Nonparametric Estimation of Copulas for Time Series

By Jean-David Fermanian, Olivier Scaillet. 2002

261 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/d77186d8f8a9cf76cc6463a632fa9b40211ace13

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Citations in the corpus

A review of copula models for economic time series (2012, methodology): “Nonparametric copula estimation using time series data is studied by Fermanian and Scaillet [56], Fermanian et al.”
Modelling the dependence structure of financial assets : A survey of four copulas (2004, unknown): “Finally, a more informal test involves comparing the plot of the estimated parametric copula with the one of a copula function estimated non-parametrically via kernel methods (Fermanian and Scaillet, 2002).”
MODELLING ASYMMETRIC EXCHANGE RATE DEPENDENCE a (2006, background): “5 See Frees et al. (1996), Bouyé et al. (2000a, 2000b), Cherubini and Luciano (2001, 2002), Costinot et al. (2000), Li (2000), Fermanian and Scaillet (2003), Embrechts et al. (2001), Granger et al. (forthcoming), Frey and McNeil (2001), Rockinger and Jondeau (2001), Sancetta and Satchell (2001),…”
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.”
Nonparametric estimation of copula functions for dependence modelling (2007, unknown): “However, we are able to obtain tractable expressions for the bias and variance, which add to the existing convergence results for copula density estimation (Gijbels and Mielnicnuk, 1990; Fermanian, Radulovic and Wegkamp, 2004) and for copula estimation (Fermanian and Scaillet, 2003), and provide a f”
On the Out-of-Sample Importance of Skewness and Asymmetric Dependence for Asset Allocation (2004, unknown): “In economics and finance, see Li (2000), Embrechts, Höing, and Juri (2001), Patton (2001a, 2001b), Rockinger and Jondeau (2001), Chen and Fan (2002a, 2002b), Mashal and Zeevi (2002), Miller and Liu (2002), Junker and May (2002), Fermanian and Scaillet (2003), and Rosenberg (2003).”
Probit Transformation for Nonparametric Kernel Estimation of the Copula Density (2014, methodology): “Moreover, Fermanian and Scaillet [20], Chen and Huang [10] and Omelka, Gijbels and Veraverbeke [43] studied kernel methods to obtain flexible smooth estimates of the bivariate 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 Copula‐Based Non‐parametric Measure of Regression Dependence (2013, background): “Theorem 3 can be generalized to dependent data under suitable mixing properties of the data generating process (Fermanian & Scaillet, 2003, assumptions 3 and 4).”
Non-Parametric Pricing of Multivariate Contingent Claims (2003, unknown): “See Fermanian and Scaillet (2000) for a nonparametric copula estimation technique.”
A kolmogorov‐smirnov type test for positive quadrant dependence (2005, methodology): “Note that Theorem 4 of Fermanian, Radulovic & Wegkamp (2004) implies that almost every observed sample has the property that”