Analysis, Modeling And Application Of Financial Market's High-Frequency/Ultra-High-Frequency Time Series

High-frequency and ultra-high-frequency time series' analysis and modeling is a new research field in financial econometrics. High-frequency time series is referred to financial data which is sampled with interval of one hour, one minute even one second; And ultra-high-frequency time series is tick-by-tick data. The paper studies high-frequency / ultra-high-frequency time series' analysis and modeling. The main work and innovations of the dissertation include: 1,Through empirical research of high-frequency time series of Shanghai Composite Index and Shenzhen Component Index, the paper discovers that high-frequency time series usually has the characteristic of high Skewness and Kurtosis, negative first order autocorrelation, calendar effects. 2,The paper studies calendar effects of Shanghai Stock Market quantitatively with Flexible Fourier Form regression based on high-frequency time series, and find out its volatility is a single "U" shape, which is different from volatility of Japanese Stock Market. Then the chapter tests winter and summer volatility. At last, uses Flexible Fourier Form Regression to fit the calendar effects, and constructs long-memory SV model for Shanghai Composite Index, and find high-frequency time series usually has low volatility persistence. 3,The paper studies realized volatility, then puts forward a more efficient approach which is the adjusted realized volatility. And the paper defines optimal frequency based on microstructure error and measure error. This paper studies the characteristics of the adjusted realized volatility of Shanghai Stock Market, and estimates ARFIMAX model. Then through a variety of criterions the paper studies prediction ability of adjusted realized volatility, GARCH model and SV model. At last, extends realized volatility based on high-frequency data to realized covariance matrix based on multivariate high-frequency data. Then the chapter studies the characteristics of the realized covariance matrix of Shanghai Composite Index and Shenzhen Component Index, and constructs FIVAR model to its long memory characteristic. 4,The paper studies the modeling of ultra-high-frequency time series, then through constructing the ACD model and UHF-GARCH model of index and single stock data in Shanghai Stock Market, the chapter researches its market microstructure. 5,The paper proposes application of Wavelet Neural Network in high-frequency time series calendar effects' study. At last, the paper proves that WNN is better than classical FFF regression. At last, the paper reviews the research of this paper and points out its' research trend. The research is sponsored by National Natural Science Foundation of China: Persistence in Volatility of Multivariate Time Series and Its Applications in Financial System (No. 70171001).