Disturbances and complexity in volatility time series

Abstract

Recent works in econophysics have quantitatively shown that the latest global financial crisis has considerably affected nonlinear dynamics in markets worldwide. In the current study, we focus on complexity in volatility time series during pre-crisis, crisis, and post-crisis time periods. In this regard, a large set of international stock and commodity markets as well as economic uncertainty indices is considered in our work. The main finding is that empirical distributions of long memory parameter, Kolmogorov complexity and Shannon entropy, have all varied across pre-crisis, crisis, and post-crisis time periods. In other words, all three complexity measures are informative and suitable in order to characterize nonlinear dynamics in volatility series throughout the examined sample periods. Indeed, it was found that complexity increased during crisis period, yet diminished during the pre-crisis period. Overall, the latest financial crisis has truly affected complexity revealed in the volatility time series of the world major markets.