Multivariate Analysis of Energy Commodities during the COVID-19 Pandemic: Evidence from a Mixed-Frequency Approach

Mila Andreani, Vincenzo Candila, Giacomo Morelli and Lea Petrella
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Mila Andreani: Scuola Normale Superiore, 56126 Pisa, PI, Italy
Vincenzo Candila: MEMOTEF Department, Sapienza University of Rome, 00161 Rome, RM, Italy
Giacomo Morelli: Department of Statistical Sciences, Sapienza University of Rome, 00161 Rome, RM, Italy

Risks, 2021, vol. 9, issue 8, 1-20

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.

Keywords: COVID-19; conditional covariance matrix; volatility; mixed-data sampling (search for similar items in EconPapers)
JEL-codes: C G0 G1 G2 G3 K2 M2 M4 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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