Detecting macroeconomic phases in the Dow Jones Industrial Average time series

• المؤلفون الرئيسيون: Wong, Jian Cheng, Lian, Heng, Cheong, Siew Ann
• مؤلفون آخرون: School of Physical and Mathematical Sciences
• التنسيق: مقال
• اللغة: English
• منشور في: 2009
• الموضوعات: DRNTU::Science::Mathematics::Statistics; DRNTU::Business::Finance::Mathematical finance
• الوصول للمادة أونلاين: https://hdl.handle.net/10356/91824; http://hdl.handle.net/10220/6107

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).