Estimation of Copula-Based Semiparametric Time Series Models

By Xiaohong Chen, Yanqin Fan. 2006

494 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Finance, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/84a7aad937bdfe4bb7086b3a42195debc44c5ad5

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Citations in the corpus

COPULA-BASED CHARACTERIZATIONS FOR HIGHER ORDER MARKOV PROCESSES (2009, methodology): “The problems of copula theory and its applications in the multivariate and time series context have been considered, in particular, in the following papers. Joe (1987, 1989) proposed multivariate extensions of Pearson’s coefficient and the Kullback-Leibler and Shannon mutual information. Nelsen (199”
Efficient Estimation of Copula-Based Semiparametric Markov Models (2009, methodology): “(1) Under Assumption M, the time series {Yt}nt=1 is strictly stationary ergodic and is also β-mixing (see, e.g., Bradley [(2005), Corollary 3.6] and Chen and Fan (2006)).”
Copula Methods for Forecasting Multivariate Time Series (2013, methodology): “The lower panel of Table 9 presents the corresponding Chen and Fan (2006b) tests for the semiparametric copula-based multivariate models, and the same conclusions are obtained.”
A Goodness-of-fit Test for Copulas (2014, methodology): “Now, Chen and Fan (2004) show that √ T (θ̂ − θo)→ N(0, B−1ΣB−1), (9)”
Vine Copula Specifications for Stationary Multivariate Markov Chains (2015, methodology): “This generalizes the approach of Chen and Fan (2006), who considered the univariate case m D 1.”
MODELLING ASYMMETRIC EXCHANGE RATE DEPENDENCE a (2006, background): “…(2003), Embrechts et al. (2001), Granger et al. (forthcoming), Frey and McNeil (2001), Rockinger and Jondeau (2001), Sancetta and Satchell (2001), Smith (2003), Rodriguez (2003), Rosenberg (2003), Cherubini et al. (2004), Patton (2004a), Rosenberg and Schuermann (2004), and Chen and Fan (2006).”
A review of copula models for economic time series (2012, methodology): “The second estimate is that of [26], which does account for the estimation error due to the EDF.”
COPULAS AND TEMPORAL DEPENDENCE (2008, methodology): “Chen and Fan (2006) claim that Clayton copulas generate a rate of β-mixing that is no slower than polynomial.”
Parameter estimation for pair-copula constructions (2013, methodology): “In practice, the parameter estimation often includes a preliminary step to deal with deviations from these assumptions (Chen and Fan [5]), for instance GARCH filtration of time series data.”
Copula-Based Models for Financial Time Series (2009, background): “Chen and Fan (2006a) consider the estimation of semiparametric copula-based univariate time series models, where the unconditional distribution is estimated nonparametrically and the copula is estimated parametrically.”
Modeling Dependence in High Dimensions With Factor Copulas (2015, background): “1 T (1 q) PT t=1 1 n F̂i (̂it) > q; F̂j ̂jt > q o ; q 2 (0:5; 1) (11)”
Granger-causality in quantiles between financial markets: Using copula approach ☆ (2014, methodology): “In the second stage, we estimate the copula parameters following Chen and Fan (2006b).”
Statistical Dependence: Beyond Pearson’s ρ (2018, background): “The estimation can be done in two steps, cf. also Chen and Fan (2006).”