A highly cited article "Empirical properties of asset returns: stylized facts and statistical issues" by R. Cont use the Figure 8. below to illustrate
the well-known phenomenon of volatility clustering: large price variations are more likely to be followed by large price variations.
Note the burst of autocorrelation of $x^2$ (red dotted line) at lag $\approx 85$.
Later on the author publishes even higher cited book "Financial Modelling with Jump Processes" (together with P. Tankov). On the FIGURE 7.3 in the book (shown below) the same autocorrelation of $x^2$ as above is shown. But this time without the burst!
Also I have to admit that I couldn't understand x axis legend for both pictures above.
So it would be great if someone who has access to S&P 500 Index futures intraday 1-minute data for 1991-1995 could calculate autocorrelation function of squared price increments and publish it here!
UPDATE I've quickly prepared a similar graph using data for 1998-2012 kindly provided by @David Addison. In appears that
- autocorellations are two times higher than in the article and in the book
- power law (blue line) does not seem to be a very good fit
P.S. I do not claim that above I and David has provided a reliable evidence of anything. Rather it should be considered as an additional justification for the question and request. Let's reproduce the results of Professor Rama Cont !