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Energy Markets and CO2 Emissions: Analysis by Stochastic Copula Autoregressive Model

Author

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  • Vêlayoudom Marimoutou

    (GREQAM - Groupement de Recherche en Économie Quantitative d'Aix-Marseille - EHESS - École des hautes études en sciences sociales - AMU - Aix Marseille Université - ECM - École Centrale de Marseille - CNRS - Centre National de la Recherche Scientifique)

  • Manel Soury

    (GREQAM - Groupement de Recherche en Économie Quantitative d'Aix-Marseille - EHESS - École des hautes études en sciences sociales - AMU - Aix Marseille Université - ECM - École Centrale de Marseille - CNRS - Centre National de la Recherche Scientifique)

Abstract

We examine the dependence between the volatility of the prices of the carbon dioxide "CO2" emissions with the volatility of one of their fundamental components, the energy prices. The dependence between the returns will be approached by a particular class of copula, the Stochastic Autoregressive Copulas (SCAR), which is a time varying copula that was first introduced by Hafner and Manner (2012)[1] in which the parameter driving the dynamic of the copula follows a stochastic autoregressive process. The standard likelihood method will be used together with Efficient Importance Sampling (EIS) method, to evaluate the integral with a large dimension in the expression of the likelihood function. The main result suggests that the dynamics of the dependence between the volatility of the CO2 emission prices and the volatility of energy returns, coal, natural gas and Brent oil prices, do vary over time, although not much in stable periods but rise noticeably during the period of crisis and turmoils.

Suggested Citation

  • Vêlayoudom Marimoutou & Manel Soury, 2015. "Energy Markets and CO2 Emissions: Analysis by Stochastic Copula Autoregressive Model," Working Papers halshs-01148746, HAL.
    • Handle: RePEc:hal:wpaper:halshs-01148746
    Note: View the original document on HAL open archive server: https://shs.hal.science/halshs-01148746
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    References listed on IDEAS

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    Keywords

    CO2 emissions; dependence; SCAR copula; efficient importance sampling; GAS model;
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