Quantifying the Behavior of Stock Correlations Under Market Stress
By Tobias Preis, Dror Y. Kenett, H. E. Stanley, Dirk Helbing, E. Ben-Jacob. 2012
194 citations recorded by the source corpus.
Understanding correlations in complex systems is crucial in the face of turbulence, such as the ongoing financial crisis. However, in complex systems, such as financial systems, correlations are not constant but instead vary in time. Here we address the question of quantifying state-dependent correlations in stock markets. Reliable estimates of correlations are absolutely necessary to protect a portfolio. We analyze 72 years of daily closing prices of the 30 stocks forming the Dow Jones Industrial Average (DJIA). We find the striking result that the average correlation among these stocks scales linearly with market stress reflected by normalized DJIA index returns on various time scales. Consequently, the diversification effect which should protect a portfolio melts away in times of market losses, just when it would most urgently be needed. Our empirical analysis is consistent with the interesting possibility that one could anticipate diversification breakdowns, guiding the design of protected portfolios.
Subfields: Accounting, Economics and Econometrics, Law
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Economics and Econometrics
Citations in the corpus
- Improving stock market prediction via heterogeneous information fusion (2017, background): “In [42], the average correlations among stocks are found to scale linearly with the market stress reflected by normalized DJIA index returns on various time scales.”