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Unraveling Timing Uncertainty of Event-driven Connectedness among Oil-Based Energy Commodities

Author

Listed:
  • Daniel Bartusek

    (Institute of Economic Studies, Faculty of Social Sciences, Charles University, Prague, Czech Republic)

  • Evzen Kocenda

    (Institute of Economic Studies, Faculty of Social Sciences, Charles University, Prague, Czech Republic; CESifo, Munich, Germany; IOS, Regensburg, Germany)

Abstract

Prices of oil-based commodities are often heavily influenced by the occurrence of various events covered in the news. We analyze over 900 events related to oil from 1978 to 2022 and group them based on a set of repeating characteristics. We quantify dynamic connectedness among energy commodities and use a novel bootstrap-after-bootstrap testing econometric framework to identify over 20 statistically significant historical events that triggered a sudden and lasting rise in volatility connectedness. We show that geopolitical events are linked with increases in connectedness much more often than economic events. Natural events do not exhibit a similar impact, though. The Majority of the events after which volatility connectedness increased share three common characteristics: they are negative, unexpected, and introduce fear of oil supply shortage.

Suggested Citation

  • Daniel Bartusek & Evzen Kocenda, 2023. "Unraveling Timing Uncertainty of Event-driven Connectedness among Oil-Based Energy Commodities," Working Papers IES 2023/35, Charles University Prague, Faculty of Social Sciences, Institute of Economic Studies, revised 2023.
    • Handle: RePEc:fau:wpaper:wp2023_35
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    File URL: https://ies.fsv.cuni.cz/en/unraveling-timing-uncertainty-event-driven-connectedness-among-oil-based-energy-commodities
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    More about this item

    Keywords

    energy commodities; crude oil; volatility connectedness; systemic events; bootstrapafter-bootstrap procedure;
    All these keywords.

    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
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets
    • Q02 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - General - - - Commodity Market
    • Q35 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Nonrenewable Resources and Conservation - - - Hydrocarbon Resources

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