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Title: Detecting macroeconomic phases in the Dow Jones Industrial Average time series
Authors: Wong, Jian Cheng
Lian, Heng
Cheong, Siew Ann
Keywords: DRNTU::Science::Mathematics::Statistics
DRNTU::Business::Finance::Mathematical finance
Issue Date: 2009
Source: Wong, J. C., Lian, H., & Cheong, S. A. (2009). Detecting macroeconomic phases in the Dow Jones Industrial Average time series. Physica A, 388, 4635-4645.
Series/Report no.: Physica A
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).
ISSN: 0378-4371
DOI: 10.1016/j.physa.2009.07.029
Rights: Physica A © copyright 2009 Elsevier. The journal's website is located at
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SPMS Journal Articles

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