Are low-frequency data really uninformative? A forecasting combination perspective
Li Liu and
The North American Journal of Economics and Finance, 2018, vol. 44, issue C, 92-108
In this study, we investigate whether low-frequency data improve volatility forecasting when high-frequency data are available. To answer this question, we utilize four forecast combination strategies that combine low-frequency and high-frequency volatility models and employ a rolling window and a range of loss functions in the framework of the novel Model Confidence Set test. Out-of-sample results show that combination forecasts with GARCH-class models can achieve high forecast accuracy. However, the combination forecast methods appear not to significantly outperform individual high-frequency volatility models. Furthermore, we find that models that combine low-frequency and high-frequency volatility yield significantly better performance than other models and combination forecast strategies in both a statistical and economic sense.
Keywords: Volatility forecasting; Realized volatility; Combine forecasts; Forecasting evaluation (search for similar items in EconPapers)
JEL-codes: C22 C52 C53 (search for similar items in EconPapers)
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5) Track citations by RSS feed
Downloads: (external link)
Full text for ScienceDirect subscribers only
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:eee:ecofin:v:44:y:2018:i:c:p:92-108
Access Statistics for this article
The North American Journal of Economics and Finance is currently edited by Hamid Beladi
More articles in The North American Journal of Economics and Finance from Elsevier
Bibliographic data for series maintained by Haili He ().