IDEAS home Printed from https://ideas.repec.org/a/eee/reveco/v88y2023icp347-364.html

How does bubble risk propagate among financial assets? A perspective from the BSADF-vine copula model

Author

Listed:
  • Yao, Can-Zhong
  • Li, Min-Jian
  • Xu, Xin

Abstract

In this paper, the BSADF method is used to construct the bubble net value sequence of 11 mainstream financial assets, and the vine copula model is used to analyze the characteristics of the bubble-related structure. First, we use the BSADF method to measure the bubbles of various financial assets. The results show that since the outbreak of COVID-19, most types of assets have experienced a sharp bubble rise and their longest bubble period as central banks have implemented quantitative easing policies. Furthermore, this article reveals the path characteristics of bubble risk transmission in different assets using the vine copula model. The R vine copula is found to be best for analyzing the tail dependence structure. The S&P 500 and the international gold price occupy the hub position in the bubble dependence structure of various financial assets. The Shanghai Composite Index and the US Dollar Index can act as relays and accelerators in the transmission of bubble risk across the financial system. Finally, we explore the tail dependence structure between financial assets. The results indicate that gold can be a good hedge against the bubble risk of the Hang Seng Index and WTI crude oil futures. The COVID-19 pandemic and accommodative monetary policies by governments can create conditions for bubble risk to spread between assets such as Bitcoin and China’s 10-year Treasury bonds and between the US Dollar Index and the S&P 500.

Suggested Citation

  • Yao, Can-Zhong & Li, Min-Jian & Xu, Xin, 2023. "How does bubble risk propagate among financial assets? A perspective from the BSADF-vine copula model," International Review of Economics & Finance, Elsevier, vol. 88(C), pages 347-364.
    • Handle: RePEc:eee:reveco:v:88:y:2023:i:c:p:347-364
    DOI: 10.1016/j.iref.2023.06.027
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S105905602300196X
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.iref.2023.06.027?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. Li, Yan & Chevallier, Julien & Wei, Yigang & Li, Jing, 2020. "Identifying price bubbles in the US, European and Asian natural gas market: Evidence from a GSADF test approach," Energy Economics, Elsevier, vol. 87(C).
    2. Vuong, Quang H, 1989. "Likelihood Ratio Tests for Model Selection and Non-nested Hypotheses," Econometrica, Econometric Society, vol. 57(2), pages 307-333, March.
    3. Michis, Antonis A., 2014. "Investing in gold: Individual asset risk in the long run," Finance Research Letters, Elsevier, vol. 11(4), pages 369-374.
    4. Charfeddine, Lanouar & Benlagha, Noureddine & Maouchi, Youcef, 2020. "Investigating the dynamic relationship between cryptocurrencies and conventional assets: Implications for financial investors," Economic Modelling, Elsevier, vol. 85(C), pages 198-217.
    5. Dißmann, J. & Brechmann, E.C. & Czado, C. & Kurowicka, D., 2013. "Selecting and estimating regular vine copulae and application to financial returns," Computational Statistics & Data Analysis, Elsevier, vol. 59(C), pages 52-69.
    6. Nikoloulopoulos, Aristidis K. & Joe, Harry & Li, Haijun, 2012. "Vine copulas with asymmetric tail dependence and applications to financial return data," Computational Statistics & Data Analysis, Elsevier, vol. 56(11), pages 3659-3673.
    7. Liu, Tangyong & Gong, Xu, 2020. "Analyzing time-varying volatility spillovers between the crude oil markets using a new method," Energy Economics, Elsevier, vol. 87(C).
    8. Hott, Christian, 2009. "Herding behavior in asset markets," Journal of Financial Stability, Elsevier, vol. 5(1), pages 35-56, January.
    9. Yao, Can-Zhong & Li, Hong-Yu, 2021. "A study on the bursting point of Bitcoin based on the BSADF and LPPLS methods," The North American Journal of Economics and Finance, Elsevier, vol. 55(C).
    10. Garber, Peter M, 1990. "Famous First Bubbles," Journal of Economic Perspectives, American Economic Association, vol. 4(2), pages 35-54, Spring.
    11. Wang, Dong & Li, Ping & Huang, Lixin, 2022. "Time-frequency volatility spillovers between major international financial markets during the COVID-19 pandemic," Finance Research Letters, Elsevier, vol. 46(PA).
    12. Yi, Shuyue & Xu, Zishuang & Wang, Gang-Jin, 2018. "Volatility connectedness in the cryptocurrency market: Is Bitcoin a dominant cryptocurrency?," International Review of Financial Analysis, Elsevier, vol. 60(C), pages 98-114.
    13. Jin, Xiaoye, 2018. "Downside and upside risk spillovers from China to Asian stock markets: A CoVaR-copula approach," Finance Research Letters, Elsevier, vol. 25(C), pages 202-212.
    14. Peter C. B. Phillips & Yangru Wu & Jun Yu, 2011. "EXPLOSIVE BEHAVIOR IN THE 1990s NASDAQ: WHEN DID EXUBERANCE ESCALATE ASSET VALUES?," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 52(1), pages 201-226, February.
    15. Tong, Bin & Wu, Chongfeng & Zhou, Chunyang, 2013. "Modeling the co-movements between crude oil and refined petroleum markets," Energy Economics, Elsevier, vol. 40(C), pages 882-897.
    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. Lupu, Radu & Călin, Adrian Cantemir & Dumitrescu, Dan Gabriel & Lupu, Iulia, 2025. "Introducing a novel fragility index for assessing financial stability amid asset bubble episodes," The North American Journal of Economics and Finance, Elsevier, vol. 75(PA).

