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>
Highlights
Volatility is one of the important elements in nowadays financial investment strategies such as asset management (Gormus, Soytas, & David Diltz, 2014), options pricing (Birkelund et al, 2015), forecasting future returns (Gallo & Otrando, 2015) and various risk management (Chkili, Hammoudeh, & Nguyen, 2014) applications
For the high frequency data background, we begin with the approximation of the realized volatility (RV) to latent volatility which is related to the theory of quadratic variation and integrated variance (Andersen and Bollerslev, 1998)
The usage of high frequency data in financial market analysis become crucial due to its accuracy in estimation which can be used in finance application such as forecasting, risk analysis and portfolio management
Summary
Volatility is one of the important elements in nowadays financial investment strategies such as asset management (Gormus, Soytas, & David Diltz, 2014), options pricing (Birkelund et al, 2015), forecasting future returns (Gallo & Otrando, 2015) and various risk management (Chkili, Hammoudeh, & Nguyen, 2014) applications. Since each group of time horizon investors consist of their own trading rules and strategies to inflowing information, these trading activities are creating a cascade of volatilities for all the time horizon investments The combinations of these dissimilar volatilities (due to reaction times) are believed to produce hyperbolic autocorrelation decays or long memory property in financial markets. The long memory is considered as constant over the whole period of studied period which not realistic in the complex dynamic financial market This issue can be overcome by using a rolling estimation (Cajueiro & Tabak, 2005, 2008) over a fix time-window, says 512 or 1024 data points. Unlike prior studies using realized volatility and static long memory estimation, our focus is to take into account the microstructure noise, possible abrupt jumps and time-varying latent volatility measurements. The remaining of this study is organized as follows: Section 2 provides the description of multipower variation of volatility estimations and the rolling window Hurst parameter estimations; Section 3 discusses the empirical data and results and Section 4 concludes the findings of the study
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