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A distribution‐based method to gauge market liquidity through scale invariance between investment horizons
Author(s) -
Bianchi Sergio,
Pianese Augusto,
Frezza Massimiliano
Publication year - 2020
Publication title -
applied stochastic models in business and industry
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.413
H-Index - 40
eISSN - 1526-4025
pISSN - 1524-1904
DOI - 10.1002/asmb.2531
Subject(s) - econometrics , market liquidity , similarity (geometry) , nonparametric statistics , scale invariance , fractal , scaling , investment (military) , mathematics , economics , distribution (mathematics) , statistics , computer science , mathematical analysis , finance , artificial intelligence , geometry , politics , political science , law , image (mathematics)
A nonparametric method is developed to detect self‐similarity among the rescaled distributions of the log‐price variations over a number of time scales. The procedure allows to test the statistical significance of the scaling exponent that possibly characterizes each pair of time scales and to analyze the link between self‐similarity and liquidity, the core assumption of the fractal market hypothesis. The method can support financial operators in the selection of the investment horizons as well as regulators in the adoption of guidelines to improve the stability of markets. The analysis performed on the S&P500 reveals a very complex, time‐changing scaling structure, which confirms the link between market liquidity and self‐similarity.