This study explores the multipower variation integrated volatility estimates using high frequency data in financial stock market. The different combinatio..." /> This study explores the multipower variation integrate..." /> This study explores the multipower variation integrate..." />

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

  • Chin W
  • Cherng Lee M
  • Pei Pei T
  • et al.
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Abstract

<p class="MsoNormal" style="text-align: justify; text-justify: inter-ideograph; tab-stops: 56.7pt;">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&amp;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.</p>

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APA

Chin, W. C., Cherng Lee, M., Pei Pei, T., Ching Yap, G. L., & Nya Ling, C. (2016). Dynamic Long Memory High Frequency Multipower Variation Volatility Evaluations for S&P500. Modern Applied Science, 10(5), 1. https://doi.org/10.5539/mas.v10n5p1

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