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Detecting macroeconomic phases in the Dow Jones Industrial Average time series.

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Detecting macroeconomic phases in the Dow Jones Industrial Average time series.

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Title: Detecting macroeconomic phases in the Dow Jones Industrial Average time series.
Author: Wong, Jian Cheng.; Lian, Heng.; Cheong, Siew Ann.
Copyright year: 2009
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).
Subject: DRNTU::Science::Mathematics::Statistics.DRNTU::Business::Finance::Mathematical finance.
Type: Journal Article
Series/ Journal Title: Physica A
School: School of Physical and Mathematical Sciences
Rights: Physica A © copyright 2009 Elsevier. The journal's website is located at http://www.elsevier.com/locate/physa.
Version: Published version

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