Simple Tests for Models of Dependence between Multiple Financial Time Series, with Applications to U.S. Equity Returns and Exchange Rates

By Xiaohong Chen, Yanqin Fan, Andrew J. Patton. 2004

155 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/128dc06ae71727b2971baca5814477f2a64b1ac0

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Citations in the corpus

Dynamic Copula Modelling for Value at Risk (2006, unknown): “This evidence confirms previous results for bivariate portfolios but with a different approach, since we used a VaR backtesting procedure instead of goodness-of-fit tests for copulas as in Chen, Fan, and Patton (2004).”
Multivariate autoregressive modeling of time series count data using copulas (2007, methodology): “We apply the goodness of fit test of Chen et al. (2004) to the Gaussian copula and we get a test statistic of −0.14, which follows a standard normal distribution under the null of normality.”
Estimation and Forecasting of Dynamic Conditional Covariance: A Semiparametric Multivariate Model (2009, background): “Since the seminal work of Engle (1982), there has developed a huge literature on modeling the timevarying volatility of economic data in univariate case.”
Copulae and operational risks (2008, unknown): “…∼ NegativeBinomial(0.33; 0.80) (5.2) 3For extensive Monte Carlo studies of copulas estimators, instead, we refer to Bouye’et al.(2000), Patton (2001), Chen et al.(2004) and Cherubini et al. (2004) and three DGPs for the Severity : Fi(Xij) ∼ Exponential(153304) (5.3) Fi(Xij) ∼ Gamma(0.2; 759717)…”
Estimation and Model Selection of Copulas with an Application to Exchange Rates (2007, background): “Examples are Chen et al. (2004), Chen and Fan (2005), Genest et al. (2006) and Fermanian (2003).”
Goodness-of-Fit Tests for Copulas of Multivariate Time Series (2017, unknown): “It is the case for general BEKK models, used in Patton (2006) and Dias and Embrechts (2009). In what follows, one starts, in Section 2, by describing the model and discussing parameter estimation for copulas.”
An empirical analysis of multivariate copula models (2009, background): “( 2004 ) or recently Berg and Bakken ( 2006 ), the main idea is to project the multivariate problem onto a set of independent and uniform U (0, 1) variables, given the multivariate distribution and to calculate the distance (e.”
Estimating the tail-dependence coefficient: Properties and pitfalls (2005, methodology): “For instance, in the case of empirical marginal distributions and specific copula, we suggest to test the goodness-of-fit of the copula via (non-)parametric procedures such as those developed in Fermanian (2003), Chen et al. (2003), Dobrić and Schmid (2004) or Genest et al. (2006). Further, if one m”
Analysis of inter-gauge dependence by Kendall’s τK, upper tail dependence coefficient, and 2-copulas with application to rainfall fields (2008, background): “The reduction of the number of considered families could be useful, especially in regional analyses, when formal tests of goodness of fit (e.g., Chen et al. 2004; Fermanian 2005; Panchenko 2005; Genest et al. 2006)”
Estimation of Copula-Based Semiparametric Time Series Models (2006, unknown): “In different contexts, (1) Chen et al. (2003) propose two simple tests for the correct specification of a parametric copula in the context of modeling the contemporaneous dependence between several univariate time series 12 and of the innovations of univariate GARCH models used to filter each…”
MODELLING ASYMMETRIC EXCHANGE RATE DEPENDENCE a (2006, background): “The normal copula may be considered the benchmark copula in economics, though Chen et al. (2004) find evidence against the bivariate normal copula for many exchange rates.”
Goodness-of-fit tests for parametric families of Archimedean copulas (2008, methodology): “Some use tests based on the probability integral transform introduced by Rosenblatt (1952) such as Chen et al. (2004) and Berg and Bakken (2005).”