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Return Volatility, Correlation, and Hedging of Green and Brown Stocks: Is there a Role for Climate Risk Factors?

Author

Listed:
  • Haohua Li

    (School of Management and Engineering, Nanjing University, No. 5 Pingcang Lane, Gulou District of Nanjing, Jiangsu Province, China)

  • Elie Bouri

    (School of Business, Lebanese American University, Byblos, Lebanon)

  • Rangan Gupta

    (Department of Economics, University of Pretoria, Private Bag X20, Hatfield 0028, South Africa)

  • Libing Fang

    (School of Management and Engineering, Nanjing University, No. 5 Pingcang Lane, Gulou District of Nanjing, Jiangsu Province, China)

Abstract

We examine the effects of three monthly climate risk factors, climate policy uncertainty (CPU), climate change news (CCN), and negative climate change news (NCCN) on the long-run volatilities and correlation of daily green and brown energy stock returns, and perform a hedging analysis. Given that our dataset combines daily and monthly data, we rely on mixed data sampling models such as GARCH-MIDAS and DCC-MIDAS in standard and asymmetric forms with a bivariate skew-t distribution, which also allows us to deal with volatility clustering, asymmetric effects, and negative skewness in innovation which characterize our dataset. Firstly, the results of the GARCH-MIDAS models show evidence that climate risk contains information useful to improve the prediction of return volatility of brown energy stocks. Secondly, the results of the DCCMIDAS model indicate that climate risk reduces the green-brown returns correlation, suggesting a negative effect and hedging opportunities. Thirdly, the results of the hedging analysis show that incorporating a climate risk factor, especially NCCN, into the long-run component of dynamic correlation significantly improves the hedging performance between green and brown energy stock indices, and this are robust to an out-of-sample analysis under various refitting window sizes. These results matter to portfolio and risk managers for energy transition and portfolio decarbonization.

Suggested Citation

  • Haohua Li & Elie Bouri & Rangan Gupta & Libing Fang, 2023. "Return Volatility, Correlation, and Hedging of Green and Brown Stocks: Is there a Role for Climate Risk Factors?," Working Papers 202301, University of Pretoria, Department of Economics.
    • Handle: RePEc:pre:wpaper:202301
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    References listed on IDEAS

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    More about this item

    Keywords

    Conditional volatility; dynamic correlation; GARCH-MIDAS; DCCMIDAS; climate change news (CCN); Climate policy uncertainty (CPU); hedging;
    All these keywords.

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • G00 - Financial Economics - - General - - - General
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • Q54 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Climate; Natural Disasters and their Management; Global Warming

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