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Multivariate stochastic volatility for herding detection: Evidence from the energy sector

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  • Tsionas, Mike G.
  • Philippas, Dionisis
  • Philippas, Nikolaos

Abstract

The paper proposes a multivariate asymmetric stochastic volatility approach, allowing for common factors that detect and measure herding behavior conditional on the stylized facts of asset returns and another factor that captures non-herding behavior. Applying our approach to the constituents of the S&P 500 energy sector in periods of high uncertainty, the findings reveal a wealth of information on herding detection related to asset returns' co-movements and volatility encountered by the energy sector. We also examine to what degree macroeconomic indicators' uncertainty influences the common factors on herding detection. We conclude that stylized facts of energy assets experience significant changes, arising from the increased systemic market risk and crude oil prices that are exposed to.

Suggested Citation

  • Tsionas, Mike G. & Philippas, Dionisis & Philippas, Nikolaos, 2022. "Multivariate stochastic volatility for herding detection: Evidence from the energy sector," Energy Economics, Elsevier, vol. 109(C).
    • Handle: RePEc:eee:eneeco:v:109:y:2022:i:c:s0140988322001402
    DOI: 10.1016/j.eneco.2022.105964
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    More about this item

    Keywords

    Herding; Stochastic volatility; Early-warning mechanism; Energy sector; Oil prices;
    All these keywords.

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C40 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - General
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation
    • Q40 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - General

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