Are low-frequency data really uninformative? A forecasting combination perspective

Feng Ma, Yu Li, Li Liu and Yaojie Zhang

The North American Journal of Economics and Finance, 2018, vol. 44, issue C, 92-108

Abstract: 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)
Date: 2018
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Citations: View citations in EconPapers (18)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:ecofin:v:44:y:2018:i:c:p:92-108

DOI: 10.1016/j.najef.2017.11.006

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