IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v318y2025ics0360544225004268.html
   My bibliography  Save this article

News sentiment, climate conditions, and New Zealand electricity market: A real-time bidding policy perspective

Author

Listed:
  • Wang, Guanghao
  • Sbai, Erwann
  • Sheng, Mingyue Selena
  • Tao, Miaomiao

Abstract

We investigate the short- and long-term dynamics between news sentiment, climate conditions, and New Zealand's wholesale electricity prices. Remarkably, our analysis is mainly grounded in investigating and comparing the determinants of power before and after implementing the real-time bidding policy in New Zealand. We apply the Quantile Autoregressive Distributed Lag (QARDL) approach to address the complex nature of these indicators effectively. Specifically, before implementing the real-time bidding policy, the electricity prices presented salient asymmetry and a non-linear structure in response to climate conditions, news sentiment, and consumer demand, consolidating the market's sensitivity to exogenous conditions in the short and long run. Interestingly, the real-time bidding shock substantially altered these powers, mainly manifested by the stylized fact that the market's response to these factors became noticeably more stable. Further, the market's ability to absorb price fluctuations is also significantly strengthened in the long term due to the insensitive response of the electricity market to climate conditions. Our analysis also confirmed the solid relationship after the relevant policy introduction.

Suggested Citation

  • Wang, Guanghao & Sbai, Erwann & Sheng, Mingyue Selena & Tao, Miaomiao, 2025. "News sentiment, climate conditions, and New Zealand electricity market: A real-time bidding policy perspective," Energy, Elsevier, vol. 318(C).
    • Handle: RePEc:eee:energy:v:318:y:2025:i:c:s0360544225004268
    DOI: 10.1016/j.energy.2025.134784
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544225004268
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2025.134784?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. Cho, Jin Seo & Kim, Tae-hwan & Shin, Yongcheol, 2015. "Quantile cointegration in the autoregressive distributed-lag modeling framework," Journal of Econometrics, Elsevier, vol. 188(1), pages 281-300.
    2. Guanghao Wang & Chenghao Liu & Erwann Sbai & Mingyue Selena Sheng & Jinhong Hu & Miaomiao Tao, 2024. "Interrelations between bitcoin market sentiment, crude oil, gold, and the stock market with bitcoin prices: Vision from the hedging market," Studies in Economics and Finance, Emerald Group Publishing Limited, vol. 41(5), pages 1166-1190, June.
    3. Guo, Yawei & Li, Jianping & Li, Yehua & You, Wanhai, 2021. "The roles of political risk and crude oil in stock market based on quantile cointegration approach: A comparative study in China and US," Energy Economics, Elsevier, vol. 97(C).
    4. Cludius, Johanna & Hermann, Hauke & Matthes, Felix Chr. & Graichen, Verena, 2014. "The merit order effect of wind and photovoltaic electricity generation in Germany 2008–2016: Estimation and distributional implications," Energy Economics, Elsevier, vol. 44(C), pages 302-313.
    5. Ismail Shah & Hasnain Iftikhar & Sajid Ali & Wendong Yang, 2022. "Modeling and Forecasting Electricity Demand and Prices: A Comparison of Alternative Approaches," Journal of Mathematics, Hindawi, vol. 2022, pages 1-14, July.
    6. Madhavan, Ananth, 1996. "Security Prices and Market Transparency," Journal of Financial Intermediation, Elsevier, vol. 5(3), pages 255-283, July.
    7. Godil, Danish Iqbal & Sarwat, Salman & Sharif, Arshian & Jermsittiparsert, Kittisak, 2020. "How oil prices, gold prices, uncertainty and risk impact Islamic and conventional stocks? Empirical evidence from QARDL technique," Resources Policy, Elsevier, vol. 66(C).
    8. Yang, Kun & Cheng, Zishu & Li, Mingchen & Wang, Shouyang & Wei, Yunjie, 2024. "Fortify the investment performance of crude oil market by integrating sentiment analysis and an interval-based trading strategy," Applied Energy, Elsevier, vol. 353(PA).
    9. Anil K Kashyap & Jeremy C. Stein, 2023. "Monetary Policy When the Central Bank Shapes Financial-Market Sentiment," Journal of Economic Perspectives, American Economic Association, vol. 37(1), pages 53-76, Winter.
