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Dynamic Long Memory High Frequency Multipower Variation Volatility Evaluations for S&P500

Author

Listed:
  • Wen Cheong Chin
  • Min Cherng Lee
  • Tan Pei Pei
  • Grace Lee Ching Yap
  • ChristineTan Nya Ling

Abstract

This study explores the multipower variation integrated volatility estimates using high frequency data in financial stock market. The different combinations of multipower variation estimators are robust to drastic financial jumps and market microstructure noise. In order to examine the informationally market efficiency, we proposed a rolling window estimate procedures of Hurst parameter using the modified rescale-range approach. In order to test the robustness of the method, we have selected the S&P500 as the empirical data. The empirical study found that the long memory cascading volatility is fluctuating across the studied period and drastically trim down after the subprime mortgage crisis. This time-varying long memory analysis allow us to understand the informationally market efficiency before and after the subprime mortgage crisis in U.S.

Suggested Citation

  • Wen Cheong Chin & Min Cherng Lee & Tan Pei Pei & Grace Lee Ching Yap & ChristineTan Nya Ling, 2016. "Dynamic Long Memory High Frequency Multipower Variation Volatility Evaluations for S&P500," Modern Applied Science, Canadian Center of Science and Education, vol. 10(5), pages 1-1, May.
    • Handle: RePEc:ibn:masjnl:v:10:y:2016:i:5:p:1
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    References listed on IDEAS

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    1. Andersen, Torben G & Bollerslev, Tim, 1998. "Answering the Skeptics: Yes, Standard Volatility Models Do Provide Accurate Forecasts," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 39(4), pages 885-905, November.
    2. Shirota, Shinichiro & Hizu, Takayuki & Omori, Yasuhiro, 2014. "Realized stochastic volatility with leverage and long memory," Computational Statistics & Data Analysis, Elsevier, vol. 76(C), pages 618-641.
    3. Su, Jung-Bin, 2014. "Empirical analysis of long memory, leverage, and distribution effects for stock market risk estimates," The North American Journal of Economics and Finance, Elsevier, vol. 30(C), pages 1-39.
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    More about this item

    JEL classification:

    • R00 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General - - - General
    • Z0 - Other Special Topics - - General

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