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

Author

Listed:
  • 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)

  • Lea Petrella

    (MEMOTEF Department, Sapienza University of Rome, 00161 Rome, RM, Italy)

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.

Suggested Citation

  • Mila Andreani & Vincenzo Candila & Giacomo Morelli & Lea Petrella, 2021. "Multivariate Analysis of Energy Commodities during the COVID-19 Pandemic: Evidence from a Mixed-Frequency Approach," Risks, MDPI, vol. 9(8), pages 1-20, August.
    • Handle: RePEc:gam:jrisks:v:9:y:2021:i:8:p:144-:d:612252
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    References listed on IDEAS

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