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Pair-copula constructions of multiple dependence
Predictive Model Assessment for Count Data
Proposals include a nonrandomized version of the probability integral transform, marginal calibration diagrams, and proper scoring rules, such as the predictive deviance, for the evaluation of probabilistic forecasts and the critique of statistical models for count data.
Selecting and estimating regular vine copulae and application to financial returns
Pair-Copula Constructions of Multivariate Copulas
- C. Czado
- Computer Science
This survey introduces and discusses the pair-copula construction method to build flexible multivariate distributions, which includes drawable, canonical and regular vines and can be applied to model complex dependencies.
A mixed copula model for insurance claims and claim sizes
A crucial assumption of the classical compound Poisson model of Lundberg for assessing the total loss incurred in an insurance portfolio is the independence between the occurrence of a claim and its…
Risk management with high-dimensional vine copulas: An analysis of the Euro Stoxx 50
This work develops a regular vine copula based factor model for asset returns, the Regular Vine Market Sector model, which is motivated by the classical CAPM and shown to be superior to the CAVA model proposed by Heinen and Valdesogo (2009).
Truncated regular vines in high dimensions with application to financial data
This work proposes using statistical model selection techniques to either truncate or simplify a regular vine copula using a multivariate copula as previously treated by Heinen & Valdesogo ( 2009 and 2009).
Maximum likelihood estimation of mixed C-vines with application to exchange rates
A novel data driven sequential selection procedure is proposed, which selects both the C-vine structure and its attached pair-copula families with parameters and maximum likelihood (ML) estimation of the parameters is facilitated using the sequential estimates as starting values.
Analyzing Dependent Data with Vine Copulas
- C. Czado
- Computer ScienceLecture Notes in Statistics
- 15 May 2019
This textbook provides a step-by-step introduction to the class of vine copulas, their statistical inference and applications, and demonstrates how the R package VineCopula can be used to explore and build statistical dependence models from scratch.
Pair Copula Constructions for Multivariate Discrete Data
This study introduces a new class of models for multivariate discrete data based on pair copula constructions (PCCs) that has two major advantages; it is shown that discrete PCCs attain highly flexible dependence structures and the high quality of inference function for margins and maximum likelihood estimates is demonstrated.