    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. Chen, Bin-xia & Sun, Yan-lin, 2024. "Risk characteristics and connectedness in cryptocurrency markets: New evidence from a non-linear framework," The North American Journal of Economics and Finance, Elsevier, vol. 69(PA).
    2. Akcora, Begum & Kandemir Kocaaslan, Ozge, 2023. "Price bubbles in the European natural gas market between 2011 and 2020," Resources Policy, Elsevier, vol. 80(C).
    3. Dalla Valle, Luciana & De Giuli, Maria Elena & Tarantola, Claudia & Manelli, Claudio, 2016. "Default probability estimation via pair copula constructions," European Journal of Operational Research, Elsevier, vol. 249(1), pages 298-311.
    4. Sayed H. Kadhem & Aristidis K. Nikoloulopoulos, 2023. "Bi-factor and Second-Order Copula Models for Item Response Data," Psychometrika, Springer;The Psychometric Society, vol. 88(1), pages 132-157, March.
    5. Brechmann, Eike Christian & Schepsmeier, Ulf, 2013. "Modeling Dependence with C- and D-Vine Copulas: The R Package CDVine," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 52(i03).
    6. Roger M. Cooke & Harry Joe & Bo Chang, 2020. "Vine copula regression for observational studies," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 104(2), pages 141-167, June.
    7. Paulo M.M. Rodrigues & Rita Fradique Lourenço, 2015. "House prices: bubbles, exuberance or something else? Evidence from euro area countries," Working Papers w201517, Banco de Portugal, Economics and Research Department.
    8. Philip Hans Franses & Wouter Knecht, 2016. "The late 1970s bubble in Dutch collectible postage stamps," Empirical Economics, Springer, vol. 50(4), pages 1215-1228, June.
    9. Zhang, Dalu, 2014. "Vine copulas and applications to the European Union sovereign debt analysis," International Review of Financial Analysis, Elsevier, vol. 36(C), pages 46-56.
    10. Arfaoui, Nadia & Naeem, Muhammad Abubakr & Boubaker, Sabri & Mirza, Nawazish & Karim, Sitara, 2023. "Interdependence of clean energy and green markets with cryptocurrencies," Energy Economics, Elsevier, vol. 120(C).
    11. Assaf, Ata & Demir, Ender & Ersan, Oguz, 2024. "Detecting and date-stamping bubbles in fan tokens," International Review of Economics & Finance, Elsevier, vol. 92(C), pages 98-113.
    12. Wang, Zuyi & Kim, Man-Keun, 2022. "Price bubbles in oil & gas markets and their transfer," Resources Policy, Elsevier, vol. 79(C).
    13. Çağlar Sözen, 2026. "Multidimensional analysis of sectoral equity dynamics: deconstructing market trends via functional data and vine copula integration," Journal of Asset Management, Palgrave Macmillan, vol. 27(1), pages 1-14, March.
    14. Yintian Wang & Guofu Zhou & Yingzi Zhu, 2021. "The Chinese warrant bubble: A fundamental analysis," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 41(1), pages 3-26, January.
    15. Maximilian-Benedikt Herwarth Kohn & Pedro L. Valls Pereira, 2017. "Speculative bubbles and contagion: Analysis of volatility’s clusters during the DotCom bubble based on the dynamic conditional correlation model," Cogent Economics & Finance, Taylor & Francis Journals, vol. 5(1), pages 1411453-141, January.
    16. Sercan Demiralay & Selçuk Bayracı, 2021. "Should stock investors include cryptocurrencies in their portfolios after all? Evidence from a conditional diversification benefits measure," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 26(4), pages 6188-6204, October.
    17. Gutierrez, Luciano, 2011. "Bootstrapping asset price bubbles," Economic Modelling, Elsevier, vol. 28(6), pages 2488-2493.
    18. Aristidis K. Nikoloulopoulos, 2022. "An one‐factor copula mixed model for joint meta‐analysis of multiple diagnostic tests," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(3), pages 1398-1423, July.
    19. Cincinelli, Peter & Pellini, Elisabetta, 2025. "The role of geopolitical and climate risk in driving uncertainty in European electricity markets," Energy Economics, Elsevier, vol. 144(C).
    20. Martin Magris, 2019. "A Vine-copula extension for the HAR model," Papers 1907.08522, arXiv.org.

    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:reveco:v:88:y:2023:i:c:p:347-364. 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.