Test of independence and randomness based on the empirical copula process

By Christian Genest, Bruno R'emillard. 2004

297 citations recorded by the source corpus.

Subfields: Finance, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/cf1de36a53c0938a9e4e60ebed5bafc11da28025

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Citations in the corpus

A review of copula models for economic time series (2012, background): “[54] and Genest and Rémillard [70] consider rank-based tests of independence for serially dependent data.”
Quantile Spectral Processes: Asymptotic Analysis and Inference (2014, background): “roaches were taken by many authors, and an overview of related results can be found in Tjøstheim [53] and Hong [26]. Copula-based tests of serial independence were considered by Genest and R´emillard [17], among others. Linton and Whang [41] introduced the socalled quantilogram, deﬁned as the autoco”
Modeling Multivariate Distributions with Continuous Margins Using the copula R Package (2010, methodology): “The test based on the former tends to give the best results and was found to frequently outperform the test based on I n in the Monte Carlo experiments carried out by Genest and R´emillard (2004) and Kojadinovic and Holmes (2009).”
Derivation of low flow distribution functions using copulas (2014, background): “(1), while q0 and k were assumed to be constants (Gottschalk and Perzyn, 1989; Gottschalk et al., 1997; Pacheco et al., 2006). These are of course very strong assumptions that might need to be relaxed. Perzyna and Gottschalk (1995) noticed the problem of variation in the coefficient k in analysing r”
A Primer on Copulas for Count Data (2007, methodology): “This fact has been used, e.g., by Genest and Rémillard (2004) to derive powerful nonparametric tests of independence based on the empirical copula process.”
On the least squares estimation of multiple-regime threshold autoregressive models (2012, methodology): “To check this fact empirically in finite samples, the multivariate independence test is used, which is based on the empirical copula process and is proposed by Genest and Rémillard (2004).”
A multivariate version of Hoeffding's Phi-Square (2010, methodology): “Note that the test by Genest and Rémillard [21] (cf.”
Copula-Based Measures of Multivariate Association (2010, background): “[6], Genest and Rémillard [40] as well as Genest et al.”