The impact of general non-parametric volatility functions in multivariate GARCH models

DOI10.1016/j.csda.2005.06.006zbMath1445.62307OpenAlexW2074680283MaRDI QIDQ959389

Francesco Audrino

Publication date: 11 December 2008

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

Full work available at URL: https://doi.org/10.1016/j.csda.2005.06.006

zbMATH Keywords

dynamic conditional correlationsmultivariate GARCH modelsasymmetric nonlinear volatilityfunctional gradient descent (FGD) estimation

Mathematics Subject Classification ID

Applications of statistics to economics (62P20) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Applications of statistics to actuarial sciences and financial mathematics (62P05)

Related Items (7)

Forecasting correlations during the late-2000s financial crisis: the short-run component, the long-run component, and structural breaks ⋮ Value-at-risk forecasting based on Gaussian mixture ARMA–GARCH model ⋮ Functional modelling of volatility in the Swedish limit order book ⋮ Modelling volatility asymmetries: a Bayesian analysis of a class of tree structured multivariate GARCH models ⋮ An analysis of the flexibility of asymmetric power GARCH models ⋮ Accurate value-at-risk forecasting based on the normal-GARCH model ⋮ Robust optimal decisions with imprecise forecasts

Cites Work

• Regime switching for dynamic correlations
• A test for constant correlations in a multivariate GARCH model
• Generalized autoregressive conditional heteroscedasticity
• Tree-structured Generalized Autoregressive Conditional Heteroscedastic Models
• Two Stage and Related Estimators and Their Applications

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