Doubly Multiplicative Error Models with Long- and Short-run Components
Statistical Finance
2020-06-08 v1
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.
Cite
@article{arxiv.2006.03458,
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 preprint arXiv:2006.03458},
year = {2020}
}