2012/07/06 by Marius Hofert, Hofert, Marius, Martin Maechler +3
Decision Sciences · Economics, Econometrics and Finance · #62F10 #62H12 #62H20 #62H99 #65C60 #Computation (stat.CO) #FOS: Computer and information sciences #FOS: Mathematics #Financial Risk and Volatility Modeling #Numerical Analysis (math.NA) #Other Statistics (stat.OT) #Probability and Risk Models #Stochastic processes and financial applications
paper · pdf · doi:10.48550/arxiv.1207.1708
openalex publication_date 2012/07/06 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
The performance of known and new parametric estimators for Archimedean copulas is investigated, with special focus on large dimensions and numerical difficulties. In particular, method-of-moments-like estimators based on pairwise Kendall's tau, a multivariate extension of Blomqvist's beta, minimum distance estimators, the maximum-likelihood estimator, a simulated maximum-likelihood estimator, and a maximum-likelihood estimator based on the copula diagonal are studied. Their performance is compared in a large-scale simulation study both under known and unknown margins (pseudo-observations), in small and high dimensions, under small and large dependencies, various different Archimedean families and sample sizes. High dimensions up to one hundred are considered for the first time and computational problems arising from such large dimensions are addressed in detail. All methods are implemented in the open source \R package \pkgcopula and can thus be easily accessed and studied.