Advanced Search
MyIDEAS: Login

Non-parametric determination of real-time lag structure between two time series: the 'optimal thermal causal path' method

Contents:

Author Info

  • Didier Sornette
  • Wei-Xing Zhou
Registered author(s):

    Abstract

    We introduce a novel non-parametric methodology to test for the dynamical time evolution of the lag-lead structure between two arbitrary time series. The method consists of constructing a distance matrix based on the matching of all sample data pairs between the two time series. Then, the lag-lead structure is searched for as the optimal path in the distance matrix landscape that minimizes the total mismatch between the two time series, and that obeys a one-to-one causal matching condition. To make the solution robust to the presence of a large amount of noise that may lead to spurious structures in the distance matrix landscape, we generalize this optimal search by introducing a fuzzy search by sampling over all possible paths, each path being weighted according to a multinomial logit or equivalently Boltzmann factor proportional to the exponential of the global mismatch of this path. We present the efficient transfer matrix method that solves the problem and test it on simple synthetic examples to demonstrate its properties and usefulness compared with the standard running-time cross-correlation method. We then apply our 'optimal thermal causal path' method to the question of the lag-dependence between the US stock market and the treasury bond yields and confirm our earlier results on an arrow of the stock markets preceding the Federal Reserve Funds' adjustments, as well as the yield rates at short maturities in the period 2000-2003. Our application of this technique to inflation, inflation change, GDP growth rate and unemployment rate unearths non-trivial lag relationships: the GDP changes lead inflation especially since the 1980s, inflation changes leads GDP only in the 1980 decade, and inflation leads unemployment rates since the 1970s. In addition, our approach seems to detect multiple competing lag structures in which one can have inflation leading GDP with a certain lag time and GDP feeding back/leading inflation with another lag time.

    Download Info

    If you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
    File URL: http://www.tandfonline.com/doi/abs/10.1080/14697680500383763
    Download Restriction: Access to full text is restricted to subscribers.

    As the access to this document is restricted, you may want to look for a different version under "Related research" (further below) or search for a different version of it.

    Bibliographic Info

    Article provided by Taylor & Francis Journals in its journal Quantitative Finance.

    Volume (Year): 5 (2005)
    Issue (Month): 6 ()
    Pages: 577-591

    as in new window
    Handle: RePEc:taf:quantf:v:5:y:2005:i:6:p:577-591

    Contact details of provider:
    Web page: http://www.tandfonline.com/RQUF20

    Order Information:
    Web: http://www.tandfonline.com/pricing/journal/RQUF20

    Related research

    Keywords: Econophysics; Causality; Correlation; Thermal average; Time series;

    References

    References listed on IDEAS
    Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
    as in new window
    1. Strozzi, Fernanda & Zaldı́var, José-Manuel & Zbilut, Joseph P, 2002. "Application of nonlinear time series analysis techniques to high-frequency currency exchange data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 312(3), pages 520-538.
    2. Hyeon-Seung Huh & Hyun-Hoon Lee, 2002. "Asymmetric output cost of lowering inflation: empirical evidence for Canada," Canadian Journal of Economics, Canadian Economics Association, vol. 35(2), pages 218-238, May.
    3. D. Sornette & A. Johansen, 2001. "Significance of log-periodic precursors to financial crashes," Quantitative Finance, Taylor & Francis Journals, vol. 1(4), pages 452-471.
    4. Fischer, Stanley, 1974. "Money and the Production Function," Economic Inquiry, Western Economic Association International, vol. 12(4), pages 517-33, December.
    5. Robert Mundell, 1963. "Inflation and Real Interest," Journal of Political Economy, University of Chicago Press, vol. 71, pages 280.
    6. Timothy Cogley & Thomas J. Sargent, 2005. "The conquest of US inflation: Learning and robustness to model uncertainty," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 8(2), pages 528-563, April.
    7. Apergis, Nicholas, 2004. "Inflation, output growth, volatility and causality: evidence from panel data and the G7 countries," Economics Letters, Elsevier, vol. 83(2), pages 185-191, May.
    8. Fountas, Stilianos & Karanasos, Menelaos & Kim, Jinki, 2002. "Inflation and output growth uncertainty and their relationship with inflation and output growth," Economics Letters, Elsevier, vol. 75(3), pages 293-301, May.
    9. Ashley, R & Granger, C W J & Schmalensee, R, 1980. "Advertising and Aggregate Consumption: An Analysis of Causality," Econometrica, Econometric Society, vol. 48(5), pages 1149-67, July.
    10. D. Sornette & A. Johansen, 2001. "Significance of log-periodic precursors to financial crashes," Papers cond-mat/0106520, arXiv.org.
    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 in new window

    Cited by:
    1. 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.

    Lists

    This item is not listed on Wikipedia, on a reading list or among the top items on IDEAS.

    Statistics

    Access and download statistics

    Corrections

    When requesting a correction, please mention this item's handle: RePEc:taf:quantf:v:5:y:2005:i:6:p:577-591. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Michael McNulty).

    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 references are entirely missing, you can add them using this form.

    If the full references list an item that is present in RePEc, but the system did not link 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 profile, as there may be some citations waiting for confirmation.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.