Statistical properties of volatility return intervals of Chinese stocks

TL;DR

This study analyzes the statistical properties of volatility return intervals in Chinese stocks, revealing scaling behaviors, short-term correlations, and long-term memory effects in the intervals between high-volatility events.

Contribution

It provides a detailed statistical analysis of return intervals, identifying scaling laws, fitting distributions, and demonstrating both short-term and long-term correlations in Chinese stock volatility data.

Findings

Scaling behavior in return interval distributions for some stocks.

Existence of short-term correlations between successive intervals.

Presence of long-term memory in volatility return intervals.

Abstract

The statistical properties of the return intervals $\tau_q$ between successive 1-min volatilities of 30 liquid Chinese stocks exceeding a certain threshold $q$ are carefully studied. The Kolmogorov-Smirnov (KS) test shows that 12 stocks exhibit scaling behaviors in the distributions of $\tau_q$ for different thresholds $q$. Furthermore, the KS test and weighted KS test shows that the scaled return interval distributions of 6 stocks (out of the 12 stocks) can be nicely fitted by a stretched exponential function $f(\tau/\bar{\tau})\sim e^{- \alpha (\tau/\bar{\tau})^{\gamma}}$ with $\gamma\approx0.31$ under the significance level of 5%, where $\bar{\tau}$ is the mean return interval. The investigation of the conditional probability distribution $P_q(\tau | \tau_0)$ and the mean conditional return interval $<\tau| \tau_0>$ demonstrates the existence of short-term correlation between successive return interval intervals. We further study the mean return interval $<\tau| \tau_0>$ after a cluster of $n$ intervals and the fluctuation $F(l)$ using detrended fluctuation analysis and find that long-term memory also exists in the volatility return intervals.