讲座题目 | Cross-market volatility forecasting with attention-based spatial-temporal graph convolutional networks | ||
主讲人 （单位） | 王纲金（湖南大学） | 主持人 （单位） | 李守伟（国产探花 ） |
讲座时间 | 11月1日10:00 | 讲座地点 | 经管楼科研中心 |
主讲人简介 |
王纲金，管理学博士，现任湖南大学工商管理学院教授、博士生导师、副院长。入选国家高层次青年人才计划、湖南省“优青”、湘江青年社科人才、“湖湘青年英才”支持计划、爱思唯尔“中国高被引学者”(管理科学与工程)、全球前2%顶尖科学家榜单。主要从事金融科技与金融工程、金融风险管理、复杂金融网络、系统性金融风险的教学与科研工作，任“产业数智金融”湖南省哲学社会科学重点实验室副主任，主持国家社科基金重大项目子课题2项、国家自然科学基金项目3项、省部级项目3项。以第一或通讯作者在《管理科学学报》等国内外权威期刊发表论文60余篇，出版专著2部，获国家发明专利授权2项。荣获教育部第九届高等学校科学研究优秀成果奖（人文社会科学）三等奖、2021年度湖南省自然科学奖二等奖、2024年全球风险管理专业人士协会(GARP)卓越研究奖等奖项。 | ||
讲座内容摘要 | We propose a cross-market volatility forecasting framework by applying attention-based spatial-temporal graph convolutional network model (ASTGCN) to forecast future volatility of stock indices in 18 financial markets. In our work, we construct cross-market volatility networks to integrate interrelations among financial markets and the corresponding features of each market. ASTGCN combines the spatial-temporal attention mechanisms with the spatial-temporal convolutions to simultaneously capture the dynamic spatial-temporal characteristics of global volatility data. Compared with competitive models, ASTGCN exhibits superiority in multivariate predictive accuracies under multiple forecasting horizons. Our proposed framework demonstrates outstanding stability through several robustness checks. We also inspect the training process of ASTGCN by extracting spatial attention matrices and find that interrelations among global financial markets perform differently in tranquil and turmoil periods. Our study levitates empirical findings in financial networks to practical application with a novel forecasting method in the deep learning community. | ||
