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      學術報告

      學術報告

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      報告時間 2023年6月9日(周五)下午2:30-5:30 報告地點 騰訊會議ID:758-171-592
      報告人 吳中明

      報告題目:Distributionally robust optimization with Wasserstein metric for multi-period portfolio selection under uncertainty

      報告人:吳中明 副教授 南京信息工程大學

      邀請人:趙志華

      報告時間:2023年6月9日(周五)下午2:30-5:30

      騰訊會議ID:758-171-592

      報告人簡介吳中明,南京信息工程大學副教授,香港中文大學博士后。主持國家自科青年基金項目和中國博士后面上資助項目,入選江蘇省“雙創博士”和人社部“香江學者”。擔任中國運籌學會宣委會執行委員、中國運籌學會數學規劃分會青年理事、江蘇省運籌學會副秘書長、理事。研究方向為最優化理論、算法及其應用,在Computational Optimization and Applications、Journal of Global Optimization、Journal of Optimization Theory and Applications、Mathematics of Computation、 IEEE Transactions on Signal Processing、IEEE Transactions on Instrumentation and Measurement、系統工程理論與實踐等期刊發表論文二十余篇。

      報告摘要:The mean-variance model formulated by Markowitz for a single period serves as a fundamental method of modern portfolio selection. In this study, we consider a multi-period case with uncertainty that better matches the reality of the financial market. Using the Wasserstein metric to characterize the uncertainty of returns in each period, a new distributionally robust mean-variance model is proposed to solve multi-period portfolio selection problem. We further transform the developed model into a tractable convex problem using duality theory. We also apply a nonparametric bootstrap method and provide a specific algorithm to estimate the radius of the Wasserstein ball. The effects of the parameters on the corresponding strategy and evaluation criteria of portfolios are analyzed using in-sample data. The analysis indicate that the return and risk of our portfolio selections are relatively immune to parameter values. Finally, a series of out-of-sample experiments demonstrate that the proposed model is superior to some other models in terms of final wealth, standard deviation, and Sharpe ratio.

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