IDEAS home Printed from https://ideas.repec.org/a/eee/finana/v89y2023ics1057521923002302.html
   My bibliography  Save this article

Time-varying linkages between energy and stock markets: Dynamic spillovers and driving factors

Author

Listed:
  • Feng, Huiqun
  • Zhang, Jun
  • Guo, Na

Abstract

Integration between international energy prices and stock market returns is critical for global economics and politics. In this study, we employ a TVP-VAR (time-varying parameter vector autoregression) connectedness decomposition approach to investigate the time-varying linkages between a diversified energy portfolio comprising oil, coal, natural gas, and stock returns in G7 countries and China. This approach allows us to show the dynamic spillovers and explore the driving factors underlying the dynamic patterns. We find that geopolitical risks, global economic policy uncertainties, and equity market volatility can influence cross-market spillovers. This study expounds the effect of energy financialization.

Suggested Citation

  • Feng, Huiqun & Zhang, Jun & Guo, Na, 2023. "Time-varying linkages between energy and stock markets: Dynamic spillovers and driving factors," International Review of Financial Analysis, Elsevier, vol. 89(C).
    • Handle: RePEc:eee:finana:v:89:y:2023:i:c:s1057521923002302
    DOI: 10.1016/j.irfa.2023.102714
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1057521923002302
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.irfa.2023.102714?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. Mensi, Walid & Rehman, Mobeen Ur & Maitra, Debasish & Al-Yahyaee, Khamis Hamed & Vo, Xuan Vinh, 2021. "Oil, natural gas and BRICS stock markets: Evidence of systemic risks and co-movements in the time-frequency domain," Resources Policy, Elsevier, vol. 72(C).
    2. Ling, Shiqing & McAleer, Michael, 2003. "Asymptotic Theory For A Vector Arma-Garch Model," Econometric Theory, Cambridge University Press, vol. 19(2), pages 280-310, April.
    3. Engle, Robert F. & Kroner, Kenneth F., 1995. "Multivariate Simultaneous Generalized ARCH," Econometric Theory, Cambridge University Press, vol. 11(1), pages 122-150, February.
    4. 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.
    5. Karali, Berna & Ramirez, Octavio A., 2014. "Macro determinants of volatility and volatility spillover in energy markets," Energy Economics, Elsevier, vol. 46(C), pages 413-421.
    6. Xie, Qiwei & Liu, Ranran & Qian, Tao & Li, Jingyu, 2021. "Linkages between the international crude oil market and the Chinese stock market: A BEKK-GARCH-AFD approach," Energy Economics, Elsevier, vol. 102(C).
    7. Dario Caldara & Matteo Iacoviello, 2022. "Measuring Geopolitical Risk," American Economic Review, American Economic Association, vol. 112(4), pages 1194-1225, April.
    8. Georgiadis, Georgios & Jančoková, Martina, 2020. "Financial globalisation, monetary policy spillovers and macro-modelling: Tales from 1001 shocks," Journal of Economic Dynamics and Control, Elsevier, vol. 121(C).
    9. Scott R. Baker & Nicholas Bloom & Steven J. Davis, 2016. "Measuring Economic Policy Uncertainty," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 131(4), pages 1593-1636.
    10. Zolfaghari, Mehdi & Ghoddusi, Hamed & Faghihian, Fatemeh, 2020. "Volatility spillovers for energy prices: A diagonal BEKK approach," Energy Economics, Elsevier, vol. 92(C).
    11. Hou, Yang & Li, Steven & Wen, Fenghua, 2019. "Time-varying volatility spillover between Chinese fuel oil and stock index futures markets based on a DCC-GARCH model with a semi-nonparametric approach," Energy Economics, Elsevier, vol. 83(C), pages 119-143.
    12. Koop, Gary & Pesaran, M. Hashem & Potter, Simon M., 1996. "Impulse response analysis in nonlinear multivariate models," Journal of Econometrics, Elsevier, vol. 74(1), pages 119-147, September.
