IDEAS home Printed from https://ideas.repec.org/p/arx/papers/1102.2138.html
   My bibliography  Save this paper

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
    as

    Download full text from publisher

    File URL: http://arxiv.org/pdf/1102.2138
    File Function: Latest version
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Ben S. Bernanke & Kenneth N. Kuttner, 2005. "What Explains the Stock Market's Reaction to Federal Reserve Policy?," Journal of Finance, American Finance Association, vol. 60(3), pages 1221-1257, June.
    2. Zhou, Wei-Xing & Sornette, Didier, 2004. "Causal slaving of the US treasury bond yield antibubble by the stock market antibubble of August 2000," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 337(3), pages 586-608.
    3. Dooley, Michael & Hutchison, Michael, 2009. "Transmission of the U.S. subprime crisis to emerging markets: Evidence on the decoupling-recoupling hypothesis," Journal of International Money and Finance, Elsevier, vol. 28(8), pages 1331-1349, December.
    4. Forni, Mario & Hallin, Marc & Lippi, Marco & Reichlin, Lucrezia, 2003. "Do financial variables help forecasting inflation and real activity in the euro area?," Journal of Monetary Economics, Elsevier, vol. 50(6), pages 1243-1255, September.
    5. Carmen M. Reinhart & Kenneth S. Rogoff, 2009. "The Aftermath of Financial Crises," American Economic Review, American Economic Association, vol. 99(2), pages 466-472, May.
    6. 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.
    7. Fatum, Rasmus & Hutchison, Michael, 1999. "Is Intervention a Signal of Future Monetary Policy? Evidece from the Federal Funds Futures Market," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 31(1), pages 54-69, February.
    8. 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.
    9. Arturo Estrella & Frederic S. Mishkin, 1998. "Predicting U.S. Recessions: Financial Variables As Leading Indicators," The Review of Economics and Statistics, MIT Press, vol. 80(1), pages 45-61, February.
    10. Brissimis, Sophocles N. & Magginas, Nicholas S., 2006. "Forward-looking information in VAR models and the price puzzle," Journal of Monetary Economics, Elsevier, vol. 53(6), pages 1225-1234, September.
    11. Rigobon, Roberto & Sack, Brian, 2004. "The impact of monetary policy on asset prices," Journal of Monetary Economics, Elsevier, vol. 51(8), pages 1553-1575, November.
    12. Bjørnland, Hilde C. & Leitemo, Kai, 2009. "Identifying the interdependence between US monetary policy and the stock market," Journal of Monetary Economics, Elsevier, vol. 56(2), pages 275-282, March.
    13. Arestis, Philip & Demetriades, Panicos O & Luintel, Kul B, 2001. "Financial Development and Economic Growth: The Role of Stock Markets," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 33(1), pages 16-41, February.
    14. Kwiatkowski, Denis & Phillips, Peter C. B. & Schmidt, Peter & Shin, Yongcheol, 1992. "Testing the null hypothesis of stationarity against the alternative of a unit root : How sure are we that economic time series have a unit root?," Journal of Econometrics, Elsevier, vol. 54(1-3), pages 159-178.
    15. Dickey, David A & Fuller, Wayne A, 1981. "Likelihood Ratio Statistics for Autoregressive Time Series with a Unit Root," Econometrica, Econometric Society, vol. 49(4), pages 1057-1072, June.
    16. Alfaro, Laura & Chanda, Areendam & Kalemli-Ozcan, Sebnem & Sayek, Selin, 2004. "FDI and economic growth: the role of local financial markets," Journal of International Economics, Elsevier, vol. 64(1), pages 89-112, October.
    17. Gilchrist, Simon & Yankov, Vladimir & Zakrajsek, Egon, 2009. "Credit market shocks and economic fluctuations: Evidence from corporate bond and stock markets," Journal of Monetary Economics, Elsevier, vol. 56(4), pages 471-493, May.
    18. Zhou, Wei-Xing & Sornette, Didier, 2007. "Lead-lag cross-sectional structure and detection of correlated–anticorrelated regime shifts: Application to the volatilities of inflation and economic growth rates," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 380(C), pages 287-296.
    19. Tobin, James, 1969. "A General Equilibrium Approach to Monetary Theory," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 1(1), pages 15-29, February.
    20. Fama, Eugene F, 1981. "Stock Returns, Real Activity, Inflation, and Money," American Economic Review, American Economic Association, vol. 71(4), pages 545-565, September.
    21. Schwert, G William, 1990. "Stock Returns and Real Activity: A Century of Evidence," Journal of Finance, American Finance Association, vol. 45(4), pages 1237-1257, September.
    22. Baeriswyl, Romain & Cornand, Camille, 2010. "The signaling role of policy actions," Journal of Monetary Economics, Elsevier, vol. 57(6), pages 682-695, September.
    23. Richard A. Werner, 2005. "New Paradigm in Macroeconomics," Palgrave Macmillan Books, Palgrave Macmillan, number 978-0-230-50607-7, October.
    24. Xia Pan, 2007. "The Linear Dependence And Feedback Spectra Between Stock Market And Economy," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 10(03), pages 437-447.
    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. Shao, Ying-Hui & Yang, Yan-Hong & Shao, Hao-Lin & Stanley, H. Eugene, 2019. "Time-varying lead–lag structure between the crude oil spot and futures markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 523(C), pages 723-733.
    2. 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.
    3. 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.
    4. Shao, Ying-Hui & Yang, Yan-Hong & Zhou, Wei-Xing, 2022. "How does economic policy uncertainty comove with stock markets: New evidence from symmetric thermal optimal path method," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 604(C).
    5. 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.
    6. 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.
    7. 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.
    8. 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.
    9. 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.
    10. Angeliki Papana & Catherine Kyrtsou & Dimitris Kugiumtzis & Cees Diks, 2023. "Identification of causal relationships in non-stationary time series with an information measure: Evidence for simulated and financial data," Empirical Economics, Springer, vol. 64(3), pages 1399-1420, March.
    11. 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.
    12. 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).
    13. 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.
    14. 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.
    15. 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.
    16. 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.
    17. 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).
    18. 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.
    19. 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.
    20. 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.
    21. 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.
    22. V. I. Yukalov & E. P. Yukalova & D. Sornette, 2015. "Dynamical system theory of periodically collapsing bubbles," Papers 1507.05311, arXiv.org.

