Vine-copula GARCH model with dynamic conditional dependence

DOI10.1016/j.csda.2013.08.008zbMath1506.62170OpenAlexW3124798671MaRDI QIDQ1623562

Mike K. P. So, Cherry Y. T. Yeung

Publication date: 23 November 2018

Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.csda.2013.08.008

zbMATH Keywords

copulaGARCHtime varying dependencevine decomposition

Mathematics Subject Classification ID

Computational methods for problems pertaining to statistics (62-08) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Applications of statistics to actuarial sciences and financial mathematics (62P05) Measures of association (correlation, canonical correlation, etc.) (62H20) Characterization and structure theory for multivariate probability distributions; copulas (62H05)

Related Items (11)

The shifting dependence dynamics between the G7 stock markets ⋮ The copula directional dependence by stochastic volatility models ⋮ Vine copula statistical disclosure control for mixed-type data ⋮ Dynamic D-vine copula model with applications to Value-at-Risk (VaR) ⋮ Spline based Hermite quasi-interpolation for univariate time series ⋮ Vine copula approximation: a generic method for coping with conditional dependence ⋮ DYNAMIC ASSET CORRELATIONS BASED ON VINES ⋮ A classification point-of-view about conditional Kendall's tau ⋮ Forecasting VaR and ES of stock index portfolio: a vine copula method ⋮ Modelling mortality dependence: an application of dynamic vine copula ⋮ Efficient estimation of high-dimensional dynamic covariance by risk factor mapping: applications for financial risk management

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• Modeling Longitudinal Data Using a Pair-Copula Decomposition of Serial Dependence

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