    13. 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.
    14. Malik, Farooq & Ewing, Bradley T., 2009. "Volatility transmission between oil prices and equity sector returns," International Review of Financial Analysis, Elsevier, vol. 18(3), pages 95-100, June.
    15. Awartani, Basel & Maghyereh, Aktham Issa, 2013. "Dynamic spillovers between oil and stock markets in the Gulf Cooperation Council Countries," Energy Economics, Elsevier, vol. 36(C), pages 28-42.
    16. Koop, Gary & Korobilis, Dimitris, 2014. "A new index of financial conditions," European Economic Review, Elsevier, vol. 71(C), pages 101-116.
    17. Pesaran, H. Hashem & Shin, Yongcheol, 1998. "Generalized impulse response analysis in linear multivariate models," Economics Letters, Elsevier, vol. 58(1), pages 17-29, January.
    18. Zihui Yang & Yinggang Zhou, 2017. "Quantitative Easing and Volatility Spillovers Across Countries and Asset Classes," Management Science, INFORMS, vol. 63(2), pages 333-354, February.
    19. Wen, Xiaoqian & Wei, Yu & Huang, Dengshi, 2012. "Measuring contagion between energy market and stock market during financial crisis: A copula approach," Energy Economics, Elsevier, vol. 34(5), pages 1435-1446.
    20. Jon Wongswan, 2006. "Transmission of Information across International Equity Markets," The Review of Financial Studies, Society for Financial Studies, vol. 19(4), pages 1157-1189.
    21. Giorgio E. Primiceri, 2005. "Time Varying Structural Vector Autoregressions and Monetary Policy," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 72(3), pages 821-852.
    22. Scott R. Baker & Nicholas Bloom & Steven J. Davis & Kyle J. Kost, 2019. "Policy News and Stock Market Volatility," NBER Working Papers 25720, National Bureau of Economic Research, Inc.
    23. Jacks, David S. & Stuermer, Martin, 2020. "What drives commodity price booms and busts?," Energy Economics, Elsevier, vol. 85(C).
    24. Khalfaoui, R. & Boutahar, M. & Boubaker, H., 2015. "Analyzing volatility spillovers and hedging between oil and stock markets: Evidence from wavelet analysis," Energy Economics, Elsevier, vol. 49(C), pages 540-549.
    25. Du, Limin & He, Yanan, 2015. "Extreme risk spillovers between crude oil and stock markets," Energy Economics, Elsevier, vol. 51(C), pages 455-465.
    26. Wang, Xunxiao & Wu, Chongfeng, 2018. "Asymmetric volatility spillovers between crude oil and international financial markets," Energy Economics, Elsevier, vol. 74(C), pages 592-604.
    27. 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.
    28. Peter R. Hartley & Kenneth B Medlock III & Jennifer E. Rosthal, 2008. "The Relationship of Natural Gas to Oil Prices," The Energy Journal, International Association for Energy Economics, vol. 0(Number 3), pages 47-66.
    29. Korobilis, D & Yilmaz, K, 2018. "Measuring Dynamic Connectedness with Large Bayesian VAR Models," Essex Finance Centre Working Papers 20937, University of Essex, Essex Business School.
    30. Jouchi Nakajima, 2011. "Time-Varying Parameter VAR Model with Stochastic Volatility: An Overview of Methodology and Empirical Applications," Monetary and Economic Studies, Institute for Monetary and Economic Studies, Bank of Japan, vol. 29, pages 107-142, November.
    31. Antonakakis, Nikolaos & Gabauer, David, 2017. "Refined Measures of Dynamic Connectedness based on TVP-VAR," MPRA Paper 78282, University Library of Munich, Germany.
    32. Baker, Malcolm & Wurgler, Jeffrey & Yuan, Yu, 2012. "Global, local, and contagious investor sentiment," Journal of Financial Economics, Elsevier, vol. 104(2), pages 272-287.
    33. Chortareas, Georgios & Noikokyris, Emmanouil, 2017. "Federal reserve's policy, global equity markets, and the local monetary policy stance," Journal of Banking & Finance, Elsevier, vol. 77(C), pages 317-327.
