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

Alessandra Amendola (), Vincenzo Candila, Fabrizio Cipollini and Giampiero Gallo ()

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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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Handle: RePEc:arx:papers:2006.03458