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Macroeconomics, finance, commodities: Interactions with carbon markets in a data-rich model

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  • Chevallier, Julien

Abstract

This article assesses the transmission of international shocks to EUA spot, EUA futures, and CER futures carbon prices using a broad dataset that includes 115 macroeconomic, financial and commodities indicators with daily frequency from April 4, 2008 to January 25, 2010 totalling 463 observations. The framework adopted is a Factor-Augmented Vector Autoregression model with latent factors extracted from the dataset, as proposed by Bernanke et al. (2005). The main results can be summarized as follows. First, based on impulse responses, we show that carbon prices tend to respond negatively (between -Â 0.2 and -Â 1.2 standard deviation) to an exogenous shock that reduces global economic indicators by one standard deviation. Second, we find evidence that the responses are heterogeneous among the different kinds of carbon prices: CER futures prices tend to react much more significantly than EUA spot and futures prices. Third, the factors explain about 50% of the total variance of all variables in the dataset. The largest contribution is accounted for by the factor correlated with commodities markets, which explains about 28% of the total variability.

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  • Chevallier, Julien, 2011. "Macroeconomics, finance, commodities: Interactions with carbon markets in a data-rich model," Economic Modelling, Elsevier, vol. 28(1-2), pages 557-567, January.
  • Handle: RePEc:eee:ecmode:v:28:y:2011:i:1-2:p:557-567
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    Cited by:

    1. Luís Aguiar-Conraria & Maria Joana Soares & Rita Sousa, 2017. "California´s Carbon Market and Energy Prices: A Wavelet Analysis," NIPE Working Papers 13/2017, NIPE - Universidade do Minho.
    2. Wen, Xiaoqian & Bouri, Elie & Roubaud, David, 2017. "Can energy commodity futures add to the value of carbon assets?," Economic Modelling, Elsevier, vol. 62(C), pages 194-206.
    3. Libo Yin & Liyan Han, 2016. "Macroeconomic impacts on commodity prices: China vs. the United States," Quantitative Finance, Taylor & Francis Journals, vol. 16(3), pages 489-500, March.
    4. Frank Venmans, 2015. "Capital market response to emission allowance prices: a multivariate GARCH approach," Environmental Economics and Policy Studies, Springer;Society for Environmental Economics and Policy Studies - SEEPS, vol. 17(4), pages 577-620, October.
    5. Bredin, Don & Hyde, Stuart & Muckley, Cal, 2014. "A microstructure analysis of the carbon finance market," International Review of Financial Analysis, Elsevier, vol. 34(C), pages 222-234.
    6. Julien Chevallier, 2013. "Carbon trading: past, present and future," Chapters,in: Handbook on Energy and Climate Change, chapter 21, pages 471-489 Edward Elgar Publishing.
    7. repec:gam:jsusta:v:10:y:2018:i:11:p:4009-:d:180002 is not listed on IDEAS
    8. Yu, Jongmin & Mallory, Mindy L., 2014. "Exchange rate effect on carbon credit price via energy markets," Journal of International Money and Finance, Elsevier, vol. 47(C), pages 145-161.
    9. Chevallier, Julien, 2013. "Variance risk-premia in CO2 markets," Economic Modelling, Elsevier, vol. 31(C), pages 598-605.
    10. Chevallier, Julien, 2011. "Nonparametric modeling of carbon prices," Energy Economics, Elsevier, vol. 33(6), pages 1267-1282.

    More about this item

    Keywords

    FAVAR Carbon price Macroeconomics Finance Commodities Factor models;

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • E52 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - Monetary Policy
    • F41 - International Economics - - Macroeconomic Aspects of International Trade and Finance - - - Open Economy Macroeconomics

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