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Storm after the Gloomy days: Influences of COVID-19 pandemic on volatility of the energy market

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  • Ha, Le Thanh

Abstract

Volatility is a common phenomenon in the energy market, but COVID-19 has cast a dark shadow over this characteristic. In light of this observation, individuals might have an incorrect impression of the impact of this shock on the energy markets. By applying a time-varying parameter vector autoregression (TVP-VAR) in combination with an extended joint connectedness approach to identify the sources of the energy market's volatility, we characterize the influences of COVID-19 health crisis and the volatility of the crude oil and precious metals (including gold and silver) market on the volatility of the energy market starting from January 1, 2020, to December 31, 2021. The total connectedness index, the net total, and pairwise directional connectedness measures obtained from the extended TVP-VAR allow us to monitor interlinkages from various variables in a designed network. The novel method has the benefit of distinguishing between a net recipient and a net transmitter. Our results demonstrate that the COVID-19 pandemic shocks first absorb the volatility from the energy and precious market to cause lagged but more severe consequences returning to these markets. Furthermore, there is a time-variant of system-wide interlinkages. Net total directional connectedness suggests that the oil and gold markets consistently appear to be a net transmitter of spillover shocks in the energy market. The COVID-19 pandemic shock first plays the role of shock receiver from other markets. However, this uncertainty shock acts as a shock transmitter, and its effects seem to be delayed but persistent for an extended period, thus making the energy and precious metal markets more volatile.

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  • Ha, Le Thanh, 2022. "Storm after the Gloomy days: Influences of COVID-19 pandemic on volatility of the energy market," Resources Policy, Elsevier, vol. 79(C).
    • Handle: RePEc:eee:jrpoli:v:79:y:2022:i:c:s0301420722003658
    DOI: 10.1016/j.resourpol.2022.102921
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    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • F3 - International Economics - - International Finance
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • Q43 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy and the Macroeconomy

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