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Real Time Monitoring of Asset Markets: Bubbles and Crises

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Abstract

While each financial crisis has its own characteristics, there is now widespread recognition that crises arising from sources such as financial speculation and excessive credit creation do inflict harm on the real economy. Detecting speculative market conditions and ballooning credit risk in real time is therefore of prime importance in the complex exercises of market surveillance, risk management, and policy action. This chapter provides an R implementation of the popular real-time monitoring strategy proposed by Phillips, Shi and Yu in the International Economic Review (2015), along with a new bootstrap procedure designed to mitigate the potential impact of heteroskedasticity and to effect family-wise size control in recursive testing algorithms. This methodology has been shown effective for bubble and crisis detection and is now widely used by academic researchers, central bank economists, and fiscal regulators. We illustrate the effectiveness of this procedure with applications to the S&P financial market and the European sovereign debt sector using the psymonitor R package developed in conjunction with this chapter.

Suggested Citation

  • Peter C.B. Phillips & Shuping Shi, 2018. "Real Time Monitoring of Asset Markets: Bubbles and Crises," Cowles Foundation Discussion Papers 2152, Cowles Foundation for Research in Economics, Yale University.
    • Handle: RePEc:cwl:cwldpp:2152
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    References listed on IDEAS

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    1. Adämmer, Philipp & Bohl, Martin T., 2015. "Speculative bubbles in agricultural prices," The Quarterly Review of Economics and Finance, Elsevier, vol. 55(C), pages 67-76.
    2. Xiaoli L. Etienne & Scott H. Irwin & Philip Garcia, 2015. "Price Explosiveness, Speculation, and Grain Futures Prices," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 97(1), pages 65-87.
    3. Diba, Behzad T & Grossman, Herschel I, 1988. "Explosive Rational Bubbles in Stock Prices?," American Economic Review, American Economic Association, vol. 78(3), pages 520-530, June.
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    6. Caspi, Itamar & Katzke, Nico & Gupta, Rangan, 2018. "Date stamping historical periods of oil price explosivity: 1876–2014," Energy Economics, Elsevier, vol. 70(C), pages 582-587.
    7. Viral V Acharya & Tim Eisert & Christian Eufinger & Christian Hirsch, 2018. "Real Effects of the Sovereign Debt Crisis in Europe: Evidence from Syndicated Loans," The Review of Financial Studies, Society for Financial Studies, vol. 31(8), pages 2855-2896.
    8. Bohl, Martin T., 2003. "Periodically collapsing bubbles in the US stock market?," International Review of Economics & Finance, Elsevier, vol. 12(3), pages 385-397.
    9. Yongheng Deng & Eric Girardin & Roselyne Joyeux & Shuping Shi, 2017. "Did bubbles migrate from the stock to the housing market in China between 2005 and 2010?," Pacific Economic Review, Wiley Blackwell, vol. 22(3), pages 276-292, August.
    10. Caspi, Itamar, 2013. "Rtadf: Testing for Bubbles with EViews," MPRA Paper 58791, University Library of Munich, Germany, revised 06 Sep 2014.
    11. Juan S. Amador‐Torres & Jose E. Gomez‐Gonzalez & Sebastian Sanin‐Restrepo, 2018. "Determinants of housing bubbles' duration in OECD countries," International Finance, Wiley Blackwell, vol. 21(2), pages 140-157, June.
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    Cited by:

    1. Scott M. R. Mahadeo & Reinhold Heinlein & Gabriella Deborah Legrenzi, 2019. "Contagion Testing in Embryonic Markets under Alternative Stressful US Market Scenarios," CESifo Working Paper Series 8029, CESifo.
    2. Jorge M. Uribe & Natalia Restrepo & Montserrat Guillen, 2021. ""Price Bubbles in Lithium Markets around the World"," IREA Working Papers 202110, University of Barcelona, Research Institute of Applied Economics, revised Apr 2021.
    3. Enoksen, F.A. & Landsnes, Ch.J. & Lučivjanská, K. & Molnár, P., 2020. "Understanding risk of bubbles in cryptocurrencies," Journal of Economic Behavior & Organization, Elsevier, vol. 176(C), pages 129-144.
    4. Xun Zhang & Fengbin Lu & Rui Tao & Shouyang Wang, 2021. "The time-varying causal relationship between the Bitcoin market and internet attention," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 7(1), pages 1-19, December.
    5. Podhorsky, Andrea, 2024. "Bursting the bitcoin bubble: Do market prices reflect fundamental bitcoin value?," International Review of Financial Analysis, Elsevier, vol. 93(C).

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    More about this item

    Keywords

    Bubbles; Crises; Real-time detection; Recursive evolving test;
    All these keywords.

    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General

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