[go: up one dir, main page]
IDEAS home Printed from https://ideas.repec.org/p/mse/cesdoc/16045r.html
   My bibliography  Save this paper

Are critical slowing down indicators useful to detect financial crises?

Author

Abstract
In this article, we consider financial markets as complex dynamical systems, and check whether the critical slowing down indicators can be used as early warning signals to detect a phase transition. Using various rolling windows, we analyze the evolution of three indicators: i) First-order autocorrelation, ii) Variance, and iii) Skewness. Using daily data for ten European stock exchanges plus the United States, and focusing on the Global Financial Crisis, our results are mitigated and depend both on the series used and the indicator. Using the main (log) indices, critical slowing down indicators seem weak to predict to predict to Global Financial Crisis. Using cumulative returns, for almost all countries an increase in variance and skewness does precede the crisis. However, first-order autocorrelations of both log-indices and cumulative returns do not provide any useful information about the Global Financial Crisis. Thus, only some of the reported critical slowing down indicators may have informational content, and could be used as early warnings

Suggested Citation

  • Hayette Gatfaoui & Isabelle Nagot & Philippe de Peretti, 2016. "Are critical slowing down indicators useful to detect financial crises?," Documents de travail du Centre d'Economie de la Sorbonne 16045r, Université Panthéon-Sorbonne (Paris 1), Centre d'Economie de la Sorbonne, revised Nov 2016.
    • Handle: RePEc:mse:cesdoc:16045r
    as

    Download full text from publisher

    File URL: ftp://mse.univ-paris1.fr/pub/mse/CES2016/16045R.pdf
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. John Y. Campbell & Sanford J. Grossman & Jiang Wang, 1993. "Trading Volume and Serial Correlation in Stock Returns," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 108(4), pages 905-939.
    2. Cees Diks & Cars Hommes & Juanxi Wang, 2019. "Critical slowing down as an early warning signal for financial crises?," Empirical Economics, Springer, vol. 57(4), pages 1201-1228, October.
    3. Marten Scheffer & Jordi Bascompte & William A. Brock & Victor Brovkin & Stephen R. Carpenter & Vasilis Dakos & Hermann Held & Egbert H. van Nes & Max Rietkerk & George Sugihara, 2009. "Early-warning signals for critical transitions," Nature, Nature, vol. 461(7260), pages 53-59, September.
    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. Chengyi Tu & Paolo DOdorico & Samir Suweis, 2018. "Critical slowing down associated with critical transition and risk of collapse in cryptocurrency," Papers 1806.08386, arXiv.org, revised Nov 2019.
    2. Haoyu Wen & Massimo Pica Ciamarra & Siew Ann Cheong, 2018. "How one might miss early warning signals of critical transitions in time series data: A systematic study of two major currency pairs," PLOS ONE, Public Library of Science, vol. 13(3), pages 1-22, March.