    10. Zheng, Shuguang & Huang, Guohe & Zhou, Xiong & Zhu, Xiaohang, 2020. "Climate-change impacts on electricity demands at a metropolitan scale: A case study of Guangzhou, China," Applied Energy, Elsevier, vol. 261(C).
    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. Tschora, Léonard & Pierre, Erwan & Plantevit, Marc & Robardet, Céline, 2022. "Electricity price forecasting on the day-ahead market using machine learning," Applied Energy, Elsevier, vol. 313(C).
    13. Odhiambo, Nicholas M., 2009. "Energy consumption and economic growth nexus in Tanzania: An ARDL bounds testing approach," Energy Policy, Elsevier, vol. 37(2), pages 617-622, February.
    14. Blázquez, Leticia & Boogen, Nina & Filippini, Massimo, 2013. "Residential electricity demand in Spain: New empirical evidence using aggregate data," Energy Economics, Elsevier, vol. 36(C), pages 648-657.
    15. Xiao, Zhijie, 2009. "Quantile cointegrating regression," Journal of Econometrics, Elsevier, vol. 150(2), pages 248-260, June.
    16. Bing Wang & Qiran Cai & Zhenming Sun, 2020. "Determinants of Willingness to Participate in Urban Incentive-Based Energy Demand-Side Response: An Empirical Micro-Data Analysis," Sustainability, MDPI, vol. 12(19), pages 1-18, September.
    17. Shahbaz, Muhammad & Lahiani, Amine & Abosedra, Salah & Hammoudeh, Shawkat, 2018. "The role of globalization in energy consumption: A quantile cointegrating regression approach," Energy Economics, Elsevier, vol. 71(C), pages 161-170.
    18. Aalbers, Rob & Shestalova, Victoria & Kocsis, Viktória, 2013. "Innovation policy for directing technical change in the power sector," Energy Policy, Elsevier, vol. 63(C), pages 1240-1250.
    19. Bouri, Elie & Gupta, Rangan & Lahiani, Amine & Shahbaz, Muhammad, 2018. "Testing for asymmetric nonlinear short- and long-run relationships between bitcoin, aggregate commodity and gold prices," Resources Policy, Elsevier, vol. 57(C), pages 224-235.
    20. Yang, Dazhi & Wang, Wenting & Gueymard, Christian A. & Hong, Tao & Kleissl, Jan & Huang, Jing & Perez, Marc J. & Perez, Richard & Bright, Jamie M. & Xia, Xiang’ao & van der Meer, Dennis & Peters, Ian , 2022. "A review of solar forecasting, its dependence on atmospheric sciences and implications for grid integration: Towards carbon neutrality," Renewable and Sustainable Energy Reviews, Elsevier, vol. 161(C).
    21. Jan Ditzen & Yiannis Karavias & Joakim Westerlund, 2021. "Testing and Estimating Structural Breaks in Time Series and Panel Data in Stata," Papers 2110.14550, arXiv.org, revised Jan 2025.
    22. Micheli, Leonardo & Solas, Álvaro F. & Soria-Moya, Alberto & Almonacid, Florencia & Fernandez, Eduardo F., 2021. "Short-Term Impact of the COVID-19 Lockdown on the Energy and Economic Performance of Photovoltaics in the Spanish Electricity Sector," MPRA Paper 107969, University Library of Munich, Germany.
    23. Alexander Ryota Keeley & Ken’ichi Matsumoto & Kenta Tanaka & Yogi Sugiawan & Shunsuke Managi, 2021. "The Impact of Renewable Energy Generation on the Spot Market Price in Germany: Ex-Post Analysis using Boosting Method," The Energy Journal, , vol. 42(1_suppl), pages 1-22, June.
    24. Sun, Xiaolei & Li, Jianping & Tang, Ling & Wu, Dengsheng, 2012. "Identifying the risk-return tradeoff and exploring the dynamic risk exposure of country portfolio of the FSU's oil economies," Economic Modelling, Elsevier, vol. 29(6), pages 2494-2503.
    25. Ahmed, T. & Muttaqi, K.M. & Agalgaonkar, A.P., 2012. "Climate change impacts on electricity demand in the State of New South Wales, Australia," Applied Energy, Elsevier, vol. 98(C), pages 376-383.
    26. Yushi Tsunoda & Yusuke Zennyo, 2021. "Platform Information Transparency and Effects on Third‐Party Suppliers and Offline Retailers," Production and Operations Management, Production and Operations Management Society, vol. 30(11), pages 4219-4235, November.
    27. Poletti, Stephen & Staffell, Iain, 2021. "Understanding New Zealand’s wind resources as a route to 100% renewable electricity," Renewable Energy, Elsevier, vol. 170(C), pages 449-461.
    28. Gholami, Mina Bahrami & Poletti, Stephen & Staffell, Iain, 2021. "Wind, rain, fire and sun: Towards zero carbon electricity for New Zealand," Energy Policy, Elsevier, vol. 150(C).
