In theories related to probability, researchers are happy to describe the world with succinct distributions such as Gaussian distribution. However, the heavy tail phenomenon is common in reality. This talk will focus on heavy tails in financial markets and their modeling and implications. In statistics, researchers develop models for heavy tails and use data to infer the parameters. The usage of data for fitting models is the primary goal in statistics or machine learning. However, in natural sciences such as physics, how to deal with the heavy tails more theoretically is still an open question, and open-minded discussions are welcome.

严兴，中国人民大学统计与大数据研究院助理教授。2019年获得香港中文大学系统工程与工程管理学系博士学位，2015年获得中国科学院计算技术研究所计算机科学硕士学位，2012年获得南开大学基础数学学士学位。研究方向为金融机器学习、金融科技、风险管理、资产定价、量化投资、衍生品等。研究成果发表在NeurIPS、AAAI、JEDC等学术会议或期刊。