Dominique, CRene (2018): Could Noise Spectra of Strange Attractors Better Explained Wealth and Income Inequalities? Evidence from the S&P500 Index.

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Abstract
SUMMARY: Inequity in wealth and income distributions is ubiquitous and persistent in markets economies. Economists have long suspected that this might be due to the workings of a power law. But studies in financial economics have focused mainly on tail exponent while attempting to recover the Pareto and Zipf’s laws. The estimation of tail exponents from loglog plots, as in stock market returns, produces biased estimators and has little impact on policy. This paper argues that economic time series are output signals of a multifractal process driven by strange attractors. Consequently, estimating noise spectra thrownup by strange attractors stands to produce a much richer set of information, including the lower and upper bounds of unequal income distribution.
Item Type:  MPRA Paper 

Original Title:  Could Noise Spectra of Strange Attractors Better Explained Wealth and Income Inequalities? Evidence from the S&P500 Index. 
English Title:  Could Noise Spectra of Strange Attractors Better Explained Wealth and Income Inequalities? Evidence from the S&P500 Index. 
Language:  English 
Keywords:  noise spectra, singularity spectrum, correlation dimension, income distribution, fractal attractor, scale exponent. 
Subjects:  G  Financial Economics > G1  General Financial Markets G  Financial Economics > G1  General Financial Markets > G14  Information and Market Efficiency ; Event Studies ; Insider Trading 
Item ID:  84182 
Depositing User:  CRene Dominique 
Date Deposited:  26 Jan 2018 10:14 
Last Modified:  27 Sep 2019 04:40 
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URI:  https://mpra.ub.unimuenchen.de/id/eprint/84182 