• DOI:10.2139/ssrn.3618157
• Corpus ID: 219401487

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

@article{Amendola2020DoublyME,
  title={Doubly Multiplicative Error Models with Long– and Short–run Components},
  author={Alessandra Amendola and Vincenzo Candila and Fabrizio Cipollini and Giampiero M. Gallo},
  journal={arXiv: Statistical Finance},
  year={2020}
}

• A. Amendola, V. Candila, +1 author G. Gallo
• Published 5 June 2020
• Economics
• arXiv: Statistical Finance

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.