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Nonlinear dependence and information spillover between electricity and fuel source markets: New evidence from a multi-scale analysis

Xia, Tongshui ; Ji, Qiang ; Geng, Jiang-Bo

Physica A, 2020-01, Vol.537, p.122298, Article 122298 [Periódico revisado por pares]

Elsevier B.V

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  • Título:
    Nonlinear dependence and information spillover between electricity and fuel source markets: New evidence from a multi-scale analysis
  • Autor: Xia, Tongshui ; Ji, Qiang ; Geng, Jiang-Bo
  • Assuntos: Connectedness network ; Electricity prices ; Ensemble empirical mode decomposition ; Fuel prices ; Nonlinear causality test
  • É parte de: Physica A, 2020-01, Vol.537, p.122298, Article 122298
  • Descrição: This study identifies nonlinear causality between electricity and fuel source returns based on a new multi-scale framework. The empirical results show that the causality direction between electricity and fuel returns behaves differently across time scales. Generally speaking, linear or nonlinear causality relationship between the electricity and gas markets exists for most time scales. The study also explores the magnitude of the feedback effects between electricity and fuel returns using the connectedness network. The results show that the degree of feedback effects between the electricity and fuel markets is relatively weak at the original data level. Across various time scales, there is positive but weak information spillover from the oil and uranium to electricity markets and reciprocal information spillover between the electricity and gas/coal markets at most time horizons. The information spillover from the gas to electricity returns is relatively strong. •Multi-scale nonlinear causality between PJM electricity and fuel returns is explored.•Information spillover between them is examined using connectedness network.•Causality direction between them behave differently across time horizons.•Positive weak spillover from oil and uranium to electricity returns exists.•Information spillover from the gas to electricity returns is relatively strong.
  • Editor: Elsevier B.V
  • Idioma: Inglês
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  • Realização: ABCD-USP USP   Logos de Redes Sociais: Freepik

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