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.
Highlights
During the last two decades, the interest of investors in energy commodities has increased, especially during financial and economic recessions
The increasing use of energy commodities for speculation and risk diversification purposes has resulted in significant shifts in prices of these assets and requires reliable estimates of their daily volatility and correlations
Conditional Correlation (DCC) model with a mixed-frequency approach in the univariate specifications to assess the effects of the COVID-19 pandemic in determining the correlation among energy markets
Summary
During the last two decades, the interest of investors in energy commodities has increased, especially during financial and economic recessions. This process, referred to as the financialization of energy commodities, along with the deregulation of the over the counter markets, has created significant shifts in commodity returns volatility Univariate models are not able to explain dependences in the dynamics of the volatility of energy commodities. For this reason, many studies employ multivariate
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