 Multivariate stochastic volatility for herding detection: Evidence from the energy sectorMike G. Tsionas, Dionisis Philippas and Nikolaos Philippas Energy Economics, 2022, vol. 109, issue C 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. Keywords: Herding; Stochastic volatility; Early-warning mechanism; Energy sector; Oil prices (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:eneeco:v:109:y:2022:i:c:s0140988322001402 DOI: 10.1016/j.eneco.2022.105964 Access Statistics for this article Energy Economics is currently edited by R. S. J. Tol, Beng Ang, Lance Bachmeier, Perry Sadorsky, Ugur Soytas and J. P. Weyant More articles in Energy Economics from Elsevier |
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