Multifractal properties of price fluctuations of stocks and commodities

By K. Matia, Yosef Ashkenazy, H. Eugene Stanley. 2003

330 citations recorded by the source corpus.

We analyze daily prices of 29 commodities and 2449 stocks, each over a period of ≈ 15 years. We find that the price fluctuations for commodities have a significantly broader multifractal spectrum than for stocks. We also propose that multifractal properties of both stocks and commodities can be attributed mainly to the broad probability distribution of price fluctuations and secondarily to their temporal organization. Furthermore, we propose that, for commodities, stronger higher-order correlations in price fluctuations result in broader multifractal spectra.

Subfields: Economics and Econometrics, Finance

View Paper: https://www.semanticscholar.org/paper/09e2706e06b51a0135ac34194412409a312bfab6

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Citations in the corpus

On non-Gaussianity and dependence in financial time series: a nonextensive approach (2005, background): “…some nonlinear systems, e.g. Lyra and Tsallis (1998), that max and min of the f ( ) curve (which can also be determined for financial time series (Matia et al. 2003, Muzy et al. 2000) present an analytical connection with entropic index q, i.e. the breakdown of ergodicity by memory, resulting in…”
Statistical properties and multifractality of Bitcoin (2017, methodology): “The MF–DFA, which is an extended method of DFA, can investigate multifractal properties of non-stationary time series, and has been successfully applied for a variety of financial markets, such as stock [16, 17, 18, 19, 20, 21, 22, 23], commodity [16, 24, 25, 26, 27], tanker [28], derivative [29], f”
A new approach to quantify power-law cross-correlation and its application to commodity markets (2011, background): “Empirical evidence supports the existence of fractals or multifractals in commodity or financial markets [1-6].”
Fundamentals of natural computing: an overview (2007, background): “However, fractals have several other approaches and applications, among which the main current trends are the use of fractals as function approximators [19,46,200], their applications in economics and finance [163,184,185,189], several technological applications, such as antenna design, data mining ”
Multifractal analysis of managed and independent float exchange rates (2015, methodology): “In this work we use the MF-DFA method which has been successfully applied in various phenomena such as physiological signals [16], hydrological processes [17], geophysical data [18], forest fires records [19] and financial time series [8,9,11].”