We study the return interval $tau$ between price volatilities that are above a certain threshold $q$ for 31 intraday datasets, including the Standard & Poors 500 index and the 30 stocks that form the Dow Jones Industrial index. For different threshold $q$, the probability density function $P_q(tau)$ scales with the mean interval $bar{tau}$ as $P_q(tau)={bar{tau}}^{-1}f(tau/bar{tau})$, similar to that found in daily volatilities. Since the intraday records have significantly more data points compared to the daily records, we could probe for much higher thresholds $q$ and still obtain good statistics. We find that the scaling function $f(x)$ is consistent for all 31 intraday datasets in various time resolutions, and the function is well approximated by the stretched exponential, $f(x)sim e^{-a x^gamma}$, with $gamma=0.38pm 0.05$ and $a=3.9pm 0.5$, which indicates the existence of correlations. We analyze the conditional probability distribution $P_q(tau|tau_0)$ for $tau$ following a certain interval $tau_0$, and find $P_q(tau|tau_0)$ depends on $tau_0$, which demonstrates memory in intraday return intervals. Also, we find that the mean conditional interval $<tau|tau_0>$ increases with $tau_0$, consistent with the memory found for $P_q(tau|tau_0)$. Moreover, we find that return interval records have long term correlations with correlation exponents similar to that of volatility records.
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