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Risk transmission of El Niño-induced climate change to regional Green Economy Index

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  • Zhang, Li
  • Li, Yan
  • Yu, Sixin
  • Wang, Lu

Abstract

Global warming and rare weather caused by climate change continue to affect ecosystems, human health, and economic systems, which pose serious climate risk challenges for humanity. To address and adapt to climate change risks and to facilitate the process of achieving carbon peaking and carbon-neutral targets, the financial industry has become more concerned about the information spillover effects of extreme climate events on green financial products. Therefore, this paper adopts the Southern Oscillation Index (SOI) to describe climate change and investigates the influence of the SOI on the volatility of the NASDAQ OMX Green Economy Index (OMX-GEI) under a variant of the Double Asymmetric GARCH-MIDAS (DA-GM-X) model. The results show that the SOI provides relevant information for OMX-GEI volatility forecasting and the DA-GM-X model yields outstanding forecasting performance in statistical and economic terms. This conclusion indicates that considering SOI and its asymmetry changes can significantly improve the prediction accuracy of econometric models. Also, several robustness tests confirm our findings. Overall, the findings of this paper suggest that to achieve the two-carbon goal and combat climate change, governments should pay more attention to policy formulation that combines environment, climate, health, energy, and economy, and actively promote green, low-carbon, and sustainable energy development globally.

