Nonparametric estimation of copula functions for dependence modelling

By S. Chen, Tzee-Ming Huang. 2007

197 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Finance, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/4cdce7b5969694eaaaf1ac5e9bc95301040e269e

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Citations in the corpus

Probit Transformation for Nonparametric Kernel Estimation of the Copula Density (2014, methodology): “Moreover, Fermanian and Scaillet (2003), Chen and Huang (2007), Omelka et al (2009) and Gijbels et al (2010) studied kernel methods to obtain ﬂexible smooth estimates of the bivariate cdf C .”
Flexible Copula Density Estimation with Penalized Hierarchical B‐splines (2013, methodology): “Considering the literature on non-parametric copula estimation, we refer to kernel density methods proposed in Gijbels & Mielniczuk (1990) that are further discussed in Fermanian & Scaillet (2003), Fermanian et al. (2004) and Chen & Huang (2007).”
A note on bootstrap approximations for the empirical copula process (2010, unknown): “See also Chen and Huang (2007) or Omelka, Gijbels and Veraverbeke (2009) for a smoothed version of this process.”
STATISTICAL INFERENCE FOR MULTIVARIATE RESIDUAL COPULA OF GARCH MODELS (2009, unknown): “For example, in the context of nonparametric copulas, Fermanian, Radulovic and Wegkamp (2004) considered empirical copula estimation, while Fermanian and Scaillet (2003) and Chen and Huang (2007) proposed kernel smoothing.”
Copula based analysis of rainfall severity and duration: a case study (2013, methodology): “Finally, the broad range of methods considered in this paper are essentially parametric and there remains the opportunity of applying a nonparametric approach, as suggested in Xi Chen and Huang (2007), to model rainfall characteristics based on copulas.”
Applying copula models to individual claim loss reserving methods (2010, methodology): “More recently, Chen and Huang (2007) proposed a bivariate estimator based on the local linear estimator, and Morettin et al. (2006) proposed an empirical copulas wavelet estimator.”
Asymptotics of empirical copula processes under non-restrictive smoothness assumptions (2010, background): “For instance, the empirical copula is a basic building block when estimating copula densities (Chen and Huang [3], Omelka, Gijbels and Veraverbeke [22]) or dependence measures and functions (Schmid et al. [27], Genest and Segers [14]), for testing for independence (Genest and Rémillard [12], Genest,”
Dependence modeling in non-life insurance using the Bernstein copula (2012, unknown): “There are alternatives that may provide better performance; for example, Chen and Huang (2007) propose local-linear kernels and Bouezmarni and Rombouts (2009) suggest a semi-parametric setupwith non-parametricmargins and parametric copula functions.”
A Copula‐Based Non‐parametric Measure of Regression Dependence (2013, methodology): “A function μ : X → [0, 1] is a measure of regression dependence (MRD) with respect to 4 if it satisfies the following conditions: (M1) (X, Y ) 4 (X̃, Ỹ ) implies μ(X, Y ) ≤ μ(X̃, Ỹ ); (M2) μ(X,Y ) = 1 if and only if Y is a.”