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Wavelet Test of Multifractality of Asia-Pacific Index Price Series

Author

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  • Rossitsa Yalamova

    (Department of Finance, Faculty of Management, University of Lethbridge, 4401 University Dr, W. Lethbridge, 71K 3M4, Canada)

Abstract

This paper argues for the superiority of multifractal over ARCH methods where the objective is to understand market microstructure based on accurate volatility modeling. The paper examines the multifractality of index price series on daily data of Nikkei 225, All Ordinaries, Hang Seng, KLSE Composite and Straits Times Index. Wavelets, short form waves with local support are used for time/scale decomposition of financial time series. The multifractal spectrum (MFS) of daily index prices is calculated with Wavelet Transform Modulus Maxima method described in Yalamova (2003). The MFS may reveal trading time irregularities suggested by the Multifractal Model of Asset Returns (Calvet & Fisher, 2002). The trading time deformation process may uncover information on the efficiency of the trading system that would be useful for regulatory and reorganization purposes. Multifractals describe the cascade of volatility of returns and are suited for research at different time scales simultaneously unlike ARCH type models. In addition, this method provides dimension estimates for the detection of emerging chaotic patterns. The Hurst exponent calculated from the scaling function indicates persistence in volatility of index returns. The choice of data around the October 1997 drawdown is based on the scientific evidence that markets as complex dynamical systems reveal their properties better in extreme conditions.

Suggested Citation

  • Rossitsa Yalamova, 2006. "Wavelet Test of Multifractality of Asia-Pacific Index Price Series," Asian Academy of Management Journal of Accounting and Finance (AAMJAF), Penerbit Universiti Sains Malaysia, vol. 2(1), pages 63-83.
    • Handle: RePEc:usm:journl:aamjaf00201_63-83
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    Citations

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    Cited by:

    1. Benbachir, Saâd & El Alaoui, Marwane, 2011. "A Multifractal Detrended Fluctuation Analysis of the Moroccan Stock Exchange," MPRA Paper 49003, University Library of Munich, Germany.
    2. Hasan, Rashid & Mohammad, Salim M., 2015. "Multifractal analysis of Asian markets during 2007–2008 financial crisis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 419(C), pages 746-761.
    3. Jammazi, Rania, 2012. "Oil shock transmission to stock market returns: Wavelet-multivariate Markov switching GARCH approach," Energy, Elsevier, vol. 37(1), pages 430-454.
    4. El Alaoui, Marwane & Benbachir, Saâd, 2013. "Multifractal detrended cross-correlation analysis in the MENA area," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(23), pages 5985-5993.
    5. Lu Han & Ruihuan Ge, 2017. "Wavelets Analysis on Structural Model for Default Prediction," Computational Economics, Springer;Society for Computational Economics, vol. 50(1), pages 111-140, June.
    6. Memon, Bilal Ahmed & Yao, Hongxing & Naveed, Hafiz Muhammad, 2022. "Examining the efficiency and herding behavior of commodity markets using multifractal detrended fluctuation analysis. Empirical evidence from energy, agriculture, and metal markets," Resources Policy, Elsevier, vol. 77(C).
    7. El Alaoui, Marwane, 2017. "Price–volume multifractal analysis of the Moroccan stock market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 486(C), pages 473-485.

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