信息科學技術學院数学系學術講座(四十八)

發布單位:成果專利綜合科 [2019-09-19 16:47:09] 打印此信息

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

內容簡介: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).

報告人:新加坡國立大學周超副教授

報告人簡介:本科畢業于法國著名的巴黎九大,博士畢業于巴黎綜合理工大學。現任職于新加坡國立大學和新加坡國立大學蘇州研究院,參與新加坡國立大學金融碩士在我國華南區域的招生工作。目前其主要研究方向爲金融數學、隨機控制,倒向隨機微分方程。他在這些方向獲得一些很好的結果,其中的一部分發表在多個國際權威的數學、金融雜志上,如:Mathematical FinanceThe Annals of Applied Probability等。

時間:2019920日(周五)下午300

地點:南海樓330

 

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