Efficient Estimation of Semiparametric Multivariate Copula Models

By Xiaohong Chen, Yanqin Fan, Viktor Tsyrennikov. 2006

193 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Finance, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/f6345af331daafbbe94f3e08d2e9dd9c651faa6e

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Citations in the corpus

Regularized rank-based estimation of high-dimensional nonparanormal graphical models (2012, background): “The semiparametric Gaussian copula model is a nice combination of ﬂexibility and interpretability, and it has generated a lot of interests in statistics, econometrics and ﬁnance; see Song (2000), Chen and Fan (2006), Chen, Fan and Tsyrennikov (2006) and references therein.”
Goodness-of-fit tests for copulas: A review and a power study (2006, background): “See Klaassen and Wellner (1997) for a notable exception and Tsukahara (2005) or Chen et al. (2006) for other rank-based estimators.”
Construction of asymmetric multivariate copulas (2008, methodology): “An efficient estimation method for parametric classes of copula densities is introduced and investigated in [3].”
Applying copula models to individual claim loss reserving methods (2010, methodology): “In addition, Tsukahara (2007) proposed pseudo Z-estimator and Chen et al. (2006) utilized the sieve maximum likelihood.”
A review of copula models for economic time series (2012, methodology): “[27] show that efficient estimation of this semiparametric copula-based model can be obtained by using the method of sieves for the marginal distributions and estimating these along with the copula parameter in a single estimation step.”
Inhomogeneous Dependence Modeling with Time-Varying Copulae (2009, unknown): “model specification see Chen and Fan (2006a), Chen and Fan (2006b) and Chen et al. (2006) .”
Copula-Based Models for Financial Time Series (2009, background): “See Chollete, et al. (2005) and Arakelian and Dellaportas (2005) for alternative approaches.”
Copulas in Econometrics (2014, background): “Fmin,Z(z) = sup x∈R max(F (x) +G(z − x)− 1, 0) and (17) Fmax,Z(z) = 1 + inf x∈R min(F (x) +G(z − x)− 1, 0).”