T. Yasseri | Searcharxiv

arXiv2017

Emergence of world-stock-market network

In the age of globalization, it is natural that the stock market of each country is not independent form the other markets. In this case, collective behavior could be emerged form their dependency together. This article studies the collective behavior of a set of forty influential markets in the world economy with the aim of exploring a global financial structure that could be called world-stock-market network. Towards this end, we analyze the cross-correlation matrix of the indices of these forty markets using Random Matrix Theory (RMT). We find the degree of collective behavior among the markets and the share of each market in their structural formation. This finding together with the results obtained from the same calculation on four stock markets reinforce the idea of a world financial market. Finally, we draw the dendrogram of the cross-correlation matrix to make communities in this abstract global market visible. The dendrogram, drawn by at least thirty percent of correlation, shows that the world financial market comprises three communities each of which includes stock markets with geographical proximity.

q-fin.ST↗

arXiv2016

Crossing Statistics of Anisotropic Stochastic Surface

In this paper, we propose crossing statistics and its generalization, as a new framework to characterize the anisotropy in a 2D field, e.g. height on a surface, extendable to higher dimensions. By measuring $ν^+$, the number of up-crossing (crossing points with positive slope at a given threshold of height ($α$)), and $N_{tot}$ (the generalized roughness function), it is possible to distinguish the nature of anisotropy, rotational invariance and Gaussianity of any given surface. For the case of anisotropic correlated self- or multi-affine surfaces (even with different correlation lengths in various directions and/or directional scaling exponents), we analytically derive some relations between $ν^+$ and $N_{tot}$ with corresponding scaling parameters. The method systematically distinguishes the directions of anisotropy, at $3σ$ confidence interval using P-value statistics. After applying a typical method in determining the corresponding scaling exponents in identified anisotropic directions, we are able to determine the kind and ratio of correlation length anisotropy. To demonstrate capability and accuracy of the method, as well validity of analytical relations, our proposed measures are calculated on synthetic stochastic rough interfaces and rough interfaces generated from simulation of ion etching. There are good consistencies between analytical and numerical computations. The proposed algorithm can be mounted with a simple software on various instruments for surface analysis and characterization, such as AFM, STM and etc.

physics.comp-ph↗