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Long Memory Features in Return and Volatility of the Malaysian Stock Market

Author

Listed:
  • Siow-Hooi Tan

    (Faculty of Management, Multimedia University, Malaysia)

  • Mohammad Tariqul Islam Khan

    (Faculty of Management, Multimedia University, Malaysia)

Abstract

This study aims to investigate the existence of long memory in the Malaysian stock market utilizing daily stock price index from the period 1998:09 to 2009:12. Various ARFIMA-G(ARCH)-type models have been taken into consideration to address this issue, which has led to several interesting conclusions. Firstly, the long memory property exists in both the return and volatility, with and without incorporating the crisis impact. Secondly, the stock volatility is found to be experiencing significant leverage effect especially with the inclusion of the impact of crisis. This implies that the volatility has the tendency to respond to bad news more than good news as compared to the other periods under study. Thirdly, among the various G(ARCH)-type models with different innovation distributions, the Student-t distribution provides better specifications in terms of the long memory volatility processes. In summary, ARFIMA-FIAPARCH model is found to be the most appropriate method of presenting the stylized facts of stock return and volatility in Malaysia.

Suggested Citation

  • Siow-Hooi Tan & Mohammad Tariqul Islam Khan, 2010. "Long Memory Features in Return and Volatility of the Malaysian Stock Market," Economics Bulletin, AccessEcon, vol. 30(4), pages 3267-3281.
    • Handle: RePEc:ebl:ecbull:eb-10-00299
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    File URL: http://www.accessecon.com/Pubs/EB/2010/Volume30/EB-10-V30-I4-P301.pdf
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    References listed on IDEAS

    as
    1. Cunado, J. & Gil-Alana, L.A. & Gracia, Fernando Perez de, 2010. "Mean reversion in stock market prices: New evidence based on bull and bear markets," Research in International Business and Finance, Elsevier, vol. 24(2), pages 113-122, June.
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    Cited by:

    1. Heni Boubaker & Giorgio Canarella & Rangan Gupta & Stephen M. Miller, 2023. "A Hybrid ARFIMA Wavelet Artificial Neural Network Model for DJIA Index Forecasting," Computational Economics, Springer;Society for Computational Economics, vol. 62(4), pages 1801-1843, December.
    2. Quynh-Trang Nguyen & John Francis Diaz & Jo-Hui Chen & Ming-Yen Lee, 2019. "Fractional Integration in Corporate Social Responsibility Indices: A FIGARCH and HYGARCH Approach," Asian Economic and Financial Review, Asian Economic and Social Society, vol. 9(7), pages 836-850, July.
    3. Argel S. Masa & John Francis T. Diaz, 2017. "Long-memory Modelling and Forecasting of the Returns and Volatility of Exchange-traded Notes (ETNs)," Margin: The Journal of Applied Economic Research, National Council of Applied Economic Research, vol. 11(1), pages 23-53, February.
    4. Tripathy, Naliniprava, 2022. "Long memory and volatility persistence across BRICS stock markets," Research in International Business and Finance, Elsevier, vol. 63(C).
    5. Muhammad Naeem & Hao Ji & Brunero Liseo, 2014. "Negative Return-Volume Relationship in Asian Stock Markets: Figarch-Copula Approach," Eurasian Journal of Economics and Finance, Eurasian Publications, vol. 2(2), pages 1-20.
    6. John Francis Diaz & Jo-Hui Chen, 2017. "Testing for Long-memory and Chaos in the Returns of Currency Exchange-traded Notes (ETNs)," Journal of Applied Finance & Banking, SCIENPRESS Ltd, vol. 7(4), pages 1-2.
    7. Rim Ammar Lamouchi, 2020. "Long Memory and Stock Market Efficiency: Case of Saudi Arabia," International Journal of Economics and Financial Issues, Econjournals, vol. 10(3), pages 29-34.
    8. Malinda & Maya & Jo-Hui & Chen, 2022. "Testing for the Long Memory and Multiple Structural Breaks in Consumer ETFs," Journal of Applied Finance & Banking, SCIENPRESS Ltd, vol. 12(6), pages 1-6.

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    More about this item

    Keywords

    long memory property; leverage effect; ARFIMA-G(ARCH) models;
    All these keywords.

    JEL classification:

    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General
    • G0 - Financial Economics - - General

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