The demand for accurate models involving larger numbers of assets is strong not only in view of the financial crisis of 2007-2009. In particular dependencies among assets have not been captured adequately. While standard multivariate copulas have added some flexibility, this flexibility is insufficient in higher dimensional applications. Regular vines can fill this gap by benefiting from the ri...

The Gaussian factor copula model is the market standard model for multi-name credit derivatives. Its main drawback is that factor copula models exhibit correlation smiles when calibrating against market tranche quotes. To overcome the calibration deficiency, we introduce a multi-period factor copula model by chaining one-period factor copula models. The correlation coefficients in our model are...

In this paper, we address two important issues in semiparametric survival model selection for censored data generated by the Archimedean copula family: method of estimating the parametric copulas and data reuse. We demonstrate that for selection among candidate copula models that could all be misspecified, estimators of the parametric copulas based on minimizing the selection criterion function...

Many popular estimators for duration models require independent competing risks or independent censoring. In contrast, copula based estimators are also consistent in presence of dependent competing risks. In this paper we suggest a computationally convenient extension of the Copula Graphic Estimator (Zheng and Klein, 1995) to a model with more than two dependent competing risks. We analyse the ...

Through empirical research is identified that the hypothesis of normal distribution of returns is no longer observed while verifying the existence of heavy tails and asymmetries in the distribution. Thus, the article has aimed empirically apply copula models using techniques of realized volatility (HAR) with high-frequency data and perform the calculation of Value at Risk for different periods....

Copula-based models provide a great deal of flexibility in modelling multivariate distributions, allowing for the specifications marginal distributions separately from dependence structure (copula) that links them to form joint distribution. Choosing class copula is not trivial task and its misspecification can lead wrong conclusions. We introduce novel grid-uniform functions, which dense space...

The estimation of dependencies between multiple variables is a central problem in the analysis of financial time series. A common approach is to express these dependencies in terms of a copula function. Typically the copula function is assumed to be constant but this may be inaccurate when there are covariates that could have a large influence on the dependence structure of the data. To account...

The estimation of loss reserves for incurred but not reported (IBNR) claims presents an important task for insurance companies to predict their liabilities. Recently, individual claim loss models have attracted a great deal of interest in actuarial literature, which overcome some shortcomings of aggregated claim loss models. The dependence of the event times with the delays is a crucial issue f...

Abstract Discrete vine copulas, introduced by Panagiotelis et al. (2012), provide a flexible modeling framework for high-dimensional data and have significant computational advantages over competing methods. A vine-based multivariate probability mass function is constructed from bivariate copula building blocks and univariate marginal distributions. However, even for a moderate number of variab...