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Portfolio Diversification and Model Uncertainty: A Robust Dynamic Mean-Variance Approach
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报告题目: Portfolio Diversification and Model Uncertainty: A Robust Dynamic Mean-Variance Approach

报告人: 周超 研究员 新加坡国立大学量化金融中心

照片:

邀请人: 薄立军

报告时间: 2020年7月7日(周二);上午10:30-12:00

报告地点:腾讯会议ID:309 133 940 密码: 820806

点此链接直接入会:

https://meeting.tencent.com/s/4pUq2ayL1QzW

报告人简介:周超博士,2012年获得法国巴黎综合理工大学博士学位,拥有巴黎综合理工大学工程师文凭,现为新加坡国立大学量化金融中心研究员。他的主要研究方向为量化金融、金融数学与随机控制,在Mathematical Finance, SIAM Journal on Control and Optimization, SIAM Journal on Financial Mathematics, Journal of Economic Dynamics and Control, Annals of Applied Probability, Annals of Probability等顶级学术期刊(SCI/SSCI)发表论文二十余篇,主持国家自然科学基金面上项目(中国)、教育部科学基金(新加坡)等科研项目多项。个人主页:https://matzc.github.io/

报告摘要: This talk is concerned with multi-asset mean-variance portfolio selection problem under model uncertainty. We develop a continuous time framework for taking into account ambiguity aversion about both expected rate of return and correlation matrix of stocks, and for studying the effects on portfolio diversification. We prove a separation principle for the associated robust control problem formulated as a mean-field type differential game, which allows to reduce the determination of the optimal dynamic strategy to the parametric computation of the minimal risk premium function. Our results provide a justification for under-diversification, as documented in empirical studies, and that we explicitly quantify in terms of correlation and Sharpe ratio ambiguity parameters. In particular, we show that an investor with a poor confidence in the expected return estimation does not hold any risky asset, and on the other hand, trades only one risky asset when the level of ambiguity on correlation matrix is large. This extends to the continuous-time setting the results obtained by Garlappi, Uppal and Wang (2007), and Liu and Zeng (2017) in a one-period model. Based on joint work with Huyên Pham (Paris Diderot) and Xiaoli Wei (UC Berkeley).

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