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Dynamic factor analysis of carbon allowances prices: From classic Arbitrage Pricing Theory to Switching Regimes

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Abstract

The aim of this paper is to identify the fundamental factors that drive the allowances market and to built an APT-like model in order to provide accurate forecasts for CO2. We show that historic dependency patterns emphasis energy, natural gas, oil, coal and equity indexes as major factors driving the carbon allowances prices. There is strong evidence that model residuals are heavily tailed and asymmetric, thereby generalized hyperbolic distribution provides with the best fit results. Introducing dynamics inside the parameters of the APT model via a Hidden Markov Chain Model outperforms the results obtained with a static approach. Empirical results clearly indicate that this model could be used for price forecasting, that it is effective in and out of sample producing consisten results in allowances futures price prediction

Suggested Citation

  • Marius-Cristian Frunza & Dominique Guegan & Antonin Lassoudière, 2010. "Dynamic factor analysis of carbon allowances prices: From classic Arbitrage Pricing Theory to Switching Regimes," Documents de travail du Centre d'Economie de la Sorbonne 10062, Université Panthéon-Sorbonne (Paris 1), Centre d'Economie de la Sorbonne.
  • Handle: RePEc:mse:cesdoc:10062
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    File URL: http://mse.univ-paris1.fr/pub/mse/CES2010/10062.pdf
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    1. Abdou Kâ Diongue & Dominique Guegan & Rodney C. Wolff, 2010. "BL-GARCH model with elliptical distributed innovations," Post-Print halshs-00368340, HAL.
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    Cited by:

    1. Marius-Cristian Frunza & Dominique Guegan & Fabrice Thiebaut, 2010. "Missing trader fraud on the emissions market," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) halshs-00523512, HAL.
    2. Marius-Cristian Frunza & Dominique Guegan & Fabrice Thiebaut, 2010. "Missing trader fraud on the emissions market," Post-Print halshs-00523512, HAL.

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    More about this item

    Keywords

    Carbon; EUA; energy; Arbitrage Pricing Theory; switching regimes; hidden Markov Chain model; forecast;
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