The Analysis of Stochastic Volatility in High-Frequency Financial Data

• Main Authors: Tsai, Min-Ju, 蔡旻儒
• Other Authors: Hung, Hui-Nien
• Format: Others
• Language: en_US
• Published: 2016
• Online Access: http://ndltd.ncl.edu.tw/handle/91629146301434611755

碩士 === 國立交通大學 === 統計學研究所 === 104 === The thesis focus on the analysis of high-frequency financial data. There are lots of estimation methods in univariate asset such as two-time scale realized volatility (TSRV) which we would introduced here. In multiple assets, we present the Wishart-Autoregressive process which can be used to model the dynamic structure of volatility matrices in financial application, with the closed-form prediction and model flexibility, it is alternative to GARCH or stochastic Models. Another method is combination approaches which can simply compute the estimations or predictions without losing any information of available data. Finally, we compare the estimations for intraday returns in stochastic volatility.