Suggested Citation

  • Zhang, Li & Li, Yan & Yu, Sixin & Wang, Lu, 2023. "Risk transmission of El Niño-induced climate change to regional Green Economy Index," Economic Analysis and Policy, Elsevier, vol. 79(C), pages 860-872.
  • Handle: RePEc:eee:ecanpo:v:79:y:2023:i:c:p:860-872
    DOI: 10.1016/j.eap.2023.07.006
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    as
    1. Tim Bollerslev & Benjamin Hood & John Huss & Lasse Heje Pedersen, 2018. "Risk Everywhere: Modeling and Managing Volatility," The Review of Financial Studies, Society for Financial Studies, vol. 31(7), pages 2729-2773.
    2. Wei, Yu & Zhang, Jiahao & Chen, Yongfei & Wang, Yizhi, 2022. "The impacts of El Niño-southern oscillation on renewable energy stock markets: Evidence from quantile perspective," Energy, Elsevier, vol. 260(C).
    3. Bissoondoyal-Bheenick, Emawtee & Brooks, Robert & Do, Hung Xuan & Smyth, Russell, 2020. "Exploiting the heteroskedasticity in measurement error to improve volatility predictions in oil and biofuel feedstock markets," Energy Economics, Elsevier, vol. 86(C).
    4. Pham, Linh, 2019. "Do all clean energy stocks respond homogeneously to oil price?," Energy Economics, Elsevier, vol. 81(C), pages 355-379.
    5. Peter R. Hansen & Asger Lunde & James M. Nason, 2011. "The Model Confidence Set," Econometrica, Econometric Society, vol. 79(2), pages 453-497, March.
    6. Xia, Yufei & Sang, Chong & He, Lingyun & Wang, Ziyao, 2023. "The role of uncertainty index in forecasting volatility of Bitcoin: Fresh evidence from GARCH-MIDAS approach," Finance Research Letters, Elsevier, vol. 52(C).
    7. Chao Liang & Yin Liao & Feng Ma & Bo Zhu, 2022. "United States Oil Fund volatility prediction: the roles of leverage effect and jumps," Empirical Economics, Springer, vol. 62(5), pages 2239-2262, May.
    8. Salisu, Afees A. & Gupta, Rangan & Nel, Jacobus & Bouri, Elie, 2022. "The (Asymmetric) effect of El Niño and La Niña on gold and silver prices in a GVAR model," Resources Policy, Elsevier, vol. 78(C).
    9. Lyócsa, Štefan & Todorova, Neda, 2020. "Trading and non-trading period realized market volatility: Does it matter for forecasting the volatility of US stocks?," International Journal of Forecasting, Elsevier, vol. 36(2), pages 628-645.
    10. Clark, Todd E. & West, Kenneth D., 2007. "Approximately normal tests for equal predictive accuracy in nested models," Journal of Econometrics, Elsevier, vol. 138(1), pages 291-311, May.
    11. Barbara Rossi & Atsushi Inoue, 2012. "Out-of-Sample Forecast Tests Robust to the Choice of Window Size," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 30(3), pages 432-453, April.
    12. Robert F. Engle & Eric Ghysels & Bumjean Sohn, 2013. "Stock Market Volatility and Macroeconomic Fundamentals," The Review of Economics and Statistics, MIT Press, vol. 95(3), pages 776-797, July.
    13. Zhou, Zhongbao & Fu, Zhangyan & Jiang, Yong & Zeng, Ximei & Lin, Ling, 2020. "Can economic policy uncertainty predict exchange rate volatility? New evidence from the GARCH-MIDAS model," Finance Research Letters, Elsevier, vol. 34(C).
    14. Liang, Chao & Xia, Zhenglan & Lai, Xiaodong & Wang, Lu, 2022. "Natural gas volatility prediction: Fresh evidence from extreme weather and extended GARCH-MIDAS-ES model," Energy Economics, Elsevier, vol. 116(C).
    15. Chao Liang & Feng Ma & Lu Wang & Qing Zeng, 2021. "The information content of uncertainty indices for natural gas futures volatility forecasting," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(7), pages 1310-1324, November.
    16. Chen, Zhonglu & Liang, Chao & Umar, Muhammad, 2021. "Is investor sentiment stronger than VIX and uncertainty indices in predicting energy volatility?," Resources Policy, Elsevier, vol. 74(C).
    17. Jean-Philippe Bouchaud & Andrew Matacz & Marc Potters, 2001. "The leverage effect in financial markets: retarded volatility and market panic," Science & Finance (CFM) working paper archive 0101120, Science & Finance, Capital Fund Management.
    18. Lucien Georgeson & Mark Maslin, 2019. "Estimating the scale of the US green economy within the global context," Palgrave Communications, Palgrave Macmillan, vol. 5(1), pages 1-12, December.
    19. Peter Reinhard Hansen & Asger Lunde & James M. Nason, 2003. "Choosing the Best Volatility Models: The Model Confidence Set Approach," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 65(s1), pages 839-861, December.
    20. Sang-Wook Yeh & Jong-Seong Kug & Boris Dewitte & Min-Ho Kwon & Ben P. Kirtman & Fei-Fei Jin, 2009. "El Niño in a changing climate," Nature, Nature, vol. 461(7263), pages 511-514, September.
    21. Jiang, Fuwei & Lee, Joshua & Martin, Xiumin & Zhou, Guofu, 2019. "Manager sentiment and stock returns," Journal of Financial Economics, Elsevier, vol. 132(1), pages 126-149.
    22. Liang, Chao & Li, Yan & Ma, Feng & Wei, Yu, 2021. "Global equity market volatilities forecasting: A comparison of leverage effects, jumps, and overnight information," International Review of Financial Analysis, Elsevier, vol. 75(C).
    23. Harvey, David I & Leybourne, Stephen J & Newbold, Paul, 1998. "Tests for Forecast Encompassing," Journal of Business & Economic Statistics, American Statistical Association, vol. 16(2), pages 254-259, April.
    24. Chao Liang & Yaojie Zhang & Xiafei Li & Feng Ma, 2022. "Which predictor is more predictive for Bitcoin volatility? And why?," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 27(2), pages 1947-1961, April.
    25. Aldy, Joseph E., 2011. "A Preliminary Review of the American Recovery and Reinvestment Act's Clean Energy Package," Working Paper Series rwp11-048, Harvard University, John F. Kennedy School of Government.
    26. Wang, Yudong & Wei, Yu & Wu, Chongfeng & Yin, Libo, 2018. "Oil and the short-term predictability of stock return volatility," Journal of Empirical Finance, Elsevier, vol. 47(C), pages 90-104.
    27. Elie Bouri & Rangan Gupta & Christian Pierdzioch & Afees A. Salisu, 2021. "El Nino and Forecastability of Oil-Price Realized Volatility," Working Papers 202105, University of Pretoria, Department of Economics.
    28. Zhang, Yaojie & Ma, Feng & Shi, Benshan & Huang, Dengshi, 2018. "Forecasting the prices of crude oil: An iterated combination approach," Energy Economics, Elsevier, vol. 70(C), pages 472-483.
    29. Mehedi, Tanveer Hassan & Gemechu, Eskinder & Kumar, Amit, 2022. "Life cycle greenhouse gas emissions and energy footprints of utility-scale solar energy systems," Applied Energy, Elsevier, vol. 314(C).
    30. Zhang, Yaojie & Wei, Yu & Zhang, Yi & Jin, Daxiang, 2019. "Forecasting oil price volatility: Forecast combination versus shrinkage method," Energy Economics, Elsevier, vol. 80(C), pages 423-433.
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    More about this item

    Keywords

    Climate risk; El niño; Green Economy Index; Volatility forecasting;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • Q43 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy and the Macroeconomy
    • Q54 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Climate; Natural Disasters and their Management; Global Warming

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