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Estimation and Inference in Univariate and Multivariate Log-GARCH-X Models When the Conditional Density is Unknown

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  • Sucarrat, Genaro
  • Grønneberg, Steffen
  • Escribano, Alvaro

Abstract

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.

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Bibliographic Info

Paper provided by University Library of Munich, Germany in its series MPRA Paper with number 49344.

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Date of creation: 11 Aug 2013
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Handle: RePEc:pra:mprapa:49344

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Keywords: ARCH; exponential GARCH; log-GARCH; ARMA-X; Multivariate GARCH;

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Cited by:
  1. Sucarrat, Genaro & Escribano, Alvaro, 2013. "Unbiased QML Estimation of Log-GARCH Models in the Presence of Zero Returns," MPRA Paper 50699, University Library of Munich, Germany.

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