IDEAS home Printed from https://ideas.repec.org/a/eee/ecofin/v81y2026ics1062940825001950.html

Does climate policy uncertainty affect expected shortfall (and Value-at-Risk) in the Chinese sector? Evidence from the mixed-frequency dynamic semi-parametric approach

Author

Listed:
  • Jiang, Kunliang
  • Luo, Pengfei
  • Gan, Wenxiao
  • Song, Jiashan
  • Wang, Yuejing

Abstract

Climate policy uncertainty (CPU) increases risks across sectors by affecting the economic environment, stock price volatility, corporate transformations, and investor confidence. However, incorporating such low-frequency information into sector risk assessments remains insufficiently addressed. This study combines the GARCH-MIDAS model with the Fissler–Ziegel (FZ) loss function to jointly model Value-at-Risk (VaR) and expected shortfall (ES), and explores the heterogeneous impact of CPU on risk measures across eleven sectors from 1 January 2008 to 31 December 2022 in China. Our findings indicate that CPU positively affects the VaR and ES in five sectors: energy, material, industry, consumer discretionary, and utility, while negatively impacting six sectors: consumer staple, healthcare, information technology, telecommunication service, real estate, and finance. The effect of CPU on the VaR and ES in the energy and material sectors demonstrates strong long memory, whereas the impact on telecommunication service is the opposite. Incorporating CPU into the model significantly improves the accuracy of sector risk measures across various risk levels, while the FZ loss function method provides effective risk measurement results primarily under extreme risk conditions.

Suggested Citation

  • Jiang, Kunliang & Luo, Pengfei & Gan, Wenxiao & Song, Jiashan & Wang, Yuejing, 2026. "Does climate policy uncertainty affect expected shortfall (and Value-at-Risk) in the Chinese sector? Evidence from the mixed-frequency dynamic semi-parametric approach," The North American Journal of Economics and Finance, Elsevier, vol. 81(C).
  • Handle: RePEc:eee:ecofin:v:81:y:2026:i:c:s1062940825001950
    DOI: 10.1016/j.najef.2025.102555
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1062940825001950
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.najef.2025.102555?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to

    for a different version of it.

