edited by Dorota Kurowicka (Delft University of Technology, The Netherlands) & Harry Joe (University of British Columbia, Canada)

Table of Contents (29k)
Preface (25k)
Chapter 1: Introduction: Dependence Modeling (1,654k)
Chapter 2: Multivariate Copulae (259k)

This book is a collaborative effort from three workshops held over the last three years, all involving principal contributors to the vine-copula methodology. Research and applications in vines have been growing rapidly and there is now a growing need to collate basic results, and standardize terminology and methods. Specifically, this handbook will (1) trace historical developments, standardizing notation and terminology, (2) summarize results on bivariate copulae, (3) summarize results for regular vines, and (4) give an overview of its applications. In addition, many of these results are new and not readily available in any existing journals. New research directions are also discussed.

• Introduction: Dependence Modeling (D Kurowicka)
• Multivariate Copulae (M Fischer)
• Vines Arise (R M Cooke et al.)
• Sampling Count Variables with Specified Pearson Correlation: A Comparison Between a Naive and a C-Vine Sampling Approach (V Erhardt & C Czado)
• Micro Correlations and Tail Dependence (R M Cooke et al.)
• The Copula Information Criterion and Its Implications for the Maximum Pseudo-Likelihood Estimator (S Grønneberg)
• Dependence Comparisons of Vine Copulae with Four or More Variables (H Joe)
• Tail Dependence in Vine Copulae (H Joe)
• Counting Vines (O Morales-Nápoles)
• Regular Vines: Generation Algorithm and Number of Equivalence Classes (H Joe et al.)
• Optimal Truncation of Vines (D Kurowicka)
• Bayesian Inference for D-Vines: Estimation and Model Selection (C Czado & A Min)
• Analysis of Australian Electricity Loads Using Joint Bayesian Inference of D-Vines with Autoregressive Margins (C Czado et al.)
• Non-Parametric Bayesian Belief Nets versus Vines (A Hanea)
• Modeling Dependence Between Financial Returns Using Pair-Copula Constructions (K Aas & D Berg)
• Dynamic D-Vine Model (A Heinen & A Valdesogo)
• Summary and Future Directions (D Kurowicka)