学术报告报告主题1：Research on the portfolio model based on Mean-MF-DCCA under multifractal feature constraint
报告人1：李佳博士（广州大学）
报告时间：2022年9月19日（星期一）上午10：00-10：40
报告主题2：Scenario Tree Reduction Algorithm in Complex Situations and Its Application in Portfolio Problems
报告人2：何小雷 博士（华南理工大学）
报告时间：2022年9月19日（星期一）上午11：00-11：40
报告地点：四号楼4318
邀请人：邓雪教授
欢迎广大师生前往。
数学学院
2022年9月15日
报告摘要1：In order to incorporate the multifractal characteristics of the capital market into the research framework of portfolios and overcome the shortcomings of existing research results that had not considered the existence of multiple fractal fluctuation characteristics and multiple fractal correlation characteristics of asset prices, in this paper, multifractal detrended cross-correlation analysis (MF-DCCA) was embedded into the mean–variance criterion, the Mean-MF-DCCA portfolio model was constructed under the constraints of multiple fractal features, and the analytical solution of the model was given. On this basis, the effectiveness of Mean-MF-DCCA portfolio model was tested using empirical analysis. The results showed that the Mean-MF-DCCA portfolio model was effective, and compared with the mean–variance portfolio model, it is more conducive to investors to construct a sophisticated portfolio under the multiple fluctuation importance degree and multiple time scales, so as to improve their portfolio performance.
报告摘要2：To effectively solve stochastic programming problems, the scenario reduction method has become an active research area to strike a balance between the fine representation of random variables and computational complexity. At present, although there are many researches on scenario reduction methods at home and abroad, there is still a lack of related research on how to get the most accurate investment decision with the smallest scenario set. Especially, some recent studies have shown that the optimal investment decision is closely related to the distribution characteristics of asset returns. Moreover, the downside risk measurement is the most commonly used method to measure the investment risk at present, and its remarkable feature is that it is only related to the tail of the return distribution. Based on this, this paper takes the portfolio problem as the main background, and makes a deeper theoretical and empirical research on scenario reduction method.
报告人1介绍：李佳，2017年华南理工大学工商管理学院博士毕业。现为广州大学经济与统计学院金融系讲师。主要研究领域为投资组合与风险管理。主持教育部人文社科青年项目一项，参与国家自然科学基金、中央高校科研项目、广东省社科等多项，在《Journal of Computational and Applied Mathematics》、《North American Journal of Economics and Finance》、《Journal of Intelligent and Fuzzy Systems》、《运筹与管理》等期刊发表论文多篇。
报告人2介绍：何小雷，2018级年华南理工大学工商管理学院博士。