    29. Renato Agurto & Fernando Fuentes & Carlos J. García & Esteban Skoknic, 2021. "The macroeconomic impact of the electricity price: lessons from Chile," Empirical Economics, Springer, vol. 60(5), pages 2407-2428, May.
    30. Syed Jawad Hussain Shahzad & Dene Hurley & Román Ferrer, 2021. "U.S. stock prices and macroeconomic fundamentals: Fresh evidence using the quantile ARDL approach," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 26(3), pages 3569-3587, July.
    31. Koenker, Roger & Xiao, Zhijie, 2006. "Quantile Autoregression," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 980-990, September.
    32. Prakash, Abhijith & Ashby, Rohan & Bruce, Anna & MacGill, Iain, 2023. "Quantifying reserve capabilities for designing flexible electricity markets: An Australian case study with increasing penetrations of renewables," Energy Policy, Elsevier, vol. 177(C).
    33. Hainmueller, Jens & Hazlett, Chad, 2014. "Kernel Regularized Least Squares: Reducing Misspecification Bias with a Flexible and Interpretable Machine Learning Approach," Political Analysis, Cambridge University Press, vol. 22(2), pages 143-168, April.
    34. Hosius, Emil & Seebaß, Johann V. & Wacker, Benjamin & Schlüter, Jan Chr., 2023. "The impact of offshore wind energy on Northern European wholesale electricity prices," Applied Energy, Elsevier, vol. 341(C).
    35. Hendrik Schmitz & Reinhard Madlener, 2020. "Heterogeneity in price responsiveness for residential space heating in Germany," Empirical Economics, Springer, vol. 59(5), pages 2255-2281, November.
    36. Koenker, Roger W & Bassett, Gilbert, Jr, 1978. "Regression Quantiles," Econometrica, Econometric Society, vol. 46(1), pages 33-50, January.
    37. Kaur, Charanjit & Siddiki, Jalal & Singh, Prakash, 2024. "The asymmetric impact of input prices, the Russia-Ukraine war and domestic policy changes on wholesale electricity prices in India: A quantile autoregressive distributed lag analysis," Energy Economics, Elsevier, vol. 132(C).
    38. Tao, Miaomiao & Poletti, Stephen & Sheng, Mingyue Selena & Wen, Le, 2024. "Nexus between carbon, stock, and energy markets in New Zealand: An analysis of causal domains," Energy, Elsevier, vol. 299(C).
    39. Zhu, Huiming & Peng, Cheng & You, Wanhai, 2016. "Quantile behaviour of cointegration between silver and gold prices," Finance Research Letters, Elsevier, vol. 19(C), pages 119-125.
    40. A. T. D. Perera & Vahid M. Nik & Deliang Chen & Jean-Louis Scartezzini & Tianzhen Hong, 2020. "Quantifying the impacts of climate change and extreme climate events on energy systems," Nature Energy, Nature, vol. 5(2), pages 150-159, February.
    41. Saikkonen, Pentti, 1991. "Asymptotically Efficient Estimation of Cointegration Regressions," Econometric Theory, Cambridge University Press, vol. 7(1), pages 1-21, March.
    42. Gunnar Eskeland & Torben Mideksa, 2010. "Electricity demand in a changing climate," Mitigation and Adaptation Strategies for Global Change, Springer, vol. 15(8), pages 877-897, December.
    43. Léonard Tschora & Erwan Pierre & Marc Plantevit & Céline Robardet, 2022. "Electricity price forecasting on the day-ahead market using machine learning," Post-Print hal-03621974, HAL.
    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. Guo, Yawei & Li, Jianping & Li, Yehua & You, Wanhai, 2021. "The roles of political risk and crude oil in stock market based on quantile cointegration approach: A comparative study in China and US," Energy Economics, Elsevier, vol. 97(C).
    2. Georgios Bertsatos & Plutarchos Sakellaris & Mike G. Tsionas, 2022. "Correction to: Extensions of the Pesaran, Shin and Smith (2001) bounds testing procedure," Empirical Economics, Springer, vol. 62(2), pages 635-635, February.
    3. Ren, Xiaohang & An, Yaning & Jin, Chenglu, 2023. "The asymmetric effect of geopolitical risk on China's crude oil prices: New evidence from a QARDL approach," Finance Research Letters, Elsevier, vol. 53(C).
    4. Cho, Jin Seo & Kim, Tae-hwan & Shin, Yongcheol, 2015. "Quantile cointegration in the autoregressive distributed-lag modeling framework," Journal of Econometrics, Elsevier, vol. 188(1), pages 281-300.
