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Price discovery analysis of green equity indices using robust asymmetric vector autoregression

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  • Cummins, Mark
  • Garry, Oonagh
  • Kearney, Claire

Abstract

Covering the first commitment period of the Kyoto Protocol (2008–2012), we perform a price discovery analysis to determine Granger causality relationships for a range of prominent green equity indices with the broader equity and commodity markets. Three pivotal contributions are made. Firstly, an expanded database is used that gives greater depth to the price discovery analysis relative to previous literature. Prominent global, regional and sectoral green equity indices are considered, as well as a broader set of commodities including crude oil, natural gas and emissions. The inclusion of natural gas recognises its role as the transition fossil fuel to a low carbon economy. In addition to the main European Union Allowance traded under the EU Emissions Trading Scheme, Certified Emissions Reduction (CER) prices are also included in the emissions database to capture activities under the global Clean Development Mechanism. Secondly, a problem with conventional symmetric vector autoregression is that its implementation commonly leads to large occurrences of insignificant parameters. Therefore, as a first layer of robustness, we utilise an asymmetric vector autoregression model to perform the Granger causality testing, which addresses this limitation by means of allowing different lag specifications among the system variables. Thirdly, explicit recognition is made in our study of the multiple comparisons bias inherent in our high-dimensional testing framework, which is the non-negligible likelihood of identifying statistically significant results by pure chance alone. As a second layer of robustness, we utilise a generalised Holm correction method to control this source of bias. At conventional statistical significance levels, we find that the FTSE 100 and FTSE Global Small Cap equity indices have a causal effect on all of the green equity indices, with limited evidence of causality in the opposite direction. Within the green equity markets, we find evidence that the chosen sectoral index has a Granger causal effect on one of the two global indices considered and also the regional index. This price transmission provides modest evidence that the global green economy is becoming ever more integrated. NBP gas is shown to have a causal effect on all of the green equity indices, whereas we find no such evidence for Brent oil. The former observation may reflect the increasing role of gas as the transition fuel to a low carbon economy, playing a key role in decisions on power generation mix and associated capital investment. Finally, we find no evidence that EUA or CER prices have a causal effect on green stocks, consistent with previous findings and likely reflecting the excessively low prices being commanded for compliance permits in the European emissions markets.

Suggested Citation

  • Cummins, Mark & Garry, Oonagh & Kearney, Claire, 2014. "Price discovery analysis of green equity indices using robust asymmetric vector autoregression," International Review of Financial Analysis, Elsevier, vol. 35(C), pages 261-267.
  • Handle: RePEc:eee:finana:v:35:y:2014:i:c:p:261-267
    DOI: 10.1016/j.irfa.2014.10.006
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    References listed on IDEAS

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    1. M. Cummins, 2013. "Multiple comparisons problem: Recent advances applied to energy and emissions," Applied Economics Letters, Taylor & Francis Journals, vol. 20(9), pages 903-909, June.
    2. Henriques, Irene & Sadorsky, Perry, 2008. "Oil prices and the stock prices of alternative energy companies," Energy Economics, Elsevier, vol. 30(3), pages 998-1010, May.
    3. Litterman, Robert B, 1986. "Forecasting with Bayesian Vector Autoregressions-Five Years of Experience," Journal of Business & Economic Statistics, American Statistical Association, vol. 4(1), pages 25-38, January.
    4. Bohl, Martin T. & Kaufmann, Philipp & Stephan, Patrick M., 2013. "From hero to zero: Evidence of performance reversal and speculative bubbles in German renewable energy stocks," Energy Economics, Elsevier, vol. 37(C), pages 40-51.
    5. Kumar, Surender & Managi, Shunsuke & Matsuda, Akimi, 2012. "Stock prices of clean energy firms, oil and carbon markets: A vector autoregressive analysis," Energy Economics, Elsevier, vol. 34(1), pages 215-226.
    6. Joseph P. Romano & Michael Wolf, "undated". "Control of Generalized Error Rates in Multiple Testing," IEW - Working Papers 245, Institute for Empirical Research in Economics - University of Zurich.
    7. Sims, Christopher A, 1980. "Macroeconomics and Reality," Econometrica, Econometric Society, vol. 48(1), pages 1-48, January.
    8. Joseph P. Romano & Azeem M. Shaikh & Michael Wolf, 2010. "Hypothesis Testing in Econometrics," Annual Review of Economics, Annual Reviews, vol. 2(1), pages 75-104, September.
    9. Cummins, Mark, 2013. "EU ETS market interactions: The case for multiple hypothesis testing approaches," Applied Energy, Elsevier, vol. 111(C), pages 701-709.
    10. Karsten Neuhoff & Anne Schopp & Rodney Boyd & Kateryna Stelmakh & Alexander Vasa, 2012. "Banking of Surplus Emissions Allowances: Does the Volume Matter?," Discussion Papers of DIW Berlin 1196, DIW Berlin, German Institute for Economic Research.
    11. Litterman, Robert, 1986. "Forecasting with Bayesian vector autoregressions -- Five years of experience : Robert B. Litterman, Journal of Business and Economic Statistics 4 (1986) 25-38," International Journal of Forecasting, Elsevier, vol. 2(4), pages 497-498.
    12. Keating, John W., 2000. "Macroeconomic Modeling with Asymmetric Vector Autoregressions," Journal of Macroeconomics, Elsevier, vol. 22(1), pages 1-28, January.
    13. Hsiao, Cheng, 1981. "Autoregressive modelling and money-income causality detection," Journal of Monetary Economics, Elsevier, vol. 7(1), pages 85-106.
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    Cited by:

    1. Donggyu Lee & Jungho Baek, 2018. "Stock Prices of Renewable Energy Firms: Are There Asymmetric Responses to Oil Price Changes?," Economies, MDPI, vol. 6(4), pages 1-8, November.
    2. Chevallier, Julien & Goutte, Stéphane & Ji, Qiang & Guesmi, Khaled, 2021. "Green finance and the restructuring of the oil-gas-coal business model under carbon asset stranding constraints," Energy Policy, Elsevier, vol. 149(C).
    3. Julien Chevallier & Stéphane Goutte & Khaled Guesmi, 2019. "Climate finance and the restructuring of the oil-gas-coal business model under carbon asset stranding constraints," Working Papers halshs-02106113, HAL.

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    More about this item

    Keywords

    Green equity indices; Asymmetric vector autoregression; Granger causality; Multiple hypothesis testing; Multiple comparisons bias;
    All these keywords.

    JEL classification:

    • C01 - Mathematical and Quantitative Methods - - General - - - Econometrics
    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation

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