Estimation and empirical performance of non-scalar dynamic conditional correlation models

DOI10.1016/j.csda.2015.02.013zbMath1466.62025OpenAlexW2119365391MaRDI QIDQ1659096

Lyudmila Grigoryeva, Juan-Pablo Ortega, Luc Bauwens

Publication date: 15 August 2018

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

Full work available at URL: http://hdl.handle.net/2078.1/143967

zbMATH Keywords

constrained optimizationBregman divergencesBregman-proximal trust-region methoddynamic conditional correlations (DCC)multivariate volatility modelingnon-scalar DCC models

Mathematics Subject Classification ID

Computational methods for problems pertaining to statistics (62-08) Applications of statistics to actuarial sciences and financial mathematics (62P05)

Related Items (3)

Correlation structure regularization via entropy loss function for high-dimension and low-sample-size data ⋮ Multivariate leverage effects and realized semicovariance GARCH models ⋮ Nonlinearities and regimes in conditional correlations with different dynamics

Cites Work

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• The Model Confidence Set
• Heteroskedasticity and Autocorrelation Consistent Covariance Matrix Estimation
• Multivariate GARCH estimation via a Bregman-proximal trust-region method
• Generalized autoregressive conditional heteroscedasticity
• Multivariate rotated ARCH models
• A Generalized Dynamic Conditional Correlation Model: Simulation and Application to Many Assets
• Matrix Nearness Problems with Bregman Divergences

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