Efficient or Fractal Market Hypothesis? A Stock Indexes Modelling Using Geometric Brownian Motion and Geometric Fractional Brownian Motion

Mathematics

Emil Dinga¹  Vasile Brătian²  Camelia Oprean-Stan²  Ana-Maria Acu³  Gabriela-Mariana Ionescu⁴ 

[1] Centre for Financial and Monetary Research “Victor Slăvescu”, Romanian Academy, 010071 Bucharest, Romania;Department of Finance and Accounting, Faculty of Economics, Lucian Blaga University of Sibiu, 550024 Sibiu, Romania;Department of Mathematics and Informatics, Faculty of Sciences, Lucian Blaga University of Sibiu, 550024 Sibiu, Romania;Faculty of Economic Sciences, Lucian Blaga University of Sibiu, 550324 Sibiu, Romania;

关键词: geometric Brownian motion;    geometric fractional Brownian motion;    efficient market hypothesis;    fractal market hypothesis;   

DOI  :  10.3390/math9222983

来源: DOAJ

【 摘 要 】

In this article, we propose a test of the dynamics of stock market indexes typical of the US and EU capital markets in order to determine which of the two fundamental hypotheses, efficient market hypothesis (EMH) or fractal market hypothesis (FMH), best describes market behavior. The article’s major goal is to show how to appropriately model return distributions for financial market indexes, specifically which geometric Brownian motion (GBM) and geometric fractional Brownian motion (GFBM) dynamic equations best define the evolution of the S&P 500 and Stoxx Europe 600 stock indexes. Daily stock index data were acquired from the Thomson Reuters Eikon database during a ten-year period, from January 2011 to December 2020. The main contribution of this work is determining whether these markets are efficient (as defined by the EMH), in which case the appropriate stock indexes dynamic equation is the GBM, or fractal (as described by the FMH), in which case the appropriate stock indexes dynamic equation is the GFBM. In this paper, we consider two methods for calculating the Hurst exponent: the rescaled range method (RS) and the periodogram method (PE). To determine which of the dynamics (GBM, GFBM) is more appropriate, we employed the mean absolute percentage error (MAPE) method. The simulation results demonstrate that the GFBM is better suited for forecasting stock market indexes than the GBM when the analyzed markets display fractality. However, while these findings cannot be generalized, they are verisimilar.

【 授权许可】

Unknown