STATISTICAL INFERENCE FOR COPULAS IN HIGH DIMENSIONS: A SIMULATION STUDY

Paul Embrechts A · Marius Hofert

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Financial Risk and Volatility Modeling Statistical Distribution Estimation and Applications Probability and Risk Models

Abstract

Abstract Statistical inference for copulas has been addressed in various research papers. Due to the complicated theoretical results, studies have been carried out mainly in the bivariate case, be it properties of estimators or goodness-of-fit tests. However, from a practical point of view, higher dimensions are of interest. This work presents the results of large-scale simulation studies with particular focus on the question to what extent dimensionality influences point and interval estimators.

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2005Quantitative Risk Management: Concepts, Techniques, and Tools 2007Goodness-of-fit tests for copulas: A review and a power study 1993Statistical Inference Procedures for Bivariate Archimedean Copulas 1995A Semiparametric Estimation Procedure of Dependence Parameters in Multivariate Families of Distributions 2010Copula Theory and Its Applications 2010Modeling Multivariate Distributions with Continuous Margins Using the copula R Package 2006Comparison of semiparametric and parametric methods for estimating copulas 2005Semiparametric estimation in copula models

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