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The US stock market leads the Federal funds rate and Treasury bond yields

Author

Listed:
  • Kun Guo

    (CAS)

  • Wei-Xing Zhou

    (ECUST)

  • Si-Wei Cheng

    (CAS)

  • Didier Sornette

    (ETH Zurich)

Abstract

Using a recently introduced method to quantify the time varying lead-lag dependencies between pairs of economic time series (the thermal optimal path method), we test two fundamental tenets of the theory of fixed income: (i) the stock market variations and the yield changes should be anti-correlated; (ii) the change in central bank rates, as a proxy of the monetary policy of the central bank, should be a predictor of the future stock market direction. Using both monthly and weekly data, we found very similar lead-lag dependence between the S&P500 stock market index and the yields of bonds inside two groups: bond yields of short-term maturities (Federal funds rate (FFR), 3M, 6M, 1Y, 2Y, and 3Y) and bond yields of long-term maturities (5Y, 7Y, 10Y, and 20Y). In all cases, we observe the opposite of (i) and (ii). First, the stock market and yields move in the same direction. Second, the stock market leads the yields, including and especially the FFR. Moreover, we find that the short-term yields in the first group lead the long-term yields in the second group before the financial crisis that started mid-2007 and the inverse relationship holds afterwards. These results suggest that the Federal Reserve is increasingly mindful of the stock market behavior, seen at key to the recovery and health of the economy. Long-term investors seem also to have been more reactive and mindful of the signals provided by the financial stock markets than the Federal Reserve itself after the start of the financial crisis. The lead of the S&P500 stock market index over the bond yields of all maturities is confirmed by the traditional lagged cross-correlation analysis.

Suggested Citation

  • Kun Guo & Wei-Xing Zhou & Si-Wei Cheng & Didier Sornette, 2011. "The US stock market leads the Federal funds rate and Treasury bond yields," Papers 1102.2138, arXiv.org.
  • Handle: RePEc:arx:papers:1102.2138
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    Cited by:

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    5. Yao, Can-Zhong & Lin, Ji-Nan & Lin, Qing-Wen & Zheng, Xu-Zhou & Liu, Xiao-Feng, 2016. "A study of causality structure and dynamics in industrial electricity consumption based on Granger network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 462(C), pages 297-320.
    6. Yang, Yan-Hong & Shao, Ying-Hui, 2020. "Time-dependent lead-lag relationships between the VIX and VIX futures markets," The North American Journal of Economics and Finance, Elsevier, vol. 53(C).
    7. Xu, Hai-Chuan & Zhou, Wei-Xing & Sornette, Didier, 2017. "Time-dependent lead-lag relationship between the onshore and offshore Renminbi exchange rates," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 49(C), pages 173-183.
    8. Hao Meng & Hai-Chuan Xu & Wei-Xing Zhou & Didier Sornette, 2017. "Symmetric thermal optimal path and time-dependent lead-lag relationship: novel statistical tests and application to UK and US real-estate and monetary policies," Quantitative Finance, Taylor & Francis Journals, vol. 17(6), pages 959-977, June.
    9. Lai, Lin & Guo, Kun, 2017. "The performance of one belt and one road exchange rate: Based on improved singular spectrum analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 483(C), pages 299-308.
    10. Yao, Can-Zhong & Li, Hong-Yu, 2020. "Time-varying lead–lag structure between investor sentiment and stock market," The North American Journal of Economics and Finance, Elsevier, vol. 52(C).
    11. Zhang, Yongjie & Zhang, Zuochao & Liu, Lanbiao & Shen, Dehua, 2017. "The interaction of financial news between mass media and new media: Evidence from news on Chinese stock market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 486(C), pages 535-541.
    12. Jiang, Tao & Bao, Si & Li, Long, 2019. "The linear and nonlinear lead–lag relationship among three SSE 50 Index markets: The index futures, 50ETF spot and options markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 525(C), pages 878-893.
    13. V. I. Yukalov & E. P. Yukalova & D. Sornette, 2015. "Dynamical system theory of periodically collapsing bubbles," Papers 1507.05311, arXiv.org.
    14. Leiss, Matthias & Nax, Heinrich H. & Sornette, Didier, 2015. "Super-exponential growth expectations and the global financial crisis," LSE Research Online Documents on Economics 65434, London School of Economics and Political Science, LSE Library.
    15. Jia, Rui-Lin & Wang, Dong-Hua & Tu, Jing-Qing & Li, Sai-Ping, 2016. "Correlation between agricultural markets in dynamic perspective—Evidence from China and the US futures markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 464(C), pages 83-92.
    16. Honghai Yu & Libing Fang & Boyang Sun, 2018. "The role of global economic policy uncertainty in long-run volatilities and correlations of U.S. industry-level stock returns and crude oil," PLOS ONE, Public Library of Science, vol. 13(2), pages 1-17, February.
    17. Susana Borrego-Domínguez & Fernando Isla-Castillo & Mercedes Rodríguez-Fernández, 2022. "Determinants of Tourism Demand in Spain: A European Perspective from 2000–2020," Economies, MDPI, vol. 10(11), pages 1-21, November.
    18. Guo, Kun & Sun, Yi & Qian, Xin, 2017. "Can investor sentiment be used to predict the stock price? Dynamic analysis based on China stock market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 469(C), pages 390-396.
    19. Wang, Xuan & Guo, Kun & Lu, Xiaolin, 2016. "The long-run dynamic relationship between exchange rate and its attention index: Based on DCCA and TOP method," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 453(C), pages 108-115.
    20. Damian Smug & Peter Ashwin & Didier Sornette, 2018. "Predicting financial market crashes using ghost singularities," PLOS ONE, Public Library of Science, vol. 13(3), pages 1-20, March.
    21. 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.
    22. Gong, Chen-Chen & Ji, Shen-Dan & Su, Li-Ling & Li, Sai-Ping & Ren, Fei, 2016. "The lead–lag relationship between stock index and stock index futures: A thermal optimal path method," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 444(C), pages 63-72.

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    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • E44 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Financial Markets and the Macroeconomy
    • E47 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Forecasting and Simulation: Models and Applications
    • E58 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - Central Banks and Their Policies
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

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