报告内容简介：

Widely used volatility forecasting methods are usually based on low frequency time series models. Although some of them employ high frequency observations, these intraday data are often summarized into a point low frequency statistic, e.g., a daily realized measure, before being incorporated into a forecasting model. This paper contributes to the volatility forecasting literature by instead predicting the next-period intraday volatility curve via a functional time series forecasting approach. In contrast with non-functional methods, the proposed functional approach fully exploits the rich intraday information and hence leads to more accurate volatility forecasts. This is further confirmed by extensive comparisons between the proposed functional method and those widely used non-functional methods in out-of-sample volatility forecasting for a number of stocks and equity indices from the Chinese market.

主讲人简介：

汤银芬，上海立信会计金融学院统计与数学学院讲师，2018年毕业于上海财经大学，获得统计学博士学位。研究方向包括应用概率和统计，金融统计与风险管理，高频金融数据统计推断等。研究成果发表在《Applied Stochastic Models in Business and Industry》、 《Journal of Statistical Planning and Inference》、《Metrika》等统计学期刊。