IDEAS home Printed from https://ideas.repec.org/a/spr/comgts/v20y2023i1d10.1007_s10287-023-00436-4.html
   My bibliography  Save this article

Wasserstein barycenter regression for estimating the joint dynamics of renewable and fossil fuel energy indices

Author

Listed:
  • Maria Elena Giuli

    (University of Pavia)

  • Alessandro Spelta

    (University of Pavia)

Abstract

In order to characterize non-linear system dynamics and to generate term structures of joint distributions, we propose a flexible and multidimensional approach, which exploits Wasserstein barycentric coordinates for histograms. We apply this methodology to study the relationships between the performance in the European market of the renewable energy sector and that of the fossil fuel energy one. Our methodology allows us to estimate the term structure of conditional joint distributions. This optimal barycentric interpolation can be interpreted as a posterior version of the joint distribution with respect to the prior contained in the past histograms history. Once the underlying dynamics mechanism among the set of variables are obtained as optimal Wasserstein barycentric coordinates, the learned dynamic rules can be used to generate term structures of joint distributions.

Suggested Citation

  • Maria Elena Giuli & Alessandro Spelta, 2023. "Wasserstein barycenter regression for estimating the joint dynamics of renewable and fossil fuel energy indices," Computational Management Science, Springer, vol. 20(1), pages 1-17, December.
    • Handle: RePEc:spr:comgts:v:20:y:2023:i:1:d:10.1007_s10287-023-00436-4
    DOI: 10.1007/s10287-023-00436-4
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10287-023-00436-4
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10287-023-00436-4?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 search for a different version of it.

    References listed on IDEAS

    as
    1. Diebold, Francis X. & Yılmaz, Kamil, 2014. "On the network topology of variance decompositions: Measuring the connectedness of financial firms," Journal of Econometrics, Elsevier, vol. 182(1), pages 119-134.
    2. Henriques, Irene & Sadorsky, Perry, 2008. "Oil prices and the stock prices of alternative energy companies," Energy Economics, Elsevier, vol. 30(3), pages 998-1010, May.
    3. Francis X. Diebold & Kamil Yilmaz, 2009. "Measuring Financial Asset Return and Volatility Spillovers, with Application to Global Equity Markets," Economic Journal, Royal Economic Society, vol. 119(534), pages 158-171, January.
    4. Umar, Zaghum & Polat, Onur & Choi, Sun-Yong & Teplova, Tamara, 2022. "The impact of the Russia-Ukraine conflict on the connectedness of financial markets," Finance Research Letters, Elsevier, vol. 48(C).
    5. Sadorsky, Perry, 2012. "Correlations and volatility spillovers between oil prices and the stock prices of clean energy and technology companies," Energy Economics, Elsevier, vol. 34(1), pages 248-255.
    6. Rabeh Khalfaoui & Sakiru Adebola Solarin & Adel Al-Qadasi & Sami Ben Jabeur, 2022. "Dynamic causality interplay from COVID-19 pandemic to oil price, stock market, and economic policy uncertainty: evidence from oil-importing and oil-exporting countries," Annals of Operations Research, Springer, vol. 313(1), pages 105-143, June.
    7. Wen, Xiaoqian & Guo, Yanfeng & Wei, Yu & Huang, Dengshi, 2014. "How do the stock prices of new energy and fossil fuel companies correlate? Evidence from China," Energy Economics, Elsevier, vol. 41(C), pages 63-75.
    8. Ferrer, Román & Shahzad, Syed Jawad Hussain & López, Raquel & Jareño, Francisco, 2018. "Time and frequency dynamics of connectedness between renewable energy stocks and crude oil prices," Energy Economics, Elsevier, vol. 76(C), pages 1-20.
    9. Jozef Baruník & Tomáš Křehlík, 2018. "Measuring the Frequency Dynamics of Financial Connectedness and Systemic Risk," Journal of Financial Econometrics, Oxford University Press, vol. 16(2), pages 271-296.
    10. Alessandro Spelta & Andrea Flori & Nicolò Pecora & Sergey Buldyrev & Fabio Pammolli, 2020. "A behavioral approach to instability pathways in financial markets," Nature Communications, Nature, vol. 11(1), pages 1-9, December.
    11. Belhassine, Olfa, 2020. "Volatility spillovers and hedging effectiveness between the oil market and Eurozone sectors: A tale of two crises," Research in International Business and Finance, Elsevier, vol. 53(C).
    12. Pagnottoni, Paolo & Spelta, Alessandro & Flori, Andrea & Pammolli, Fabio, 2022. "Climate change and financial stability: Natural disaster impacts on global stock markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 599(C).
    13. Diebold, Francis X. & Yilmaz, Kamil, 2012. "Better to give than to receive: Predictive directional measurement of volatility spillovers," International Journal of Forecasting, Elsevier, vol. 28(1), pages 57-66.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Spelta, Alessandro & De Giuli, Maria Elena, 2023. "Does renewable energy affect fossil fuel price? A time–frequency analysis for the Europe," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 626(C).

