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

Haohua Li (), Elie Bouri (), Rangan Gupta and Libing Fang ()
Additional contact information
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
Libing Fang: School of Management and Engineering, Nanjing University, No. 5 Pingcang Lane, Gulou District of Nanjing, Jiangsu Province, China

No 202301, Working Papers from University of Pretoria, Department of Economics

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.

Keywords: Conditional volatility; dynamic correlation; GARCH-MIDAS; DCCMIDAS; climate change news (CCN); Climate policy uncertainty (CPU); hedging (search for similar items in EconPapers)
JEL-codes: C32 G00 G11 Q54 (search for similar items in EconPapers)
Pages: 31 pages
Date: 2023-01
New Economics Papers: this item is included in nep-ene, nep-env and nep-fmk
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (10)

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:pre:wpaper:202301

Access Statistics for this paper

More papers in Working Papers from University of Pretoria, Department of Economics Contact information at EDIRC.
Bibliographic data for series maintained by Rangan Gupta ().