    References listed on IDEAS

    as
    1. Cai, Zongwu & Wang, Xian, 2008. "Nonparametric estimation of conditional VaR and expected shortfall," Journal of Econometrics, Elsevier, vol. 147(1), pages 120-130, November.
    2. Chen, Wen, 2023. "Bank connections, corporate social responsibility and low-carbon innovation," Energy Policy, Elsevier, vol. 183(C).
    3. Monasterolo, Irene & de Angelis, Luca, 2020. "Blind to carbon risk? An analysis of stock market reaction to the Paris Agreement," Ecological Economics, Elsevier, vol. 170(C).
    4. Mo, Yan & Liu, Xiaotong, 2023. "Climate policy uncertainty and digital transformation of enterprise—Evidence from China," Economics Letters, Elsevier, vol. 233(C).
    5. Asgharian, Hossein & Christiansen, Charlotte & Hou, Ai Jun, 2023. "The effect of uncertainty on stock market volatility and correlation," Journal of Banking & Finance, Elsevier, vol. 154(C).
    6. Bai, Dongbei & Du, Lizhao & Xu, Yang & Abbas, Shujaat, 2023. "Climate policy uncertainty and corporate green innovation: Evidence from Chinese A-share listed industrial corporations," Energy Economics, Elsevier, vol. 127(PB).
    7. Yao, Can-Zhong & Li, Min-Jian, 2023. "GARCH-MIDAS-GAS-copula model for CoVaR and risk spillover in stock markets," The North American Journal of Economics and Finance, Elsevier, vol. 66(C).
    8. Patton, Andrew J. & Ziegel, Johanna F. & Chen, Rui, 2019. "Dynamic semiparametric models for expected shortfall (and Value-at-Risk)," Journal of Econometrics, Elsevier, vol. 211(2), pages 388-413.
    9. Lyócsa, Štefan & Plíhal, Tomáš & Výrost, Tomáš, 2024. "Forecasting day-ahead expected shortfall on the EUR/USD exchange rate: The (I)relevance of implied volatility," International Journal of Forecasting, Elsevier, vol. 40(4), pages 1275-1301.
    10. Taylor, James W., 2020. "Forecast combinations for value at risk and expected shortfall," International Journal of Forecasting, Elsevier, vol. 36(2), pages 428-441.
    11. Robert F. Engle & Simone Manganelli, 2004. "CAViaR: Conditional Autoregressive Value at Risk by Regression Quantiles," Journal of Business & Economic Statistics, American Statistical Association, vol. 22, pages 367-381, October.
    12. Ye, Wuyi & Jiang, Kunliang & Liu, Xiaoquan, 2021. "Financial contagion and the TIR-MIDAS model," Finance Research Letters, Elsevier, vol. 39(C).
    13. 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.
    14. Tiwari, Aviral Kumar & Abakah, Emmanuel Joel Aikins & Shao, Xuefeng & Le, TN-Lan & Gyamfi, Matthew Ntow, 2023. "Financial technology stocks, green financial assets, and energy markets: A quantile causality and dependence analysis," Energy Economics, Elsevier, vol. 118(C).
    15. Li, Xiafei & Yang, Shuangpeng & Luo, Keyu & Liang, Chao, 2024. "Spillover relationships between international crude oil markets and global energy stock markets under the influence of geopolitical risks: New evidence," International Review of Financial Analysis, Elsevier, vol. 96(PA).
    16. James W. Taylor, 2019. "Forecasting Value at Risk and Expected Shortfall Using a Semiparametric Approach Based on the Asymmetric Laplace Distribution," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 37(1), pages 121-133, January.
    17. Eliana Christou & Michael Grabchak, 2022. "Estimation of Expected Shortfall Using Quantile Regression: A Comparison Study," Computational Economics, Springer;Society for Computational Economics, vol. 60(2), pages 725-753, August.
    18. Chen, Zhonglu & Zhang, Li & Weng, Chen, 2023. "Does climate policy uncertainty affect Chinese stock market volatility?," International Review of Economics & Finance, Elsevier, vol. 84(C), pages 369-381.
    19. Eric Ghysels & Arthur Sinko & Rossen Valkanov, 2007. "MIDAS Regressions: Further Results and New Directions," Econometric Reviews, Taylor & Francis Journals, vol. 26(1), pages 53-90.
    20. Colacito, Riccardo & Engle, Robert F. & Ghysels, Eric, 2011. "A component model for dynamic correlations," Journal of Econometrics, Elsevier, vol. 164(1), pages 45-59, September.
    21. Segnon, Mawuli & Gupta, Rangan & Wilfling, Bernd, 2024. "Forecasting stock market volatility with regime-switching GARCH-MIDAS: The role of geopolitical risks," International Journal of Forecasting, Elsevier, vol. 40(1), pages 29-43.
    22. Salisu, Afees A. & Ogbonna, Ahamuefula E. & Lasisi, Lukman & Olaniran, Abeeb, 2022. "Geopolitical risk and stock market volatility in emerging markets: A GARCH – MIDAS approach," The North American Journal of Economics and Finance, Elsevier, vol. 62(C).
    23. Kessler, Melanie & Arlinghaus, Julia C. & Rosca, Eugenia & Zimmermann, Manuel, 2022. "Curse or Blessing? Exploring risk factors of digital technologies in industrial operations," International Journal of Production Economics, Elsevier, vol. 243(C).
    24. Ramos, Sofia B. & Veiga, Helena, 2011. "Risk factors in oil and gas industry returns: International evidence," Energy Economics, Elsevier, vol. 33(3), pages 525-542, May.
    25. Ren, Xiaohang & Li, Jingyao & He, Feng & Lucey, Brian, 2023. "Impact of climate policy uncertainty on traditional energy and green markets: Evidence from time-varying granger tests," Renewable and Sustainable Energy Reviews, Elsevier, vol. 173(C).
    26. Ren, Xiaohang & Zhang, Xiao & Yan, Cheng & Gozgor, Giray, 2022. "Climate policy uncertainty and firm-level total factor productivity: Evidence from China," Energy Economics, Elsevier, vol. 113(C).
