Abstract
We report evidence of a deep interplay between cross-correlations hierarchical properties and multifractality of New York Stock Exchange daily stock returns. The degree of multifractality displayed by different stocks is found to be positively correlated to their depth in the hierarchy of cross-correlations. We propose a dynamical model that reproduces this observation along with an array of other empirical properties. The structure of this model is such that the hierarchical structure of heterogeneous risks plays a crucial role in the time evolution of the correlation matrix, providing an interpretation to the mechanism behind the interplay between cross-correlation and multifractality in financial markets, where the degree of multifractality of stocks is associated to their hierarchical positioning in the cross-correlation structure. Empirical observations reported in this paper present a new perspective towards the merging of univariate multi scaling and multivariate cross-correlation properties of financial time series.
Highlights
Dependency structure and scaling properties of financial time series are related Raffaello Morales[1], T
The taxonomy of the stocks in the respective sectors is given in the Supplementary Material (SM), where we report details on the clusters detected through DBHT clustering algorithm
We have performed the clustering by means of a different clustering procedure, namely the Single Linkage Cluster Analysis (SLCA)[36], whose comparison with DBHT we report in SM
Summary
Dependency structure and scaling properties of financial time series are related Raffaello Morales[1], T. In this paper we point out that -- they are related and we propose a model that can reproduce the observed relationship In spite of their intrinsic complexity, prices show remarkable regularities, which are commonly referred to as stylised facts[6,7,8]. Prices increments at low frequencies (intra-day to weekly) are known to show non-Gaussian behaviour and other distributions, for example Student-t with degrees of freedom in the range [3, 5] have been shown to better fit financial returns[11] Another ubiquitous property not exhibited by random noise is the presence of persistence in the price increments, responsible for a phenomenon which is commonly referred to as volatility clustering[12,13]. The hierarchical organisation provides a very useful display of the hidden dependency patterns governing a set of stocks: it reveals which www.nature.com/scientificreports prices are more strongly correlated with respect to the rest of the market and it has been shown to match the intuition of explaining market moves in terms of few hierarchical factors[32]
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
