On June 27, 2026, the "2026 Financial Statistics and Risk Management Seminar" hosted by the School of Statistics and Data Science and the Big Data Research Institute of Shanghai University of Finance and Economics was successfully held in Conference Room 114 of the School of Statistics and Data Science. The theme of this seminar is "Prediction and Decision making in Complex Data Environments", aiming to strengthen academic exchanges in the fields of financial statistics and risk management, and promote the cross integration of financial econometrics, risk management, big data science, artificial intelligence, and financial practice.
At the beginning of the conference, Feng Xingdong, Dean of the School of Statistics and Data Science at Shanghai University of Finance and Economics, delivered a welcome speech, warmly welcoming the arrival of experts, scholars, and industry guests, and expressing sincere gratitude to colleagues from all walks of life who have long cared for and supported the development of the college. He pointed out that financial statistics and risk management are at a critical stage of rapid changes in data forms, algorithm tools, and financial scenarios. He hopes that this seminar can provide a platform for in-depth communication and consensus building between academia and industry.
The academic report in the morning shared rich content and diverse perspectives with the industry. Professor Chen Zhiping from Xi'an Jiaotong University introduced the latest research on generalized tail risk measurement based on optimal confidence level selection; Professor Hu Zhaolin from Tongji University focused on mean variance portfolio selection and discussed statistical properties and distribution robust optimization methods; Dr. Chai Lei, CEO of MoShu Zhiqing, shared his exploration on the integration of business needs and artificial intelligence technology in the context of financial business AI application practice; Wang Li, the manager of Shanghai Steel Union Aluminum Business Unit, analyzed the operational logic and future trends of the aluminum industry chain based on industry data and fundamental research.
Subsequently, the attending experts held a roundtable discussion on the transformation of research, teaching, and financial decision-making in the AI era. The guests conducted in-depth discussions on the impact of artificial intelligence on academic research paradigms, classroom teaching models, financial data analysis, and industry decision-making processes, based on their own research directions and practical experience. The atmosphere of the on-site discussions was lively, and the attending teachers and students benefited greatly.
The afternoon report continued to focus on financial statistics, risk management, and data-driven decision-making. Professor Liu Zhi from the University of Macau introduced non parametric testing methods for diurnal variations in intraday related processes; Professor Li Xun from the Hong Kong Polytechnic University reported on an optimization framework for solving forward backward stochastic differential equations; Professor Yang Nian from Nanjing University shared a general and fast framework for pricing options under three-dimensional models; An Fuhang, the manager of the Data Business Department of Shanghai Steel Union, introduced the application scenarios of industrial data in the financial industry from the perspective of macro commodity data.
Subsequently, young teachers Liu Qiang, Yu Gen, and Yang Zixin from the School of Statistics and Data Science at Shanghai University of Finance and Economics gave presentations on topics such as leverage effects and volatility estimation in high-frequency financial data, data-driven order assembly decision-making problems, and non parametric network vector autoregression models. They showcased the latest research progress of young teachers in financial statistics, data-driven optimization, and network econometric modeling.
This seminar has a distinct theme and rich content, covering cutting-edge theoretical issues in financial statistics and risk management, as well as practical applications in artificial intelligence, industrial data, and financial practice. The conference further promoted in-depth exchanges between experts and scholars in related fields, industry representatives, and faculty and students of the college, which has positive significance for promoting research on financial forecasting, risk management, and intelligent decision-making in complex data environments.
