Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/258228 
Year of Publication: 
2021
Citation: 
[Journal:] Risks [ISSN:] 2227-9091 [Volume:] 9 [Issue:] 8 [Article No.:] 144 [Publisher:] MDPI [Place:] Basel [Year:] 2021 [Pages:] 1-20
Publisher: 
MDPI, Basel
Abstract: 
This paper shows the effects of the COVID-19 pandemic on energy markets. We estimate daily volatilities and correlations among energy commodities relying on a mixed-frequency approach that exploits information from the number of weekly deaths related to COVID-19 in the United States. The mixed-frequency approach takes advantage of the MIxing-Data Sampling (MIDAS) methods. We compare our results to those obtained by employing two well-known models that do not account for the COVID-19 low-frequency variable, namely the Dynamic EquiCorrelation (DECO) and corrected Dynamic Conditional Correlation (cDCC). Moreover, we consider four possible specifications of the volatility: GARCH, GJR, GARCH-MIDAS, and Double-Asymmetric GARCH-MIDAS. The empirical results show that our approach is statistically superior to other models and represents a valuable methodology that can be used for risk managers, investors, and policy makers to assess the effects of the pandemic on spillovers effects in energy markets.
Subjects: 
conditional covariance matrix
COVID-19
mixed-data sampling
volatility
Persistent Identifier of the first edition: 
Creative Commons License: 
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Document Type: 
Article
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