Multifactor Analysis of Multiscaling in Volatility Return Intervals

Abstract

We study the volatility time series of 1137 most traded stocks in the US stock markets for the two-year period 2001-02 and analyze their return intervals $\tau$, which are time intervals between volatilities above a given threshold $q$. We explore the probability density function of $\tau$, $P_q(\tau )$, assuming a stretched exponential function, $P_q(\tau )\sim e^{-{\tau }^{\gamma }}$. We find that the exponent $\gamma$ depends on the threshold in the range between $q=1$ and 6 standard deviations of the volatility. This finding supports the multiscaling nature of the return interval distribution. To better understand the multiscaling origin, we study how $\gamma$ depends on four essential factors, capitalization, risk, number of trades and return. We show that $\gamma$ depends on the capitalization, risk and return but almost does not depend on the number of trades. This suggests that $\gamma$ relates to the portfolio selection but not on the market activity. To further characterize the multiscaling of individual stocks, we fit the moments of $\tau$, ${\mu }_m\equiv <(\tau /<\tau >{)}^m{>}^{1/m}$, in the range of $10<<\tau >\le 100$ by a power-law, ${\mu }_m\sim <\tau {>}^{\delta }$. The exponent $\delta$ is found also to depend on the capitalization, risk and return but not on the number of trades, and its tendency is opposite to that of $\gamma$. Moreover, we show that $\delta$ decreases with $\gamma$ approximately by a linear relation. The return intervals demonstrate the temporal structure of volatilities and our findings suggest that their multiscaling features may be helpful for portfolio optimization.

16 pages, 6 figures

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