    27. Wang, Qin & Li, Xianhua, 2024. "Copula-MIDAS-TRV model for risk spillover analysis − Evidence from the Chinese stock market," The North American Journal of Economics and Finance, Elsevier, vol. 74(C).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Qifa Xu & Lu Chen & Cuixia Jiang & Yezheng Liu, 2022. "Forecasting expected shortfall and value at risk with a joint elicitable mixed data sampling model," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(3), pages 407-421, April.
    2. Laura Garcia‐Jorcano & Lidia Sanchis‐Marco, 2025. "Measuring the Impact of Transition Risk on Financial Markets: A Joint VaR‐ES Approach," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 44(6), pages 1907-1945, September.
    3. Xu, Qifa & Chen, Lu & Jiang, Cuixia & Yu, Keming, 2020. "Mixed data sampling expectile regression with applications to measuring financial risk," Economic Modelling, Elsevier, vol. 91(C), pages 469-486.
    4. Storti, Giuseppe & Wang, Chao, 2022. "Nonparametric expected shortfall forecasting incorporating weighted quantiles," International Journal of Forecasting, Elsevier, vol. 38(1), pages 224-239.
    5. d’Addona, Stefano & Khanom, Najrin, 2022. "Estimating tail-risk using semiparametric conditional variance with an application to meme stocks," International Review of Economics & Finance, Elsevier, vol. 82(C), pages 241-260.
    6. Horváth, Matúš & Výrost, Tomáš, 2025. "No shortfall of ES estimators: Insights from cryptocurrency portfolios," Finance Research Letters, Elsevier, vol. 73(C).
    7. Dimitriadis, Timo & Schnaitmann, Julie, 2021. "Forecast encompassing tests for the expected shortfall," International Journal of Forecasting, Elsevier, vol. 37(2), pages 604-621.
    8. Timo Dimitriadis & iaochun Liu & Julie Schnaitmann, 2023. "Encompassing Tests for Value at Risk and Expected Shortfall Multistep Forecasts Based on Inference on the Boundary," Journal of Financial Econometrics, Oxford University Press, vol. 21(2), pages 412-444.
    9. Catania, Leopoldo & Luati, Alessandra, 2025. "Quasi Maximum Likelihood Estimation of Value at Risk and Expected Shortfall," Econometrics and Statistics, Elsevier, vol. 33(C), pages 23-34.
    10. Jianzhou Wang & Shuai Wang & Mengzheng Lv & He Jiang, 2024. "Forecasting VaR and ES by using deep quantile regression, GANs-based scenario generation, and heterogeneous market hypothesis," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 10(1), pages 1-35, December.
    11. Giuseppe Storti & Chao Wang, 2023. "Modeling uncertainty in financial tail risk: A forecast combination and weighted quantile approach," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(7), pages 1648-1663, November.
    12. Hu, Zinan & Borjigin, Sumuya, 2024. "The amplifying role of geopolitical Risks, economic policy Uncertainty, and climate risks on Energy-Stock market volatility spillover across economic cycles," The North American Journal of Economics and Finance, Elsevier, vol. 71(C).
    13. Bonaccolto, Giovanni & Caporin, Massimiliano & Maillet, Bertrand B., 2022. "Dynamic large financial networks via conditional expected shortfalls," European Journal of Operational Research, Elsevier, vol. 298(1), pages 322-336.
    14. Marc Hallin & Carlos Trucíos, 2020. "Forecasting Value-at-Risk and Expected Shortfall in Large Portfolios: a General Dynamic Factor Approach," Working Papers ECARES 2020-50, ULB -- Universite Libre de Bruxelles.
    15. Hallin, Marc & Trucíos, Carlos, 2023. "Forecasting value-at-risk and expected shortfall in large portfolios: A general dynamic factor model approach," Econometrics and Statistics, Elsevier, vol. 27(C), pages 1-15.
    16. Le, Trung H., 2020. "Forecasting value at risk and expected shortfall with mixed data sampling," International Journal of Forecasting, Elsevier, vol. 36(4), pages 1362-1379.
    17. Alessandra Amendola & Vincenzo Candila & Antonio Naimoli & Giuseppe Storti, 2024. "Combining Value-at-Risk and Expected Shortfall forecasts via the Model Confidence Set," Papers 2406.06235, arXiv.org, revised Feb 2026.
    18. Raza, Syed Ali & Khan, Komal Akram, 2024. "Climate policy uncertainty and its relationship with precious metals price volatility: Comparative analysis pre and during COVID-19," Resources Policy, Elsevier, vol. 88(C).
    19. Xu, Ziyao & Zhou, Deheng & Ma, Junfeng & Yuan, Jing, 2025. "The time-varying relationship between climate uncertainty, low-carbon stocks and green bonds," The North American Journal of Economics and Finance, Elsevier, vol. 77(C).
    20. Vincenzo Candila & Giampiero M. Gallo & Lea Petrella, 2020. "Mixed--frequency quantile regressions to forecast Value--at--Risk and Expected Shortfall," Papers 2011.00552, arXiv.org, revised Mar 2023.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    JEL classification:

    • C5 - Mathematical and Quantitative Methods - - Econometric Modeling
    • Q54 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Climate; Natural Disasters and their Management; Global Warming
    • G32 - Financial Economics - - Corporate Finance and Governance - - - Financing Policy; Financial Risk and Risk Management; Capital and Ownership Structure; Value of Firms; Goodwill

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:ecofin:v:81:y:2026:i:c:s1062940825001950. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/inca/620163 .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.