Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/87187
Authors: 
Ji, Jiangyu
Lucas, Andre
Year of Publication: 
2012
Series/Report no.: 
Tinbergen Institute Discussion Paper 12-055/2/DSF35
Abstract: 
We propose a new semiparametric observation-driven volatility model where the form of the error density directly influences the volatility dynamics. This feature distinguishes our model from standard semiparametric GARCH models. The link between the estimated error density and the volatility dynamics follows from the application of the generalized autoregressive score framework of Creal, Koopman, and Lucas (2012). We provide simulated evidence for the estimation efficiency and forecast accuracy of the new model, particularly if errors are fat-tailed and possibly skewed. In an application to equity return data we find that the model also does well in density forecasting.
Subjects: 
volatility clustering
Generalized Autoregressive Score model
kernel density estimation
density forecast evaluation
JEL: 
C10
C14
C22
Persistent Identifier of the first edition: 
Document Type: 
Working Paper

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