IDEAS home Printed from https://ideas.repec.org/a/eee/ecolet/v212y2022ics0165176522000192.html
   My bibliography  Save this article

The varying spillover of U.S. systemic risk: A functional-coefficient cointegration approach

Author

Listed:
  • Li, Li
  • Tu, Yundong

Abstract

We examine the spillover effects of systemic risk from U.S. to U.K. based on a functional-coefficient cointegration approach. We find that the international spillover of systemic risk is varying as the global financial cycle evolves. When the global financial market is booming or busting, the systemic risk spillover effect could be elevated. The forecasting experiment suggests that the systemic risk spillover through the global financial factor can help improve the forecasting accuracy of future systemic risk in U.K.

Suggested Citation

  • Li, Li & Tu, Yundong, 2022. "The varying spillover of U.S. systemic risk: A functional-coefficient cointegration approach," Economics Letters, Elsevier, vol. 212(C).
  • Handle: RePEc:eee:ecolet:v:212:y:2022:i:c:s0165176522000192
    DOI: 10.1016/j.econlet.2022.110306
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0165176522000192
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.econlet.2022.110306?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. Sun, Yiguo & Cai, Zongwu & Li, Qi, 2016. "A Consistent Nonparametric Test On Semiparametric Smooth Coefficient Models With Integrated Time Series," Econometric Theory, Cambridge University Press, vol. 32(4), pages 988-1022, August.
    2. Rey, Hélène, 2015. "Dilemma not Trilemma: The Global Financial Cycle and Monetary Policy Independence," CEPR Discussion Papers 10591, C.E.P.R. Discussion Papers.
    3. Billio, Monica & Getmansky, Mila & Lo, Andrew W. & Pelizzon, Loriana, 2012. "Econometric measures of connectedness and systemic risk in the finance and insurance sectors," Journal of Financial Economics, Elsevier, vol. 104(3), pages 535-559.
    4. Phillips, Peter C.B. & Li, Degui & Gao, Jiti, 2017. "Estimating smooth structural change in cointegration models," Journal of Econometrics, Elsevier, vol. 196(1), pages 180-195.
    5. Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-144, January.
    6. Giglio, Stefano & Kelly, Bryan & Pruitt, Seth, 2016. "Systemic risk and the macroeconomy: An empirical evaluation," Journal of Financial Economics, Elsevier, vol. 119(3), pages 457-471.
    7. Silvia Miranda-Agrippino & Hélène Rey, 2020. "U.S. Monetary Policy and the Global Financial Cycle," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 87(6), pages 2754-2776.
    8. Gu, Jingping & Liang, Zhongwen, 2014. "Testing cointegration relationship in a semiparametric varying coefficient model," Journal of Econometrics, Elsevier, vol. 178(P1), pages 57-70.
    9. Viral Acharya & Robert Engle & Matthew Richardson, 2012. "Capital Shortfall: A New Approach to Ranking and Regulating Systemic Risks," American Economic Review, American Economic Association, vol. 102(3), pages 59-64, May.
    10. Xiao, Zhijie, 2009. "Functional-coefficient cointegration models," Journal of Econometrics, Elsevier, vol. 152(2), pages 81-92, October.
    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. Yin, Libo & Feng, Jiabao & Han, Liyan, 2021. "Systemic risk in international stock markets: Role of the oil market," International Review of Economics & Finance, Elsevier, vol. 71(C), pages 592-619.
    2. Caporin, Massimiliano & Costola, Michele & Garibal, Jean-Charles & Maillet, Bertrand, 2022. "Systemic risk and severe economic downturns: A targeted and sparse analysis," Journal of Banking & Finance, Elsevier, vol. 134(C).
    3. Varotto, Simone & Zhao, Lei, 2018. "Systemic risk and bank size," Journal of International Money and Finance, Elsevier, vol. 82(C), pages 45-70.
    4. Abendschein, Michael & Grundke, Peter, 2018. "On the ranking consistency of global systemic risk measures: empirical evidence," VfS Annual Conference 2018 (Freiburg, Breisgau): Digital Economy 181623, Verein für Socialpolitik / German Economic Association.
    5. Ellis, Scott & Sharma, Satish & Brzeszczyński, Janusz, 2022. "Systemic risk measures and regulatory challenges," Journal of Financial Stability, Elsevier, vol. 61(C).
    6. Yang Deng & Chenyin Gao, 2023. "Where does the risk lie? Systemic risk and tail risk networks in the Chinese financial market," Pacific Economic Review, Wiley Blackwell, vol. 28(2), pages 167-190, May.
    7. Sessi Tokpavi, 2013. "Testing for the Systemically Important Financial Institutions: a Conditional Approach," Working Papers hal-04141194, HAL.
    8. Jean-Baptiste Hasse, 2022. "Systemic risk: a network approach," Empirical Economics, Springer, vol. 63(1), pages 313-344, July.
    9. Mert Demirer & Francis X. Diebold & Laura Liu & Kamil Yilmaz, 2018. "Estimating global bank network connectedness," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 33(1), pages 1-15, January.
    10. Moratis, Georgios & Sakellaris, Plutarchos, 2021. "Measuring the systemic importance of banks," Journal of Financial Stability, Elsevier, vol. 54(C).
    11. Garcia-Jorcano, Laura & Sanchis-Marco, Lidia, 2021. "Systemic-systematic risk in financial system: A dynamic ranking based on expectiles," International Review of Economics & Finance, Elsevier, vol. 75(C), pages 330-365.
    12. Jean-Baptiste Hasse, 2020. "Systemic Risk: a Network Approach," Working Papers halshs-02893780, HAL.
    13. Härdle, Wolfgang Karl & Wang, Weining & Yu, Lining, 2016. "TENET: Tail-Event driven NETwork risk," Journal of Econometrics, Elsevier, vol. 192(2), pages 499-513.
    14. Andrieş, Alin Marius & Ongena, Steven & Sprincean, Nicu & Tunaru, Radu, 2022. "Risk spillovers and interconnectedness between systemically important institutions," Journal of Financial Stability, Elsevier, vol. 58(C).
    15. Park, Sangjin & Yang, Jae-Suk, 2021. "Relationships between capital flow and economic growth: A network analysis," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 72(C).
    16. Lorenzo Frattarolo & Francesca Parpinel & Claudio Pizzi, 2020. "Combining permutation tests to rank systemically important banks," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 29(3), pages 581-596, September.
    17. Christian Brownlees & Robert F. Engle, 2017. "SRISK: A Conditional Capital Shortfall Measure of Systemic Risk," Review of Financial Studies, Society for Financial Studies, vol. 30(1), pages 48-79.
    18. Bevilacqua, Mattia & Tunaru, Radu & Vioto, Davide, 2023. "Options-based systemic risk, financial distress, and macroeconomic downturns," Journal of Financial Markets, Elsevier, vol. 65(C).
    19. Nucera, Federico & Schwaab, Bernd & Koopman, Siem Jan & Lucas, André, 2016. "The information in systemic risk rankings," Journal of Empirical Finance, Elsevier, vol. 38(PA), pages 461-475.
    20. Bevilacqua, Mattia & Tunaru, Radu & Vioto, Davide, 2020. "Options-based systemic risk, financial distress, and macroeconomic downturns," LSE Research Online Documents on Economics 118850, London School of Economics and Political Science, LSE Library.

    More about this item

    Keywords

    Global financial factor; Prediction; Spillover effect; Systemic risk;
    All these keywords.

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation

    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:ecolet:v:212:y:2022:i:c:s0165176522000192. 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/ecolet .

    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.