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1

School of Statistics and Mathematics, Central University of Finance and Economics, Beijing 100081, China

2

Cracow University of Economics, Rakowicka 27, Kraków 31-510, Poland





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Academic Editor: Christian Brownlees

Abstract Analyzing social systems, particularly financial markets, using a complex network approach has become one of the most popular fields within econophysics. A similar trend is currently appearing within the econometrics and finance communities, as well. In this study, we present a state-of-the-artmethod for analyzing the structure and risk within stockmarkets, treating them as complex networks using model-free, nonlinear dependency measures based on information theory. This study is the first network analysis of the stockmarket in Shanghai using a nonlinear network methodology. Further, it is often assumed that markets outside the United States and Western Europe are inherently riskier. We find that the Chinese stock market is not structurally risky, contradicting this popular opinion. We use partial mutual information to create filtered networks representing the Shanghai stock exchange, comparing them to networks based on Pearson’s correlation. Consequently, we discuss the structure and characteristics of both the presented methods and the Shanghai stock exchange. This paper provides an insight into the cutting edge methodology designed for analyzing complex financial networks, as well as analyzing the structure of the market in Shanghai and, as such, is of interest to both researchers and financial analysts. View Full-Text

Keywords: dependency networks; financial markets; complex systems; mutual information dependency networks; financial markets; complex systems; mutual information





Autor: Tao You 1, Paweł Fiedor 2,* and Artur Hołda 2

Fuente: http://mdpi.com/



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