    5. Maynard, Alex & Shimotsu, Katsumi & Kuriyama, Nina, 2024. "Inference in predictive quantile regressions," Journal of Econometrics, Elsevier, vol. 245(1).
    6. Lee, Dong Jin & Kim, Tae-Hwan & Mizen, Paul, 2021. "Impulse response analysis in conditional quantile models with an application to monetary policy," Journal of Economic Dynamics and Control, Elsevier, vol. 127(C).
    7. Syed Jawad Hussain Shahzad & Dene Hurley & Román Ferrer, 2021. "U.S. stock prices and macroeconomic fundamentals: Fresh evidence using the quantile ARDL approach," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 26(3), pages 3569-3587, July.
    8. Agbeyegbe, Terence D., 2015. "An inverted U-shaped crude oil price return-implied volatility relationship," Review of Financial Economics, Elsevier, vol. 27(C), pages 28-45.
    9. Christou, Christina & Gupta, Rangan & Nyakabawo, Wendy & Wohar, Mark E., 2018. "Do house prices hedge inflation in the US? A quantile cointegration approach," International Review of Economics & Finance, Elsevier, vol. 54(C), pages 15-26.
    10. Mighri, Zouheir & Ragoubi, Hanen & Sarwar, Suleman & Wang, Yihan, 2022. "Quantile Granger causality between US stock market indices and precious metal prices," Resources Policy, Elsevier, vol. 76(C).
    11. Cheng Jin & Asif Razzaq & Faiza Saleem & Avik Sinha, 2022. "Asymmetric effects of eco-innovation and human capital development in realizing environmental sustainability in China: evidence from quantile ARDL framework," Economic Research-Ekonomska Istraživanja, Taylor & Francis Journals, vol. 35(1), pages 4947-4970, December.
    12. Abbass, Kashif & Sharif, Arshian & Song, Huaming & Ali, Malik Tayyab & Khan, Farina & Amin, Nabila, 2022. "Do geopolitical oil price risk, global macroeconomic fundamentals relate Islamic and conventional stock market? Empirical evidence from QARDL approach," Resources Policy, Elsevier, vol. 77(C).
    13. Xiang, Feiyun & Chang, Tsangyao & Jiang, Shi-jie, 2023. "Economic and climate policy uncertainty, geopolitical risk and life insurance premiums in China: A quantile ARDL approach," Finance Research Letters, Elsevier, vol. 57(C).
    14. Godwin Olasehinde-Williams & Ruth Omotosho & Festus Victor Bekun, 2024. "Interest Rate Volatility and Economic Growth in Nigeria: New Insight from the Quantile Autoregressive Distributed Lag (QARDL) Model," Journal of the Knowledge Economy, Springer;Portland International Center for Management of Engineering and Technology (PICMET), vol. 15(4), pages 20172-20195, December.
    15. Gaglianone, Wagner Piazza & Guillén, Osmani Teixeira de Carvalho & Figueiredo, Francisco Marcos Rodrigues, 2018. "Estimating inflation persistence by quantile autoregression with quantile-specific unit roots," Economic Modelling, Elsevier, vol. 73(C), pages 407-430.
    16. Gangopadhyay, Partha & Das, Narasingha & Alam, G.M. Monirul & Khan, Uzma & Haseeb, Mohammad & Hossain, Md. Emran, 2023. "Revisiting the carbon pollution-inhibiting policies in the USA using the quantile ARDL methodology: What roles can clean energy and globalization play?," Renewable Energy, Elsevier, vol. 204(C), pages 710-721.
    17. Emmanuel Uche & Lionel Effiom, 2021. "Fighting capital flight in Nigeria: have we considered global uncertainties and exchange rate volatilities? Fresh insights via quantile ARDL model," SN Business & Economics, Springer, vol. 1(6), pages 1-22, June.
    18. White, Halbert & Kim, Tae-Hwan & Manganelli, Simone, 2015. "VAR for VaR: Measuring tail dependence using multivariate regression quantiles," Journal of Econometrics, Elsevier, vol. 187(1), pages 169-188.
    19. Wang, Kai-Hua & Zhao, Yan-Xin & Su, Yun Hsuan & Lobonţ, Oana-Ramona, 2023. "Energy security and CO2 emissions: New evidence from time-varying and quantile-varying aspects," Energy, Elsevier, vol. 273(C).
    20. Li, Cong & Lin, Shiwei & Sun, Yihan & Afshan, Sahar & Yaqoob, Tanzeela, 2022. "The asymmetric effect of oil price, news-based uncertainty, and COVID-19 pandemic on equity market," Resources Policy, Elsevier, vol. 77(C).

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    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:energy:v:318:y:2025:i:c:s0360544225004268. 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.journals.elsevier.com/energy .

    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.