Macroeconomic Phase Transitions Detected from the Dow Jones Industrial Average Time Series
Wong Jian Cheng, Lian Heng, and Cheong Siew Ann

TL;DR
This study analyzes the Dow Jones Industrial Average from 1997 to 2008, identifying macroeconomic phases through statistical segmentation, revealing patterns of volatility associated with economic cycles and specific financial crises.
Contribution
It introduces a method to detect macroeconomic phases from stock index data using segmentation and clustering, linking phases to volatility and economic events.
Findings
Most time spent in low- and high-volatility phases
Market crashes occur mainly during high-volatility phases
Transitions are preceded by identifiable shocks
Abstract
In this paper, we perform statistical segmentation and clustering analysis of the Dow Jones Industrial Average 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 to seven clusters, each representing a different macroeconomic phase. The macroeconomic phases are distinguished primarily by their volatilities. We find 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, but extremely-high-volatility market…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsComplex Systems and Time Series Analysis · Financial Risk and Volatility Modeling · Market Dynamics and Volatility