    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. Pablo Ottonello & Wenting Song, 2022. "Financial Intermediaries and the Macroeconomy: Evidence from a High-Frequency Identification," Staff Working Papers 22-24, Bank of Canada.
    2. Fady Barsoum, 2013. "The Effects of Monetary Policy Shocks on a Panel of Stock Market Volatilities: A Factor-Augmented Bayesian VAR Approach," Working Paper Series of the Department of Economics, University of Konstanz 2013-15, Department of Economics, University of Konstanz.
    3. Aliyu, Shehu Usman Rano, 2020. "What have we learnt from modelling stock returns in Nigeria: Higgledy-piggledy?," MPRA Paper 110382, University Library of Munich, Germany, revised 06 Jun 2021.
    4. Lyócsa, Štefan & Výrost, Tomáš & Plíhal, Tomáš, 2021. "A tale of tails : New evidence on the growth-return nexus," Finance Research Letters, Elsevier, vol. 38(C).
    5. Rangan GUPTA & Roula INGLESI-LOTZ, 2012. "Macro Shocks and Real US Stock Prices with Special Focus on the “Great Recession”," Applied Econometrics and International Development, Euro-American Association of Economic Development, vol. 12(2).
    6. Bouakez, Hafedh & Essid, Badye & Normandin, Michel, 2013. "Stock returns and monetary policy: Are there any ties?," Journal of Macroeconomics, Elsevier, vol. 36(C), pages 33-50.
    7. Dohyun CHUN & Hoon CHO & Doojin RYU, 2018. "Macroeconomic Structural Changes in a Leading Emerging Market: The Effects of the Asian Financial Crisis," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(2), pages 22-42, December.
    8. van Holle, Frederiek, 2017. "Essays in empirical finance and monetary policy," Other publications TiSEM 30d11a4b-7bc9-4c81-ad24-5, Tilburg University, School of Economics and Management.
    9. Stijn Claessens & M Ayhan Kose, 2018. "Frontiers of macrofinancial linkages," BIS Papers, Bank for International Settlements, number 95.
    10. Balcilar, Mehmet & Gupta, Rangan & Wohar, Mark E., 2017. "Common cycles and common trends in the stock and oil markets: Evidence from more than 150years of data," Energy Economics, Elsevier, vol. 61(C), pages 72-86.
    11. Marfatia, Hardik A., 2014. "Impact of uncertainty on high frequency response of the U.S. stock markets to the Fed's policy surprises," The Quarterly Review of Economics and Finance, Elsevier, vol. 54(3), pages 382-392.
    12. Ekaterini Panopoulou, 2006. "The predictive content of financial variables: Evidence from the euro area," The Institute for International Integration Studies Discussion Paper Series iiisdp178, IIIS.
    13. Aliyu, Shehu Usman Rano, 2011. "Reactions of stock market to monetary policy shocks during the global financial crisis: the Nigerian case," MPRA Paper 35581, University Library of Munich, Germany, revised 28 Dec 2011.
    14. Marinescu, Ion-Iulian & Horobet, Alexandra & Lupu, Radu, 2018. "Dichotomous stock market reaction to episodes of rules and discretion in the US monetary policy," Economic Modelling, Elsevier, vol. 70(C), pages 56-66.
    15. Bahram Adrangi & Arjun Chatrath & Joseph Macri & Kambiz Raffiee, 2016. "The US Monetary Base and Major World Equity Markets: An Empirical Investigation," Review of Economics & Finance, Better Advances Press, Canada, vol. 6, pages 49-64, August.
    16. Johannes A. Skjeltorp & Bernt Arne Ødegaard, 2009. "The information content of market liquidity: An empirical analysis of liquidity at the Oslo Stock Exchange?," Working Paper 2009/26, Norges Bank.
    17. Eksi, Ozan & Tas, Bedri Kamil Onur, 2017. "Unconventional monetary policy and the stock market’s reaction to Federal Reserve policy actions," The North American Journal of Economics and Finance, Elsevier, vol. 40(C), pages 136-147.
    18. Kenneth N. Kuttner & Adam S. Posen, 2010. "Do Markets Care Who Chairs the Central Bank?," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 42(2‐3), pages 347-371, March.
    19. repec:hal:spmain:info:hdl:2441/74362fq3f99s299n07e84dlcib is not listed on IDEAS
    20. Hondroyiannis, George & Lolos, Sarantis & Papapetrou, Evangelia, 2005. "Financial markets and economic growth in Greece, 1986-1999," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 15(2), pages 173-188, April.
    21. Domian, Dale L. & Louton, David A., 1997. "A threshold autoregressive analysis of stock returns and real economic activity," International Review of Economics & Finance, Elsevier, vol. 6(2), pages 167-179.

    More about this item

    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

    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:arx:papers:1102.2138. 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: arXiv administrators (email available below). General contact details of provider: http://arxiv.org/ .

    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.