    34. Engle, Robert, 2002. "Dynamic Conditional Correlation: A Simple Class of Multivariate Generalized Autoregressive Conditional Heteroskedasticity Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(3), pages 339-350, July.
    35. Liu, Bing-Yue & Fan, Ying & Ji, Qiang & Hussain, Nazim, 2022. "High-dimensional CoVaR network connectedness for measuring conditional financial contagion and risk spillovers from oil markets to the G20 stock system," Energy Economics, Elsevier, vol. 105(C).
    36. 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.
    37. Geng, Jiang-Bo & Chen, Fu-Rui & Ji, Qiang & Liu, Bing-Yue, 2021. "Network connectedness between natural gas markets, uncertainty and stock markets," Energy Economics, Elsevier, vol. 95(C).
    38. 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).
    39. Rabeh KHALFAOUI & M. Boutahar & H. Boubaker, 2015. "Analyzing volatility spillovers and hedging between oil and stock markets: Evidence from wavelet analysis," Post-Print hal-03797593, HAL.
    40. Nikolaos Antonakakis & Ioannis Chatziantoniou & David Gabauer, 2020. "Refined Measures of Dynamic Connectedness based on Time-Varying Parameter Vector Autoregressions," JRFM, MDPI, vol. 13(4), pages 1-23, April.
    41. Gabauer, David & Gupta, Rangan, 2018. "On the transmission mechanism of country-specific and international economic uncertainty spillovers: Evidence from a TVP-VAR connectedness decomposition approach," Economics Letters, Elsevier, vol. 171(C), pages 63-71.
    42. Leduc, Sylvain & Liu, Zheng, 2016. "Uncertainty shocks are aggregate demand shocks," Journal of Monetary Economics, Elsevier, vol. 82(C), pages 20-35.
    43. Zhang, Dayong, 2017. "Oil shocks and stock markets revisited: Measuring connectedness from a global perspective," Energy Economics, Elsevier, vol. 62(C), pages 323-333.
    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. Wang, Xiaoyu & Wang, Jiaojiao & Wang, Wenhuan & Zhang, Shuquan, 2023. "International and Chinese energy markets: Dynamic spillover effects," Energy, Elsevier, vol. 282(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. Ghaemi Asl, Mahdi & Adekoya, Oluwasegun Babatunde & Rashidi, Muhammad Mahdi & Ghasemi Doudkanlou, Mohammad & Dolatabadi, Ali, 2022. "Forecast of Bayesian-based dynamic connectedness between oil market and Islamic stock indices of Islamic oil-exporting countries: Application of the cascade-forward backpropagation network," Resources Policy, Elsevier, vol. 77(C).
    2. Coskun, Merve & Taspinar, Nigar, 2022. "Volatility spillovers between Turkish energy stocks and fossil fuel energy commodities based on time and frequency domain approaches," Resources Policy, Elsevier, vol. 79(C).
    3. Assaf, Ata & Charif, Husni & Mokni, Khaled, 2021. "Dynamic connectedness between uncertainty and energy markets: Do investor sentiments matter?," Resources Policy, Elsevier, vol. 72(C).
    4. Evrim Mandacı, Pınar & Cagli, Efe Çaglar & Taşkın, Dilvin, 2020. "Dynamic connectedness and portfolio strategies: Energy and metal markets," Resources Policy, Elsevier, vol. 68(C).
    5. Antonakakis, Nikolaos & Cunado, Juncal & Filis, George & Gabauer, David & Perez de Gracia, Fernando, 2018. "Oil volatility, oil and gas firms and portfolio diversification," Energy Economics, Elsevier, vol. 70(C), pages 499-515.
    6. Lee, Chien-Chiang & Zhou, Hegang & Xu, Chao & Zhang, Xiaoming, 2023. "Dynamic spillover effects among international crude oil markets from the time-frequency perspective," Resources Policy, Elsevier, vol. 80(C).
    7. Antonakakis, Nikolaos & Cunado, Juncal & Filis, George & Gabauer, David & de Gracia, Fernando Perez, 2023. "Dynamic connectedness among the implied volatilities of oil prices and financial assets: New evidence of the COVID-19 pandemic," International Review of Economics & Finance, Elsevier, vol. 83(C), pages 114-123.
