Doubly Multiplicative Error Models with Long- and Short-run Components
Alessandra Amendola (),
Vincenzo Candila,
Fabrizio Cipollini and
Giampiero Gallo ()
Papers from arXiv.org
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.
Date: 2020-06
New Economics Papers: this item is included in nep-ecm, nep-ets and nep-ore
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Journal Article: Doubly multiplicative error models with long- and short-run components (2024) 
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Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:2006.03458
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