报告人：崔振嵛 副教授 Stevens Institute of Technology
报告时间：2024年7月4日下午15:30-16:30
报告地点：红瓦楼726
报告摘要： In this paper, we propose an almost model-free dynamic mean quadratic variation (MQV) asset allocation analysis for log returns, which we termed as log-MQV. It has several advantages such as time-consistent optimal investment decision, conforming to investment wisdom, and the explicit closed-form optimal investment strategies for most stochastic models in finance under both complete and incomplete market settings. Through a unified framework, the proposed model can incorporate Ito diffusion models, jump risks, regime switching, and stochastic volatility features. We also illustrate that the proposed framework allows for a data-driven implementation utilizing historical time series data, and this paves the path for a fully model-free robo-advising investment strategy. Extensive numerical and empirical experiments illustrate the performance of the proposed optimal log-MQV portfolio as compared to the log-MV portfolio.
报告人简介：崔振嵛，理学博士，Stevens Institute of Technology 商学院副教授，博士生导师，博士毕业于University of Waterloo，现任International Journal of Finance and Economics 副主编。主要研究兴趣有金融工程，随机模拟，及金融科技，在 Mathematical Finance, SIAM Journal on Financial Mathematics, INFORMS Journal on Computing, Econometric Theory, Journal of Financial Econometrics, European Journal of Operational Research 等杂志发表数十篇论文。目前主持 NSF CNS-2113906: “Fast Quantum Method for Financial Risk Measurement” 科研项目。
报告邀请人：马俊美 副教授