学术报告：Optimal Conditional Mean-Variance Portfolio Averaging

报告时间：2023年11月29日（星期三）下午14:30-15:30

报告地点：沙河校区，二教205

报告人：张新雨，中科院数学与系统科学研究院，研究员

报告摘要：In this article, we develop a novel portfolio averaging strategy under the conditional mean-variance framework for achieving a desired risk-return trade-off. A series of shrunken candidate portfolios are constructed, which differ from each other in candidate models, target portfolios, weighting matrices and/or penalty parameters. We adopt a modified Mallows-type criterion to determine the weights across these candidate portfolios. Theoretically, we establish the asymptotic optimality of the proposed strategy in the sense of achieving the lowest possible out-of-sample expected utility loss, and also derive the convergence of weights arising from this criterion. Empirically, we illustrate that the proposed strategy compares favorably with 15 alternative strategies across five datasets.

报告人简介：张新雨，中科院数学与系统科学研究院研究员。主要从事统计学和计量经济学的理论和应用研究工作，具体研究方向包括模型平均、管理统计、机器学习和经济预测等，担任SCI期刊《Journal of Systems Science & Complexity (JSSC)》领域主编和其他5个国内外重要期刊的编委，是管理科学与工程学会常务理事，曾先后获得青托、优青、杰青等项目支持。

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