Modelling mortality dependence: An application of dynamic vine copula

Dependence Modelling via the Copula Method

Dynamic Tail Dependence in Copula-GARCH Models: an Application to Stock-Index Returns

1. Methods and application Several studies in empirical finance literature have highlighted the importance of allowing for skewness, tail-fatness, non normality of returns for asset allocation and pricing models. Moreover, the dependence between returns, that can impact portfolio decisions, often exhibits nonlinear structures and asymmetric extremal behavior that the usual correlation coefficie...

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...

Dependence Structure Analysis Of Meta-level Metrics in YouTube Videos: A Vine Copula Approach

This paper uses vine copula to analyze the multivariate statistical dependence in a massive YouTube dataset consisting of 6 million videos over 25 thousand channels. Specifically we study the statistical dependency of 7 YouTube meta-level metrics: view count, number of likes, number of comments, length of video title, number of subscribers, click rates, and average percentage watching. Dependen...

Dynamic copula modelling for Value at Risk

This paper proposes dynamic copula and marginals functions to model the joint distribution of risk factor returns affecting portfolios profit and loss distribution over a specified holding period. By using copulas, we can separate the marginal distributions from the dependence structure and estimate portfolio Value-at-Risk, assuming for the risk factors a multivariate distribution that can be d...