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Uncovering the nonlinear predictive causality between natural gas and electricity prices

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  • Uribe, Jorge M.
  • Guillen, Montserrat
  • Mosquera-López, Stephania

Abstract

We measure the directional predictability between electricity and natural gas prices at different quantiles of their respective price distributions. This reveals significant nonlinearities in the relationship that characterizes the interconnected gas and electricity markets of both New England and Pennsylvania-New Jersey-Maryland. We identify a double causality from gas to electricity and vice versa, which increases as their respective market prices rise. In general, this causality is decidedly higher for both price sets at market values at and above their median. The feedback effect from electricity to gas is stronger in the case of New England – where 50% of the power generation mix comprises natural-gas-fired plants – than it is in the case of Pennsylvania-New Jersey-Maryland – where only 24% of the generation mix relies on natural gas sources.

Suggested Citation

  • Uribe, Jorge M. & Guillen, Montserrat & Mosquera-López, Stephania, 2018. "Uncovering the nonlinear predictive causality between natural gas and electricity prices," Energy Economics, Elsevier, vol. 74(C), pages 904-916.
    • Handle: RePEc:eee:eneeco:v:74:y:2018:i:c:p:904-916
    DOI: 10.1016/j.eneco.2018.07.025
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    More about this item

    Keywords

    Natural gas; Electricity; Directional predictability; Quantiles; Cross-quantilogram;
    All these keywords.

    JEL classification:

    • Q40 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - General
    • L94 - Industrial Organization - - Industry Studies: Transportation and Utilities - - - Electric Utilities
    • L95 - Industrial Organization - - Industry Studies: Transportation and Utilities - - - Gas Utilities; Pipelines; Water Utilities
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes

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