7 Pages Posted: 8 Oct 2012
Date Written: October 4, 2012
In this paper we discuss univariate and multivariate statistical properties of volatility with the aim of understanding how these two aspects are interrelated. Specifically, we focus on the relationship between the cross-correlation among stock's volatilities and the volatility clustering. Volatility clustering is related to the memory property of the volatility time-series and therefore to its predictability. Our results show that there exists a relationship between the level of predictability of any volatility time-series and the amount of its inter-dependence with other assets. In all considered cases, the more the asset is linked to other assets, the more its volatility keeps memory of its past behavior. We also show that the way the volatility autocorrelation function decays is only marginally related to the decay in the probability distribution function. As a side result, we present a parsimonious univariate model that well reproduces two stylized facts of volatility: the power-law decay of the volatility probability density function and the power-law decay of the autocorrelation function. It also reproduces, at least qualitatively, the empirical observation than when the probability density function decays faster, then the autocorrelation decays slower.
JEL Classification: G10, C10, C22
Suggested Citation: Suggested Citation
Miccichè, Salvatore, Empirical Study on the Relationship between the Cross-Correlation among Stocks and the Stocks' Volatility Clustering (October 4, 2012). Available at SSRN: https://ssrn.com/abstract=2158434 or http://dx.doi.org/10.2139/ssrn.2158434