Hidden temporal order unveiled in stock market volatility variance

In summary, our methodology can be used as a quantitative measure for the amount of local
temporal order. For any specific sample length n, the comparison between the variance of the samples
volatility for the original series and that of the shuffled one can be used as a measure for the temporal
order for that specific n. Repeating that procedure for each value of n, we get an integral measure for
the temporal order. Furthermore, it seems that the volatility is very important for technical analysis.
Our results suggest that traders who use technical analysis should use local temporal behavior of the
volatility, besides local temporal order of the return and the trade volume.  http://scitation.aip.org/getpdf/servlet/GetPDFServlet?filetype=pdf&id=AAIDBI000001000002022127000001&idtype=cvips&doi=10.1063/1.3598412&prog=normal http://www.sciencedaily.com/releases/2011/06/110608122819.htm

They analyzed the volatility time series of 10 different stock markets from seven countries over a period of about 50 years and, rather than following traditional economic analyses, they analyzed time variations in the volatility -- or the "volatility of volatility," a.k.a. "fear volatility."

In all markets studied, analysis revealed the existence of hidden temporal order in the volatility and very high correlations between the volatility and the magnitude of price variations. This marks the first time hidden temporal order has been found in these market "human factors."

The existence of such volatility order, or "ordered fear," implies that proper portfolio design should take into consideration the "volatility of volatility," according to the team. For example, the common approach to reducing risk is to select stocks with negative or low correlations in their sequence of returns. The new findings suggest that selection criteria should incorporate the correlations in the stocks' volatility dynamics.

"We're working on incorporating human factors into market analysis," Ben-Jacob says, "by constructing a new parameter to replace the traditional systemic risk parameter."