    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. Spelta, Alessandro & De Giuli, Maria Elena, 2023. "Does renewable energy affect fossil fuel price? A time–frequency analysis for the Europe," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 626(C).
    2. Capucine Nobletz, 2021. "Return spillovers between green energy indexes and financial markets: a first sectoral approach," EconomiX Working Papers 2021-24, University of Paris Nanterre, EconomiX.
    3. Dai, Zhifeng & Zhu, Haoyang & Zhang, Xinhua, 2022. "Dynamic spillover effects and portfolio strategies between crude oil, gold and Chinese stock markets related to new energy vehicle," Energy Economics, Elsevier, vol. 109(C).
    4. Umar, Muhammad & Farid, Saqib & Naeem, Muhammad Abubakr, 2022. "Time-frequency connectedness among clean-energy stocks and fossil fuel markets: Comparison between financial, oil and pandemic crisis," Energy, Elsevier, vol. 240(C).
    5. Shah, Adil Ahmad & Sahay, Arvind, 2024. "Is gold a preferable diversifier of cleaner equity risk across diverse scenarios? Evidence from multidimensional connectedness and spillover measures," Energy, Elsevier, vol. 305(C).
    6. Song, Yingjie & Ji, Qiang & Du, Ya-Juan & Geng, Jiang-Bo, 2019. "The dynamic dependence of fossil energy, investor sentiment and renewable energy stock markets," Energy Economics, Elsevier, vol. 84(C).
    7. Wang, Zi-Xin & Liu, Bing-Yue & Fan, Ying, 2023. "Network connectedness between China's crude oil futures and sector stock indices," Energy Economics, Elsevier, vol. 125(C).
    8. Mensi, Walid & Al Rababa'a, Abdel Razzaq & Vo, Xuan Vinh & Kang, Sang Hoon, 2021. "Asymmetric spillover and network connectedness between crude oil, gold, and Chinese sector stock markets," Energy Economics, Elsevier, vol. 98(C).
    9. Samir Cedic & Alwan Mahmoud & Matteo Manera & Gazi Salah Uddin, 2021. "Information Diffusion and Spillover Dynamics in Renewable Energy Markets," Working Papers 2021.10, Fondazione Eni Enrico Mattei.
    10. Naeem, Muhammad Abubakr & Peng, Zhe & Suleman, Mouhammed Tahir & Nepal, Rabindra & Shahzad, Syed Jawad Hussain, 2020. "Time and frequency connectedness among oil shocks, electricity and clean energy markets," Energy Economics, Elsevier, vol. 91(C).
    11. Tiantian Liu & Shigeyuki Hamori, 2020. "Spillovers to Renewable Energy Stocks in the US and Europe: Are They Different?," Energies, MDPI, vol. 13(12), pages 1-28, June.
    12. Ziadat, Salem Adel & Mensi, Walid & Al-Kharusi, Sami & Vo, Xuan Vinh & Kang, Sang Hoon, 2024. "Are clean energy markets hedges for stock markets? A tail quantile connectedness regression," Energy Economics, Elsevier, vol. 136(C).
    13. Urom, Christian & Mzoughi, Hela & Abid, Ilyes & Brahim, Mariem, 2021. "Green markets integration in different time scales: A regional analysis," Energy Economics, Elsevier, vol. 98(C).
    14. Sohag, Kazi & Hassan, M. Kabir & Bakhteyev, Stepan & Mariev, Oleg, 2023. "Do green and dirty investments hedge each other?," Energy Economics, Elsevier, vol. 120(C).
    15. Lovcha, Yuliya & Perez-Laborda, Alejandro, 2020. "Dynamic frequency connectedness between oil and natural gas volatilities," Economic Modelling, Elsevier, vol. 84(C), pages 181-189.
    16. Elsayed, Ahmed H. & Nasreen, Samia & Tiwari, Aviral Kumar, 2020. "Time-varying co-movements between energy market and global financial markets: Implication for portfolio diversification and hedging strategies," Energy Economics, Elsevier, vol. 90(C).
    17. Lovcha, Yuliya & Perez-Laborda, Alejandro, 2022. "Long-memory and volatility spillovers across petroleum futures," Energy, Elsevier, vol. 243(C).
    18. Li, Jingpeng & Umar, Muhammad & Huo, Jiale, 2023. "The spillover effect between Chinese crude oil futures market and Chinese green energy stock market," Energy Economics, Elsevier, vol. 119(C).
    19. Zheng, Biao & Zhang, Yuquan & Chen, Yufeng, 2021. "Asymmetric connectedness and dynamic spillovers between renewable energy and rare earth markets in China: Evidence from firms’ high-frequency data," Resources Policy, Elsevier, vol. 71(C).
    20. Tan, Xueping & Geng, Yong & Vivian, Andrew & Wang, Xinyu, 2021. "Measuring risk spillovers between oil and clean energy stocks: Evidence from a systematic framework," Resources Policy, Elsevier, vol. 74(C).

    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:spr:comgts:v:20:y:2023:i:1:d:10.1007_s10287-023-00436-4. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.