IDEAS home Printed from https://ideas.repec.org/a/eee/reveco/v88y2023icp92-106.html
   My bibliography  Save this article

Factors behind the performance of green bond markets

Author

Listed:
  • Adekoya, Oluwasegun B.
  • Abakah, Emmanuel J.A.
  • Oliyide, Johnson A.
  • Luis A, Gil-Alana

Abstract

The market for green bonds has grown dramatically over the past several years, necessitating an understanding of the variables that might forecast its performance. Studies on how the green bond market interacts with other markets are widely discussed in the literature, but little is known about the variables that improve predictions of green bond returns. In this study, we use data on commodity and financial asset prices, as well as speculative factors, to predict the returns on green bonds using the Feasible Quasi-Generalized Least Squares (FQGLS) and the causality-in-quantiles estimators. The findings demonstrate that most factors are significant predictors of the returns on green bonds, with speculative factors having a detrimental predictive influence, and commodity and financial asset prices having a mixed predictive impact. When asymmetries are taken into account, the asymmetric predictive model performs better at predicting the returns on green bonds than its symmetric counterpart in most instances. Finally, all the factors, except investors' sentiment, affect the returns on green bonds in a variety of market situations. The interdependence among the global financial and commodity markets, as well as economic uncertainties justify the established predictive influence, since green bonds are a component of the broader investment bonds.