    8. Zhang, Yulian & Hamori, Shigeyuki, 2021. "Do news sentiment and the economic uncertainty caused by public health events impact macroeconomic indicators? Evidence from a TVP-VAR decomposition approach," The Quarterly Review of Economics and Finance, Elsevier, vol. 82(C), pages 145-162.
    9. Lovcha, Yuliya & Perez-Laborda, Alejandro, 2020. "Dynamic frequency connectedness between oil and natural gas volatilities," Economic Modelling, Elsevier, vol. 84(C), pages 181-189.
    10. Yousaf, Imran & Jareño, Francisco & Tolentino, Marta, 2023. "Connectedness between Defi assets and equity markets during COVID-19: A sector analysis," Technological Forecasting and Social Change, Elsevier, vol. 187(C).
    11. Nikolaos Antonakakis & Ioannis Chatziantoniou & David Gabauer, 2020. "Refined Measures of Dynamic Connectedness based on Time-Varying Parameter Vector Autoregressions," JRFM, MDPI, vol. 13(4), pages 1-23, April.
    12. Chatziantoniou, Ioannis & Gabauer, David & Stenfors, Alexis, 2020. "From CIP-deviations to a market for risk premia: A dynamic investigation of cross-currency basis swaps," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 69(C).
    13. Mahdi Ghaemi Asl & Oluwasegun B. Adekoya & Muhammad Mahdi Rashidi, 2023. "Quantiles dependence and dynamic connectedness between distributed ledger technology and sectoral stocks: enhancing the supply chain and investment decisions with digital platforms," Annals of Operations Research, Springer, vol. 327(1), pages 435-464, August.
    14. Mensi, Walid & Vo, Xuan Vinh & Kang, Sang Hoon, 2023. "Quantile spillovers and connectedness analysis between oil and African stock markets," Economic Analysis and Policy, Elsevier, vol. 78(C), pages 60-83.
    15. Al-Shboul, Mohammad & Assaf, Ata & Mokni, Khaled, 2023. "Does economic policy uncertainty drive the dynamic spillover among traditional currencies and cryptocurrencies? The role of the COVID-19 pandemic," Research in International Business and Finance, Elsevier, vol. 64(C).
    16. Wang, Tiantian & Wu, Fei & Zhang, Dayong & Ji, Qiang, 2023. "Energy market reforms in China and the time-varying connectedness of domestic and international markets," Energy Economics, Elsevier, vol. 117(C).
    17. Manel Youssef & Khaled Mokni & Ahdi Noomen Ajmi, 2021. "Dynamic connectedness between stock markets in the presence of the COVID-19 pandemic: does economic policy uncertainty matter?," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 7(1), pages 1-27, December.
    18. Chatziantoniou, Ioannis & Gabauer, David, 2021. "EMU risk-synchronisation and financial fragility through the prism of dynamic connectedness," The Quarterly Review of Economics and Finance, Elsevier, vol. 79(C), pages 1-14.
    19. Chan, Ying Tung & Qiao, Hui, 2023. "Volatility spillover between oil and stock prices: Structural connectedness based on a multi-sector DSGE model approach with Bayesian estimation," International Review of Economics & Finance, Elsevier, vol. 87(C), pages 265-286.
    20. Zhang, Jiahao & Chen, Xiaodan & Wei, Yu & Bai, Lan, 2023. "Does the connectedness among fossil energy returns matter for renewable energy stock returns? Fresh insights from the Cross-Quantilogram analysis," International Review of Financial Analysis, Elsevier, vol. 88(C).

    More about this item

    Keywords

    Dynamic spillovers; Energy market; Stock market; TVP-VAR;
    All these keywords.

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C50 - Mathematical and Quantitative Methods - - Econometric Modeling - - - General
    • 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:finana:v:89:y:2023:i:c:s1057521923002302. 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/620166 .

    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.