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Evidence of multi-affinity in the Japanese stock market

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  • Katsuragi, Hiroaki

Abstract

Fluctuations of the Japanese stock market (Tokyo Stock Price Index: TOPIX) are analyzed using a multi-affine analysis method. In the research to date, only some simulated self-affine models have shown multi-affinity. In most experiments using observations of self-affine fractal profiles, multi-affinity has not been found. However, we find evidence of multi-affinity in fluctuations of the Japanese stock market (TOPIX). The qth-order Hurst exponent Hq varies with changes in q. This multi-affinity indicates that there are plural mechanisms that affect the same time scale as stock market price fluctuation dynamics.

Suggested Citation

  • Katsuragi, Hiroaki, 2000. "Evidence of multi-affinity in the Japanese stock market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 278(1), pages 275-281.
    • Handle: RePEc:eee:phsmap:v:278:y:2000:i:1:p:275-281
    DOI: 10.1016/S0378-4371(00)00004-2
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    Cited by:

    1. Ho, Ding-Shun & Lee, Chung-Kung & Wang, Cheng-Cai & Chuang, Mang, 2004. "Scaling characteristics in the Taiwan stock market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 332(C), pages 448-460.
    2. Stavroyiannis, S. & Makris, I. & Nikolaidis, V., 2010. "Non-extensive properties, multifractality, and inefficiency degree of the Athens Stock Exchange General Index," International Review of Financial Analysis, Elsevier, vol. 19(1), pages 19-24, January.
    3. Li, Yong, 2019. "Multifractal view on China’s stock market crashes," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 536(C).
    4. Zunino, Luciano & Figliola, Alejandra & Tabak, Benjamin M. & Pérez, Darío G. & Garavaglia, Mario & Rosso, Osvaldo A., 2009. "Multifractal structure in Latin-American market indices," Chaos, Solitons & Fractals, Elsevier, vol. 41(5), pages 2331-2340.
    5. Zunino, L. & Tabak, B.M. & Figliola, A. & Pérez, D.G. & Garavaglia, M. & Rosso, O.A., 2008. "A multifractal approach for stock market inefficiency," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(26), pages 6558-6566.
    6. Zhou, Yaping & Lu, Baoqun & Lv, Dayong & Ruan, Qingsong, 2019. "The informativeness of options-trading activities: Non-linear analysis based on MF-DCCA and Granger test," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 534(C).
    7. Li, Yong & Vilela, André L.M. & Stanley, H. Eugene, 2020. "The institutional characteristics of multifractal spectrum of China’s stock market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 550(C).
    8. Zhu, Huijian & Zhang, Weiguo, 2018. "Multifractal property of Chinese stock market in the CSI 800 index based on MF-DFA approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 490(C), pages 497-503.
    9. Mensi, Walid & Tiwari, Aviral Kumar & Al-Yahyaee, Khamis Hamed, 2019. "An analysis of the weak form efficiency, multifractality and long memory of global, regional and European stock markets," The Quarterly Review of Economics and Finance, Elsevier, vol. 72(C), pages 168-177.
    10. Du, Guoxiong & Ning, Xuanxi, 2008. "Multifractal properties of Chinese stock market in Shanghai," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(1), pages 261-269.

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