About: Doubly Multiplicative Error Models with Long- and Short-run Components

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An Entity of Type : schema:ScholarlyArticle, within Data Space : covidontheweb.inria.fr associated with source document(s)

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type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
title	Doubly Multiplicative Error Models with Long- and Short-run Components
Creator	Gallo, G Amendola, A Candila, V Cipollini, F
source	ArXiv
abstract	We suggest the Doubly Multiplicative Error class of models (DMEM) for modeling and forecasting realized volatility, which combines two components accommodating low-, respectively, high-frequency features in the data. We derive the theoretical properties of the Maximum Likelihood and Generalized Method of Moments estimators. Two such models are then proposed, the Component-MEM, which uses daily data for both components, and the MEM-MIDAS, which exploits the logic of MIxed-DAta Sampling (MIDAS). The empirical application involves the S&P 500, NASDAQ, FTSE 100 and Hang Seng indices: irrespective of the market, both DMEM's outperform the HAR and other relevant GARCH-type models.
has issue date	2020-06-05(xsd:dateTime)
has license	arxiv
sha1sum (hex)	eeae4c447abb8f26db4427674801689ec15f335d
resource representing a document's title	Doubly Multiplicative Error Models with Long- and Short-run Components
resource representing a document's body	covid:eeae4c447abb8f26db4427674801689ec15f335d#body_text
is schema:about of	named entity 'DOUBLY' named entity 'USES' named entity 'COMPONENT' named entity 'realized volatility' named entity 'Maximum Likelihood' »more»

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