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Quantifying the behavior of stock correlations under market stress.

Preis T, Kenett DY, Stanley HE, Helbing D, Ben-Jacob E - Sci Rep (2012)

Bottom Line: Reliable estimates of correlations are absolutely necessary to protect a portfolio.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.

View Article: PubMed Central - PubMed

Affiliation: Warwick Business School, University of Warwick, Coventry, United Kingdom. mail@tobiaspreis.de

ABSTRACT
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.

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Related in: MedlinePlus

Visualization of the analysis method.(A) For a time interval of Δt trading days, we calculate for the index the price return log(pDJIA(t + Δt))/log(pDJIA(t)) in this interval. (B) We determine the Pearson correlation coefficients of all pairs of all 30 DJIA components depicted in a matrix of correlation coefficients. Ticker symbols are used to abbreviate company names in this example. We calculate the mean correlation coefficient by averaging over all non-diagonal elements of this matrix.
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f2: Visualization of the analysis method.(A) For a time interval of Δt trading days, we calculate for the index the price return log(pDJIA(t + Δt))/log(pDJIA(t)) in this interval. (B) We determine the Pearson correlation coefficients of all pairs of all 30 DJIA components depicted in a matrix of correlation coefficients. Ticker symbols are used to abbreviate company names in this example. We calculate the mean correlation coefficient by averaging over all non-diagonal elements of this matrix.

Mentions: To quantify state-dependent correlations, we calculate the mean value of Pearson product-moment correlation coefficients49 among all DJIA components in a time interval comprising Δt trading days each (Fig. 2). In each time interval comprising Δt trading day, we determine correlation coefficients for all pairs of N ≡ 30 stocks. From these correlation coefficients, we calculate their mean value for each time interval separately.


Quantifying the behavior of stock correlations under market stress.

Preis T, Kenett DY, Stanley HE, Helbing D, Ben-Jacob E - Sci Rep (2012)

Visualization of the analysis method.(A) For a time interval of Δt trading days, we calculate for the index the price return log(pDJIA(t + Δt))/log(pDJIA(t)) in this interval. (B) We determine the Pearson correlation coefficients of all pairs of all 30 DJIA components depicted in a matrix of correlation coefficients. Ticker symbols are used to abbreviate company names in this example. We calculate the mean correlation coefficient by averaging over all non-diagonal elements of this matrix.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3475344&req=5

f2: Visualization of the analysis method.(A) For a time interval of Δt trading days, we calculate for the index the price return log(pDJIA(t + Δt))/log(pDJIA(t)) in this interval. (B) We determine the Pearson correlation coefficients of all pairs of all 30 DJIA components depicted in a matrix of correlation coefficients. Ticker symbols are used to abbreviate company names in this example. We calculate the mean correlation coefficient by averaging over all non-diagonal elements of this matrix.
Mentions: To quantify state-dependent correlations, we calculate the mean value of Pearson product-moment correlation coefficients49 among all DJIA components in a time interval comprising Δt trading days each (Fig. 2). In each time interval comprising Δt trading day, we determine correlation coefficients for all pairs of N ≡ 30 stocks. From these correlation coefficients, we calculate their mean value for each time interval separately.

Bottom Line: Reliable estimates of correlations are absolutely necessary to protect a portfolio.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.

View Article: PubMed Central - PubMed

Affiliation: Warwick Business School, University of Warwick, Coventry, United Kingdom. mail@tobiaspreis.de

ABSTRACT
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.

Show MeSH
Related in: MedlinePlus