Scaling and memory of intraday volatility return intervals in stock market

Abstract

We study the return interval $τ$ between price volatilities that are above a certain threshold $q$ for 31 intraday datasets, including the Standard & Poor's 500 index and the 30 stocks that form the Dow Jones Industrial index. For different threshold $q$, the probability density function $P_q(τ)$ scales with the mean interval $\barτ$ as $P_q(τ)={\barτ}^{-1}f(τ/\barτ)$, 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, $f(x)\sim e^{-a x^γ}$, with $γ=0.38\pm 0.05$ and $a=3.9\pm 0.5$, which indicates the existence of correlations. We analyze the conditional probability distribution $P_q(τ|τ_0)$ for $τ$ following a certain interval $τ_0$, and find $P_q(τ|τ_0)$ depends on $τ_0$, which demonstrates memory in intraday return intervals. Also, we find that the mean conditional interval $<τ|τ_0>$ increases with $τ_0$, consistent with the memory found for $P_q(τ|τ_0)$. Moreover, we find that return interval records have long term correlations with correlation exponents similar to that of volatility records.

19 pages, 8 figures