报告时间：10月23日13:30-14:30
报告地点：中北校区理科大楼A1716
报告题目：Realized GARCH-Ito Model：Volatility Analysis with Combined Low-frequency, High-frequency andOption Data
报告人：宋馨雨 Assistant Professor（Shanghai University of Finance and Economics）
Bio:
Dr. Song is an assistant Professor at Shanghai University of Finance and Economics. She received her PhD degree from University of Wisconsin-Madison and her research interests include volatility matrix estimation and prediction, dimension reduction of large volatility matrix, and statistical analysis of quantum annealing.
Abstract:
In modern financial markets, volatility measures the degree of dispersion for a trading asset and plays an important role in portfolio allocation, performance evaluation, and risk management. Traditional approach adopted to model the dynamic evolution of volatility process considers parametric models such as the GARCH model and uses low-frequency data, data at daily or longer time horizons. Modern approach investigates volatility process with high-frequency data that refer to intra-daily observations. Various methodologies have been developed in literature for volatility estimation with high-frequency data, well-known estimators include the multi-scale realized volatility (MSRV) estimator, pre-averaging realized volatility (PRV) estimator and kernel realized volatility (KRV) estimator. We believe that the interconnection between data gathered at the two different time scales should be emphasized. In this talk, we introduce unified models for modeling high-frequency financial data that can accommodate both the continuous-time Ito diffusion and the discrete-time realized GARCH model by embedding the discrete realized GARCH model structure in the continuous instantaneous volatility process. The key feature of the proposed model is that its conditional daily integrated volatility retains an autoregressive structure. Given the autoregressive structure, we propose a quasi-likelihood function for parameter estimation and establish its asymptotic properties. To improve the parameter estimation, we propose a joint quasi-likelihood function that is built on the marriage of daily integrated volatility estimated by high-frequency data and daily implied volatility obtained from option data. We demonstrate the advantage of proposed methodology with real Bank of America stock and option data.