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Detecting macroeconomic phases in the Dow Jones Industrial Average time series

Author

Listed:
  • Wong, Jian Cheng
  • Lian, Heng
  • Cheong, Siew Ann

Abstract

In this paper, we perform statistical segmentation and clustering analysis of the Dow Jones Industrial Average (DJI) time series between January 1997 and August 2008. Modeling the index movements and log-index movements as stationary Gaussian processes, we find a total of 116 and 119 statistically stationary segments respectively. These can then be grouped into between five and seven clusters, each representing a different macroeconomic phase. The macroeconomic phases are distinguished primarily by their volatilities. We find that the US economy, as measured by the DJI, spends most of its time in a low-volatility phase and a high-volatility phase. The former can be roughly associated with economic expansion, while the latter contains the economic contraction phase in the standard economic cycle. Both phases are interrupted by a moderate-volatility market correction phase, but extremely-high-volatility market crashes are found mostly within the high-volatility phase. From the temporal distribution of various phases, we see a high-volatility phase from mid-1998 to mid-2003, and another starting mid-2007 (the current global financial crisis). Transitions from the low-volatility phase to the high-volatility phase are preceded by a series of precursor shocks, whereas the transition from the high-volatility phase to the low-volatility phase is preceded by a series of inverted shocks. The time scale for both types of transitions is about a year. We also identify the July 1997 Asian Financial Crisis to be the trigger for the mid-1998 transition, and an unnamed May 2006 market event related to corrections in the Chinese markets to be the trigger for the mid-2007 transition.

Suggested Citation

  • Wong, Jian Cheng & Lian, Heng & Cheong, Siew Ann, 2009. "Detecting macroeconomic phases in the Dow Jones Industrial Average time series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 388(21), pages 4635-4645.
  • Handle: RePEc:eee:phsmap:v:388:y:2009:i:21:p:4635-4645
    DOI: 10.1016/j.physa.2009.07.029
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    References listed on IDEAS

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    Cited by:

    1. Isakov, A., 2013. "Interbank Market Structure and Accurate Estimation of an Aggregate Liquidity Shock," Journal of the New Economic Association, New Economic Association, vol. 19(3), pages 52-64.
    2. Cheong, Siew Ann & Fornia, Robert Paulo & Lee, Gladys Hui Ting & Kok, Jun Liang & Yim, Woei Shyr & Xu, Danny Yuan & Zhang, Yiting, 2011. "The Japanese economy in crises: A time series segmentation study," Economics Discussion Papers 2011-24, Kiel Institute for the World Economy (IfW).
    3. Sandoval, Leonidas & Franca, Italo De Paula, 2012. "Correlation of financial markets in times of crisis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(1), pages 187-208.
    4. repec:eee:apmaco:v:268:y:2015:i:c:p:399-412 is not listed on IDEAS
    5. Cheong, Siew Ann & Fornia, Robert Paulo & Lee, Gladys Hui Ting & Kok, Jun Liang & Yim, Woei Shyr & Xu, Danny Yuan & Zhang, Yiting, 2012. "The Japanese economy in crises: A time series segmentation study," Economics - The Open-Access, Open-Assessment E-Journal, Kiel Institute for the World Economy (IfW), vol. 6, pages 1-81.
    6. Xia, Jianan & Shang, Pengjian & Lu, Dan & Yin, Yi, 2016. "A comprehensive segmentation analysis of crude oil market based on time irreversibility," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 450(C), pages 104-114.
    7. Zhang, Yiting & Lee, Gladys Hui Ting & Wong, Jian Cheng & Kok, Jun Liang & Prusty, Manamohan & Cheong, Siew Ann, 2011. "Will the US economy recover in 2010? A minimal spanning tree study," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(11), pages 2020-2050.

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    Keywords

    DJI; Macroeconomic cycle; Segmentation; Clustering;

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