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Causal Slaving of the U.S. Treasury Bond Yield Antibubble by the Stock Market Antibubble of August 2000

Author

Listed:
  • W. -X. Zhou

    (UCLA)

  • D. Sornette

    (UCLA and CNRS-Univ. Nice)

Abstract

Using the descriptive method of log-periodic power laws (LPPL) based on a theory of behavioral herding, we use a battery of parametric and non-parametric tests to demonstrate the existence of an antibubble in the yields with maturities larger than 1 year since October 2000. The concept of ``antibubble'' describes the existence of a specific LPPL pattern that is thought to reflect collective herding effects. From the dependence of the parameters of the LPPL formula as a function of yield maturities and using lagged cross-correlation calculations between the S&P 500 and bond yields, we find strong evidence for the following causality: Stock Market $\to$ Fed Reserve (Federal funds rate) $\to$ short-term yields $\to$ long-term yields (as well as a direct and instantaneous influence of the stock market on the long-term yields). Our interpretation is that the FRB is ``causally slaved'' to the stock market (at least for the studied period), because the later is (taken as) a proxy for the present and future health of the economy.

Suggested Citation

  • W. -X. Zhou & D. Sornette, 2003. "Causal Slaving of the U.S. Treasury Bond Yield Antibubble by the Stock Market Antibubble of August 2000," Papers cond-mat/0312658, arXiv.org.
    • Handle: RePEc:arx:papers:cond-mat/0312658
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    Cited by:

    1. Didier Sornette & Wei-Xing Zhou, 2005. "Non-parametric determination of real-time lag structure between two time series: the 'optimal thermal causal path' method," Quantitative Finance, Taylor & Francis Journals, vol. 5(6), pages 577-591.
    2. Kun Guo & Wei-Xing Zhou & Si-Wei Cheng & Didier Sornette, 2011. "The US Stock Market Leads the Federal Funds Rate and Treasury Bond Yields," PLOS ONE, Public Library of Science, vol. 6(8), pages 1-9, August.
    3. von der Becke Susanne & Sornette Didier, 2019. "An Asset-Based Framework of Credit Creation (applied to the Global Financial Crisis)," Accounting, Economics, and Law: A Convivium, De Gruyter, vol. 9(2), pages 1-21, July.
    4. Leiss, Matthias & Nax, Heinrich H. & Sornette, Didier, 2015. "Super-exponential growth expectations and the global financial crisis," Journal of Economic Dynamics and Control, Elsevier, vol. 55(C), pages 1-13.
    5. Zhou, Wei-Xing & Sornette, Didier, 2006. "Non-parametric determination of real-time lag structure between two time series: The "optimal thermal causal path" method with applications to economic data," Journal of Macroeconomics, Elsevier, vol. 28(1), pages 195-224, March.
    6. Zhou, Wei-Xing & Sornette, Didier, 2005. "Testing the stability of the 2000 US stock market “antibubble”," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 348(C), pages 428-452.
    7. Zhou, Wei & Huang, Yang & Chen, Jin, 2018. "The bubble and anti-bubble risk resistance analysis on the metal futures in China," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 503(C), pages 947-957.
    8. Marcin Wk{a}torek & Stanis{l}aw Dro.zd.z & Jaros{l}aw Kwapie'n & Ludovico Minati & Pawe{l} O'swik{e}cimka & Marek Stanuszek, 2020. "Multiscale characteristics of the emerging global cryptocurrency market," Papers 2010.15403, arXiv.org, revised Mar 2021.
    9. Zhou, Wei-Xing & Sornette, Didier, 2006. "Fundamental factors versus herding in the 2000–2005 US stock market and prediction," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 360(2), pages 459-482.
    10. Zhou, Wei-Xing & Sornette, Didier, 2009. "A case study of speculative financial bubbles in the South African stock market 2003–2006," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 388(6), pages 869-880.
    11. D. Sornette & R. Woodard, "undated". "Financial Bubbles, Real Estate bubbles, Derivative Bubbles, and the Financial and Economic Crisis," Working Papers CCSS-09-003, ETH Zurich, Chair of Systems Design.
    12. Didier Sornette & Ryan Woodard, 2009. "Financial Bubbles, Real Estate bubbles, Derivative Bubbles, and the Financial and Economic Crisis," Papers 0905.0220, arXiv.org.
    13. Pawel Dlotko & Simon Rudkin, 2019. "The Topology of Time Series: Improving Recession Forecasting from Yield Spreads," Working Papers 2019-02, Swansea University, School of Management.
    14. Zheng, Zhiyong & Lu, Yunfan & Zhang, Junhuan, 2022. "Multiscale complexity fluctuation behaviours of stochastic interacting cryptocurrency price model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 593(C).
    15. John Fry & McMillan David, 2015. "Stochastic modelling for financial bubbles and policy," Cogent Economics & Finance, Taylor & Francis Journals, vol. 3(1), pages 1002152-100, December.
    16. Fry, John & Cheah, Eng-Tuck, 2016. "Negative bubbles and shocks in cryptocurrency markets," International Review of Financial Analysis, Elsevier, vol. 47(C), pages 343-352.
    17. Didier Sornette & Peter Cauwels, 2014. "1980–2008: The Illusion of the Perpetual Money Machine and What It Bodes for the Future," Risks, MDPI, vol. 2(2), pages 1-29, April.
    18. Duan, Wen-Qi & Stanley, H. Eugene, 2011. "Cross-correlation and the predictability of financial return series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(2), pages 290-296.
    19. Miśkiewicz, Janusz, 2012. "Economy with the time delay of information flow—The stock market case," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(4), pages 1388-1394.
    20. V. I. Yukalov & E. P. Yukalova & D. Sornette, 2015. "Dynamical system theory of periodically collapsing bubbles," Papers 1507.05311, arXiv.org.

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