Volatility is usually employed to measure the dispersion of asset returns, and it’s widely used in risk analysis and asset management. This first chapter studies a kernel-based spot volatility matrix estimator with pre-averaging approach for high-frequency data contaminated by market microstructure noise. When the sample size goes to infinity and the bandwidth vanishes, we show that our estimator is consistent and its asymptotic normality is established with achieving an optimal convergence rate. We also construct a consistent pairwise spot co-volatility estimator with Hayashi-Yoshida method for non-synchronous high-frequency data with noise contamination. The simulation studies demonstrate that the proposed estimators work well under different noise levels, and their estimation performances are improved by the increasing sample frequency. In empirical applications, we implement the estimators on the intraday prices of four component stocks of Dow Jones Industrial Average. The second chapter shows a factor-based vast volatility matrix estimation method for high- frequency financial data with market microstructure noise, finite large jumps and infinite activity small jumps. We construct the sample volatility matrix estimator based on the approximate factor model, and use the pre-averaging and thresholding estimation method (PATH) to digest the noise and jumps. After using the principle component analysis (PCA) to decompose the sample volatility matrix estimator, our proposed volatility matrix estimator is finally obtained by imposing the block-diagonal regularization on the residual covariance matrix through sorting the assets with the global industry classification standard (GICS) codes. The Monte Carlo simulation shows that our proposed volatility matrix estimator can remove the majority effects of noise and jumps, and its estimation performance improves fast when the sample frequency increases. Finally, the PCA-based estimators are employed to perform volatility matrix estimation and asset allocation for S&P 500 stocks. To compare with PCA-based estimators, we also include the exchange-traded funds (ETFs) data to construct observable factors such as the Fama-French factors for volatility estimation. / A Dissertation submitted to the Department of Statistics in partial fulfillment of the requirements for the degree of Doctor of Philosophy. / Spring Semester 2018. / April 17, 2018. / Factor Model, High-frequency data, Jumps, Market microstructure noise, PCA, Volatility matrix / Includes bibliographical references. / Minjing Tao, Professor Directing Dissertation; Yingmei Cheng, University Representative; Fred Huffer, Committee Member; Xu-Feng Niu, Committee Member.
| Identifer | oai:union.ndltd.org:fsu.edu/oai:fsu.digital.flvc.org:fsu_653534 |
| Contributors | Xue, Yang (author), Tao, Minjing (professor directing dissertation), Cheng, Yingmei (university representative), Fendler, Rachel Loveitt (university representative), Huffer, Fred W. (committee member), Niu, Xufeng, 1954- (committee member), Florida State University (degree granting institution), College of Arts and Sciences (degree granting college), Department of Statistics (degree granting departmentdgg) |
| Publisher | Florida State University |
| Source Sets | Florida State University |
| Language | English, English |
| Detected Language | English |
| Type | Text, text, doctoral thesis |
| Format | 1 online resource (95 pages), computer, application/pdf |
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