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Non‐periodic Australian Stock Market Cycles: Evidence from Rescaled Range Analysis

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  • Michael D. McKenzie

Abstract

The standard complement of statistical techniques used to identify predictable market structure is only capable of identifying regular periodic cycles and assumes that the data are independent and identically distributed (i.i.d.). Yet, financial returns data are not independent and cycles are most probably not periodic. Rescaled range analysis is a nonparametric technique that can distinguish the average cycle length of irregular cycles. Using Australian stock market data, this paper finds evidence of long memory in the returns generating process and non‐periodic cycles of approximately 3, 6 and 12 years in average duration.

Suggested Citation

  • Michael D. McKenzie, 2001. "Non‐periodic Australian Stock Market Cycles: Evidence from Rescaled Range Analysis," The Economic Record, The Economic Society of Australia, vol. 77(239), pages 393-406, December.
    • Handle: RePEc:bla:ecorec:v:77:y:2001:i:239:p:393-406
    DOI: 10.1111/1475-4932.00032
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    Cited by:

    1. Onali, Enrico & Goddard, John, 2011. "Are European equity markets efficient? New evidence from fractal analysis," International Review of Financial Analysis, Elsevier, vol. 20(2), pages 59-67, April.
    2. Ivani Bora & Naliniprava Tripathy, 2016. "Random or Deterministic? Evidence from Indian Stock Market," International Journal of Economics and Financial Issues, Econjournals, vol. 6(4), pages 1716-1721.
    3. Kuttu, Saint, 2018. "Modelling long memory in volatility in sub-Saharan African equity markets," Research in International Business and Finance, Elsevier, vol. 44(C), pages 176-185.
    4. Karuppiah, Jeyanthi & Los, Cornelis A., 2005. "Wavelet multiresolution analysis of high-frequency Asian FX rates, Summer 1997," International Review of Financial Analysis, Elsevier, vol. 14(2), pages 211-246.
    5. Onali, Enrico & Goddard, John, 2009. "Unifractality and multifractality in the Italian stock market," International Review of Financial Analysis, Elsevier, vol. 18(4), pages 154-163, September.
    6. David G. McMillan & Pako Thupayagale, 2009. "The efficiency of African equity markets," Studies in Economics and Finance, Emerald Group Publishing Limited, vol. 26(4), pages 275-292, October.
    7. Mynhardt, H. R. & Plastun, Alex & Makarenko, Inna, 2014. "Behavior of Financial Markets Efficiency During the Financial Market Crisis: 2007-2009," MPRA Paper 58942, University Library of Munich, Germany.
    8. Mr. Jun Nagayasu, 2003. "The Efficiency of the Japanese Equity Market," IMF Working Papers 2003/142, International Monetary Fund.
    9. Vogl, Markus, 2022. "Controversy in financial chaos research and nonlinear dynamics: A short literature review," Chaos, Solitons & Fractals, Elsevier, vol. 162(C).
    10. Bai, Limiao & Yan, Sen & Zheng, Xiaolian & Chen, Ben M., 2015. "Market turning points forecasting using wavelet analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 437(C), pages 184-197.
    11. Goddard, John & Onali, Enrico, 2012. "Self-affinity in financial asset returns," International Review of Financial Analysis, Elsevier, vol. 24(C), pages 1-11.
    12. Keith Jefferis & Pako Thupayagale, 2008. "Long Memory In Southern African Stock Markets," South African Journal of Economics, Economic Society of South Africa, vol. 76(3), pages 384-398, September.
    13. Kristoufek, Ladislav, 2009. "Procesy s dlouhou pamětí a jejich vývoj ve výnosech indexu PX v letech 1999 – 2009 [Long-term memory and its evolution in returns of PX between 1999 and 2009]," MPRA Paper 16435, University Library of Munich, Germany.

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