题  目：Portfolio diversification and model uncertainty: a robust dynamic mean-variance approach

内容简介：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 (Paris Diderot).

报告人：新加坡国立大学  周超  副教授

报告人简介：本科毕业于法国著名的巴黎九大，博士毕业于巴黎综合理工大学。现任职于新加坡国立大学和新加坡国立大学苏州研究院，参与新加坡国立大学金融硕士在我国华南区域的招生工作。目前其主要研究方向为金融数学、随机控制，倒向随机微分方程。他在这些方向获得一些很好的结果，其中的一部分发表在多个国际权威的数学、金融杂志上，如：Mathematical Finance、 The Annals of Applied Probability等。

时  间：2019年9月20日（周五）下午3：00始

地  点：南海楼330室

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信息科学技术学院

2019年9月19日