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)

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
has 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' named entity 'logic' named entity 'MIxed-DAta Sampling' named entity 'Multiplicative Error' named entity 'parameter vector' named entity 'conditional variance' named entity 'conditional expectation' named entity 'volatility' named entity 'DMEM' named entity 'standard errors' named entity 'Engle' named entity 'Engle' named entity 'shape parameter' named entity 'shape parameter' named entity 'S&P 500' named entity 'Maximum Likelihood' named entity 'p-values' named entity 'discrete time' named entity 'standard deviation' named entity 'volatility' named entity 'consistent estimator' named entity 'GARCH' named entity 'DMEM' named entity 'DMEM' named entity 'QML' named entity 'realized kernel' named entity 'smoothing spline' named entity 'matrices' named entity 'realized kernel' named entity 'error term' named entity 'volatility' named entity 'GARCH' named entity 'DMEM' named entity 'Engle' named entity 'DMEM' named entity 'volatility' named entity 'significance level' named entity 'correlation coefficient' named entity 'parameter vector' named entity 'daily information' named entity 'Lognormal' named entity 'digamma function' named entity 'Maximum Likelihood' named entity 'GARCH' named entity 'volatility' named entity 'estimator' named entity 'time series' named entity 'subperiod' named entity 'out-of-sample' named entity 'business cycle' named entity 'error distribution' named entity 'Federal Reserve Economic Data' named entity 'Method of Moments' named entity 'volatility' named entity 'GARCH' named entity 'regression spline' named entity 'realized kernel' named entity 'realized volatility' named entity 'GARCH' named entity 'realized volatility' named entity 'score function' named entity 'stationarity' named entity 'volatility' named entity 'QML' named entity 'percentage change' named entity 'volatility' named entity 'GARCH' named entity 'out-of-sample' named entity 'GARCH' named entity 'Engle' named entity 'conditional expectation' named entity 'Hessian matrices' named entity 'loss functions' named entity 'volatility' named entity 'realized kernel' named entity 'asymptotic variance' named entity 'conditional variance' named entity 'asymptotic variance' named entity 'financial econometrics' named entity 'shape parameter'

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