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2015-12-24 10:44:29   来源:   评论:0 点击:

主题:  Semiparametric Dynamic Portfolio Choice with Multiple Conditioning Variables
主讲人:  Prof  Zudi Lu
主持人: 董朝华
时间: 2015年12月25日, 上午10—11点
../../../../custom/2015/1224/20151224104700695.doc
地点:格致楼 918
主办单位: 科研处,经济学院
主讲人简介:Zudi Lu is a Professor/Chair in Statistics, in Mathematical Sciences Academic Unit and Southampton Statistical Sciences Research Institute (S3RI) at University of Southampton, UK. His research interest includes nonlinear financial time series modelling and financial statistics / econometrics; statistical inference & computation for nonlinear spatial/temporal modelling and prediction; applied temporal/spatial modelling for financial, environmental and socioeconomic risks; non-parametric/semi-parametric modelling and statistical learning.
主要内容:
Dynamic portfolio choice has been a central and essential objective for investors in active asset management. In this paper, we study the dynamic portfolio choice with multiple conditioning variables, where the dimension of the conditioning variables can be either fixed or diverging to infinity at certain polynomial rate of the sample size. We propose a novel data-driven method to estimate the optimal portfolio choice, motivated by the model averaging marginal regression approach suggested by Li, Linton and Lu (2015, Journal of Econometrics). More specifically, in order to avoid the curse of dimensionality associated with the multivariate nonparametric regression problem and to make it practically implementable, we first estimate the marginal optimal portfolio choice by maximising the conditional utility function for each univariate conditioning variable, and then construct the joint dynamic optimal portfolio through the weighted average of the marginal optimal portfolio across all the conditioning variables. Under some regularity conditions, we establish the large sample properties for the developed portfolio choice procedure. Both the simulation study and empirical application well demonstrate the finite-sample performance of the proposed methodology.
 
 

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