Suggested Citation

  • Adekoya, Oluwasegun B. & Abakah, Emmanuel J.A. & Oliyide, Johnson A. & Luis A, Gil-Alana, 2023. "Factors behind the performance of green bond markets," International Review of Economics & Finance, Elsevier, vol. 88(C), pages 92-106.
  • Handle: RePEc:eee:reveco:v:88:y:2023:i:c:p:92-106
    DOI: 10.1016/j.iref.2023.06.015
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1059056023001831
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.iref.2023.06.015?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. John Y. Campbell & Samuel B. Thompson, 2008. "Predicting Excess Stock Returns Out of Sample: Can Anything Beat the Historical Average?," The Review of Financial Studies, Society for Financial Studies, vol. 21(4), pages 1509-1531, July.
    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. Hu, Yuanfeng & Tian, Yixiang, 2024. "The role of green reputation, carbon trading and government intervention in determining the green bond pricing: An externality perspective," International Review of Economics & Finance, Elsevier, vol. 89(PB), pages 46-62.
    2. Gao, Yang & Zhou, Yueyi & Zhao, Longfeng, 2024. "Quantile interdependence and network connectedness between China's green financial and energy markets," Economic Analysis and Policy, Elsevier, vol. 81(C), pages 1148-1177.
    3. Zhong, Yufei & Chen, Xuesheng & Wang, Chengfang & Wang, Zhixian & Zhang, Yuchen, 2023. "The hedging performance of green bond markets in China and the U.S.: Novel evidence from cryptocurrency uncertainty," Energy Economics, Elsevier, vol. 128(C).
    4. Cheng, Xuanmei & Yan, Chengnuo & Ye, Kaite & Chen, Kanxiang, 2024. "Enhancing resource efficiency through the utilization of the green bond market: An empirical analysis of Asian economies," Resources Policy, Elsevier, vol. 89(C).
    5. Fameliti Stavroula & Skintzi Vasiliki, 2024. "Macroeconomic attention and commodity market volatility," Empirical Economics, Springer, vol. 67(5), pages 1967-2007, November.
    6. Wei Su, Chi & Yue Song, Xin & Qin, Meng & Lobonţ, Oana-Ramona & Umar, Muhammad, 2024. "Optimistic or pessimistic: How do investors impact the green bond market?," The North American Journal of Economics and Finance, Elsevier, vol. 74(C).
    7. Wei, Yu & Shi, Chunpei & Zhou, Chunyan & Wang, Qian & Liu, Yuntong & Wang, Yizhi, 2024. "Market volatilities vs oil shocks: Which dominate the relative performance of green bonds?," Energy Economics, Elsevier, vol. 136(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. Gkillas, Konstantinos & Gupta, Rangan & Pierdzioch, Christian, 2020. "Forecasting realized oil-price volatility: The role of financial stress and asymmetric loss," Journal of International Money and Finance, Elsevier, vol. 104(C).
    2. Liu, Shan & Li, Ziwei, 2023. "Macroeconomic attention and oil futures volatility prediction," Finance Research Letters, Elsevier, vol. 57(C).
    3. Faria, Gonçalo & Verona, Fabio, 2023. "Forecast combination in the frequency domain," Bank of Finland Research Discussion Papers 1/2023, Bank of Finland.
    4. Papapostolou, Nikos C. & Pouliasis, Panos K. & Nomikos, Nikos K. & Kyriakou, Ioannis, 2016. "Shipping investor sentiment and international stock return predictability," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 96(C), pages 81-94.
    5. Wang, Yubao & Huang, Xiaozhou & Huang, Zhendong, 2024. "Energy-related uncertainty and Chinese stock market returns," Finance Research Letters, Elsevier, vol. 62(PB).
    6. Davide Pettenuzzo & Francesco Ravazzolo, 2016. "Optimal Portfolio Choice Under Decision‐Based Model Combinations," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 31(7), pages 1312-1332, November.
    7. Yue-Jun Zhang & Han Zhang, 2023. "Volatility Forecasting of Crude Oil Market: Which Structural Change Based GARCH Models have Better Performance?," The Energy Journal, , vol. 44(1), pages 175-194, January.
    8. Esther Eiling & Raymond Kan & Ali Sharifkhani, 2018. "Sectoral Labor Reallocation and Return Predictability," Working Papers 2018-006, Human Capital and Economic Opportunity Working Group.
    9. Chia-Lin Chang & Jukka Ilomäki & Hannu Laurila & Michael McAleer, 2018. "Long Run Returns Predictability and Volatility with Moving Averages," Risks, MDPI, vol. 6(4), pages 1-18, September.
    10. Su, Yuandong & Lu, Xinjie & Zeng, Qing & Huang, Dengshi, 2022. "Good air quality and stock market returns," Research in International Business and Finance, Elsevier, vol. 62(C).
    11. Söderlind, Paul, 2009. "The C-CAPM without ex post data," Journal of Macroeconomics, Elsevier, vol. 31(4), pages 721-729, December.
    12. Zongwu Cai & Haiqiang Chen & Xiaosai Liao, 2020. "A New Robust Inference for Predictive Quantile Regression," WORKING PAPERS SERIES IN THEORETICAL AND APPLIED ECONOMICS 202002, University of Kansas, Department of Economics, revised Feb 2020.
    13. Salisu, Afees A. & Ademuyiwa, Idris & Isah, Kazeem O., 2018. "Revisiting the forecasting accuracy of Phillips curve: The role of oil price," Energy Economics, Elsevier, vol. 70(C), pages 334-356.
    14. Clark, Todd E. & McCracken, Michael W., 2012. "In-sample tests of predictive ability: A new approach," Journal of Econometrics, Elsevier, vol. 170(1), pages 1-14.
    15. Chen, Jian & Jiang, Fuwei & Liu, Yangshu & Tu, Jun, 2017. "International volatility risk and Chinese stock return predictability," Journal of International Money and Finance, Elsevier, vol. 70(C), pages 183-203.
    16. Peñaranda, Francisco & Sentana, Enrique, 2016. "Duality in mean-variance frontiers with conditioning information," Journal of Empirical Finance, Elsevier, vol. 38(PB), pages 762-785.
    17. Fernando M. Duarte & Carlo Rosa, 2015. "The equity risk premium: a review of models," Economic Policy Review, Federal Reserve Bank of New York, issue 2, pages 39-57.
    18. Dunbar, Kwamie, 2021. "Pricing the hedging factor in the cross-section of stock returns," The North American Journal of Economics and Finance, Elsevier, vol. 56(C).
    19. Wang, Yudong & Liu, Li & Diao, Xundi & Wu, Chongfeng, 2015. "Forecasting the real prices of crude oil under economic and statistical constraints," Energy Economics, Elsevier, vol. 51(C), pages 599-608.
    20. Thomas Nitschka, 2012. "Global and country-specific business cycle risk in time-varying excess returns on asset markets," Working Papers 2012-10, Swiss National Bank.

    More about this item

    Keywords

    Green bond; Commodities; Financials; Uncertainties; Predictability;
    All these keywords.

    JEL classification:

    • C2 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)
    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets

    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:reveco:v:88:y:2023:i:c:p:92-106. 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/620165 .

    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.