Estimation and Inference in Univariate and Multivariate Log-GARCH-X Models When the Conditional Density is Unknown
Steffen Grønneberg and
Alvaro Escribano ()
MPRA Paper from University Library of Munich, Germany
Exponential models of Autoregressive Conditional Heteroscedasticity (ARCH) enable richer dynamics (e.g. contrarian or cyclical), provide greater robustness to jumps and outliers, and guarantee the positivity of volatility. The latter is not guaranteed in ordinary ARCH models, in particular when additional exogenous or predetermined variables ("X") are included in the volatility specification. Here, we propose estimation and inference methods for univariate and multivariate Generalised log-ARCH-X (i.e. log-GARCH-X) models when the conditional density is not known via (V)ARMA-X representations. The multivariate specification allows for volatility feedback across equations, and time-varying correlations can be fitted in a subsequent step. Finally, our empirical applications on electricity prices show that the model-class is particularly useful when the X-vector is high-dimensional.
Keywords: ARCH; exponential GARCH; log-GARCH; ARMA-X; Multivariate GARCH (search for similar items in EconPapers)
JEL-codes: C22 C32 C51 C52 (search for similar items in EconPapers)
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https://mpra.ub.uni-muenchen.de/49344/1/loggarch.pdf original version (application/pdf)
https://mpra.ub.uni-muenchen.de/57237/1/loggarch.pdf revised version (application/pdf)
https://mpra.ub.uni-muenchen.de/57238/1/MPRA_paper_57238.pdf revised version (application/pdf)
https://mpra.ub.uni-muenchen.de/62352/1/MPRA_paper_62352.pdf revised version (application/pdf)
Journal Article: Estimation and inference in univariate and multivariate log-GARCH-X models when the conditional density is unknown (2016)
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Persistent link: https://EconPapers.repec.org/RePEc:pra:mprapa:49344
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