vine copula is a special case of vine copulas which are a relatively new and very flexible concept to construct multivariate copulas. In particular it allows to model dependency between pairs of margins individually. In a simulation study and three real data ...

Beyond simplified pair-copula constructions

Any multivariate density can be decomposed through successive conditionings into basic building blocks involving only pairs of variables. The various ways in which this can be done are called regular vines; C-vines and D-vines are prime examples of such structures. A pair-copula construction (PCC) is a modelling strategy in which conditional and unconditional bivariate copula densities are assi...

Exponential Models: Approximations for Probabilities

Welch & Peers (1963) used a root-information prior to obtain posterior probabilities for a scalar parameter exponential model and showed that these Bayes probabilities had the confidence property to second order asymptotically. An important undercurrent of this indicates that the constant information reparameterization provides location model structure, for which the confidence property ...