Abstract I study score-driven models for modelling high persistence dependence between financial volatility series. model this with two components, one the long memory and other short-term process. The addition of components offers a parsimonious solution also allows component transient shocks. apply to emerging equities in Americas. estimates are robust advent pandemic. In addition, data resam...

When dealing with multi-issuer credit derivatives such as CDO, it is customary to referthe reader to either of two approaches: “static models” which focus on the copula between thevariables of interest, and “dynamic models” where the diffusion of the underlying variablesis described directly. While the former is widely used due to its simplicity, it is not clearthat there is...

Multi-type recurrent events are often encountered in medical applications when two or more different event types could repeatedly occur over an observation period. For example, patients may experience recurrences of multi-type nonmelanoma skin cancers a clinical trial for cancer prevention. The aims those to characterize features the marginal processes, evaluate covariate effects, and quantify ...

Thomas Nagler*, Christian Schellhase, and Claudia Czado Nonparametric estimation of simpli ed vine copula models: comparison of methods DOI 10.1515/demo-2017-0007 Received December 27, 2016; accepted May 16, 2017 Abstract: In the last decade, simpli ed vine copula models have been an active area of research. They build a high dimensional probability density from the product of marginals densiti...

Sklar’s theorem allows the construction of models for dependent components using a multivariate copula together with marginal distributions. For estimation of the copula and marginal parameters, a two step procedure is often used to avoid high dimensional optimization. Here, marginal parameters are estimated first, then used to transform to uniform margins and in a second step, the copula param...

Model selection is an important activity in modern data analysis and the conventional Bayesian approach to this problem involves calculation of marginal likelihoods for different models, together with diagnostics which examine specific aspects of model fit. Calculating the marginal likelihood is a difficult computational problem. Our article proposes some extensions of the Laplace approximation...