    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. Jan Willem van den End, 2019. "Applying Complexity Theory to Interest Rates: Evidence of Critical Transitions in the Euro Area," Credit and Capital Markets, Credit and Capital Markets, vol. 52(1), pages 1-33.
    2. Haoyu Wen & Massimo Pica Ciamarra & Siew Ann Cheong, 2018. "How one might miss early warning signals of critical transitions in time series data: A systematic study of two major currency pairs," PLOS ONE, Public Library of Science, vol. 13(3), pages 1-22, March.
    3. Zejie Zhou & Boleslaw K Szymanski & Jianxi Gao, 2020. "Modeling competitive evolution of multiple languages," PLOS ONE, Public Library of Science, vol. 15(5), pages 1-16, May.
    4. Safarzyńska, Karolina & van den Bergh, Jeroen C.J.M., 2017. "Integrated crisis-energy policy: Macro-evolutionary modelling of technology, finance and energy interactions," Technological Forecasting and Social Change, Elsevier, vol. 114(C), pages 119-137.
    5. Ismail, Mohd Sabri & Noorani, Mohd Salmi Md & Ismail, Munira & Razak, Fatimah Abdul & Alias, Mohd Almie, 2022. "Early warning signals of financial crises using persistent homology," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 586(C).
    6. Pastor, Lubos & Stambaugh, Robert F., 2003. "Liquidity Risk and Expected Stock Returns," Journal of Political Economy, University of Chicago Press, vol. 111(3), pages 642-685, June.
    7. Chang, Sanders S. & Wang, F. Albert, 2015. "Adverse selection and the presence of informed trading," Journal of Empirical Finance, Elsevier, vol. 33(C), pages 19-33.
    8. Ellul, Andrew & Holden, Craig W. & Jain, Pankaj & Jennings, Robert, 2007. "Order dynamics: Recent evidence from the NYSE," Journal of Empirical Finance, Elsevier, vol. 14(5), pages 636-661, December.
    9. Victor Olkhov, 2023. "Market-Based Probability of Stock Returns," Papers 2302.07935, arXiv.org, revised Feb 2024.
    10. Stephen Morris & Hyun Song Shin, 2004. "Liquidity Black Holes," Review of Finance, Springer, vol. 8(1), pages 1-18.
    11. Rodrigo Aranda & Patricio Jaramillo, 2008. "Nonlinear Dynamic in the Chilean Stock Market: Evidence from Returns and Trading Volume," Working Papers Central Bank of Chile 463, Central Bank of Chile.
    12. Elaut, Gert & Frömmel, Michael & Lampaert, Kevin, 2018. "Intraday momentum in FX markets: Disentangling informed trading from liquidity provision," Journal of Financial Markets, Elsevier, vol. 37(C), pages 35-51.
    13. Dutta, Shantanu & Essaddam, Naceur & Kumar, Vinod & Saadi, Samir, 2017. "How does electronic trading affect efficiency of stock market and conditional volatility? Evidence from Toronto Stock Exchange," Research in International Business and Finance, Elsevier, vol. 39(PB), pages 867-877.
    14. Menkhoff, Lukas & Schmeling, Maik, 2010. "Trader see, trader do: How do (small) FX traders react to large counterparties' trades?," Journal of International Money and Finance, Elsevier, vol. 29(7), pages 1283-1302, November.
    15. Nicholas Apergis & Vasilios Plakandaras & Ioannis Pragidis, 2022. "Industry momentum and reversals in stock markets," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 27(3), pages 3093-3138, July.
    16. Caporale, Guglielmo Maria & Spagnolo, Fabio & Spagnolo, Nicola, 2016. "Macro news and stock returns in the Euro area: A VAR-GARCH-in-mean analysis," International Review of Financial Analysis, Elsevier, vol. 45(C), pages 180-188.
    17. Nicholas Taylor, 2011. "Time-varying price discovery in fragmented markets," Applied Financial Economics, Taylor & Francis Journals, vol. 21(10), pages 717-734.
    18. Polanski, Arnold & Stoja, Evarist, 2017. "Forecasting multidimensional tail risk at short and long horizons," International Journal of Forecasting, Elsevier, vol. 33(4), pages 958-969.
    19. James J Elser & Timothy J Elser & Stephen R Carpenter & William A Brock, 2014. "Regime Shift in Fertilizer Commodities Indicates More Turbulence Ahead for Food Security," PLOS ONE, Public Library of Science, vol. 9(5), pages 1-7, May.
    20. Guo, Li & Sang, Bo & Tu, Jun & Wang, Yu, 2024. "Cross-cryptocurrency return predictability," Journal of Economic Dynamics and Control, Elsevier, vol. 163(C).

    More about this item

    Keywords

    Critical slowing down; Complex dynamical system; Global financial crisis; Phase transition;
    All these keywords.

    JEL classification:

    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General
    • C4 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:mse:cesdoc:16045r. 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: Lucie Label (email available below). General contact details of provider: https://edirc.repec.org/data/cenp1fr.html .

    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.