千亿手机app下载

Asset allocation for a DC pension plan with learning about stock return predictability

发布时间:2019-12-19 浏览次数:171

报告人:张玲 副教授,广东金融学院

时  间2019.12.27(周五) 10:00-11:00

地  点:金融工程研究中心105

摘  要We explore an optimal investment problem for a defined contribution (DC) pension plan member who can learning about the expected excess return of stock. The expected excess return of stock can be predicted both by an observable predictor and an unobservable predictor, and the member has to estimate the unobservable predicator by learning the history information. Moreover, the inflation risk and the stochastic salary are also considered in our model. By using the filtering technique and the stochastic optimal control, we derive the closed-form optimal investment strategy and the corresponding optimal value function for the DC pension plan with inflation and learning about stock return predictability. According to the theoretical and numerical results, we find that the hedging components in the optimal allocation on the inflation-indexed bond, which are used to hedge the risk arising from the stock return predictor, are more important than the corresponding hedging components on the stock. The correlations between the stock return predictors and the inflation-indexed bond have significant impact on the optimal investment strategy for DC pension plan. In addition, considerable welfare improvements may result from using the inflation-indexed bond either in learning about or hedging against the stock return predictors.

报告人简介:张玲,广东金融学院保险学院副教授,研究方向为:金融工程、风险管理、养老金融。在《InsuranceMathematics and Economics》、《Journal of Computational and Applied Mathematics》、《North American Journal of Economics and Finance》、《Applied stochastic models in business and industry》、《运筹与管理》等发表学术论文20余篇,主持国家级、省部级研究项目8项,参与国家自然科学基金创新群体项目、杰出青年基金项目、面上项目等若干。广东省高等学校“千百十工程”培养对象,广东省人文社会科学重点研究基地“广东区域金融政策研究中心”副主任,广东省人文社会科学重点研究基地“中山大学金融工程与风险管理研究中心”兼职研究员,中国管理现代化研究会管理与决策科学专业委员会理事,中国管理科学与工程学会金融计量与风险管理研究会理事,广东省运筹学会理事。


 
app下载十梓街1号
版权所有 Copyright © 2012 千亿手机app下载金融工程研究中心. 访问次数: