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Semiparametric dynamic portfolio choice with multiple conditioning variables

Semiparametric dynamic portfolio choice with multiple conditioning variables
Semiparametric dynamic portfolio choice with multiple conditioning variables
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 et al. (2015). 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 maximizing 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.
0304-4076
309-318
Chen, Jia
3b32661d-16b8-46ed-9fee-8cbacd390119
Li, Degui
e341f702-23cd-4c1a-91a8-3b7aa3dfda15
Linton, Oliver
36fa04bf-d781-45bd-bd7f-c28f68e5df6e
Lu, Zudi
4aa7d988-ac2b-4150-a586-ca92b8adda95
Chen, Jia
3b32661d-16b8-46ed-9fee-8cbacd390119
Li, Degui
e341f702-23cd-4c1a-91a8-3b7aa3dfda15
Linton, Oliver
36fa04bf-d781-45bd-bd7f-c28f68e5df6e
Lu, Zudi
4aa7d988-ac2b-4150-a586-ca92b8adda95

Chen, Jia, Li, Degui, Linton, Oliver and Lu, Zudi (2016) Semiparametric dynamic portfolio choice with multiple conditioning variables. [in special issue: Financial Statistics and Risk Management] Journal of Econometrics, 194 (2), 309-318. (doi:10.1016/j.jeconom.2016.05.009).

Record type: Article

Abstract

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 et al. (2015). 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 maximizing 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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Accepted/In Press date: 1 February 2016
e-pub ahead of print date: 1 June 2016
Published date: October 2016
Organisations: Statistics

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Local EPrints ID: 390375
URI: https://eprints.soton.ac.uk/id/eprint/390375
ISSN: 0304-4076
PURE UUID: 6dcd8830-0fe1-4404-b126-687691626cc6

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Date deposited: 31 Mar 2016 15:22
Last modified: 03 May 2018 04:01

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