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From the same journal

A Dynamic Structure for High Dimensional Covariance Matrices and its Application in Portfolio Allocation

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A Dynamic Structure for High Dimensional Covariance Matrices and its Application in Portfolio Allocation. / Guo, Shaojun; Box, John Leigh; Zhang, Wenyang.

In: Journal of the American Statistical Association, Vol. 112, No. 517, 02.01.2017, p. 235-253.

Research output: Contribution to journalArticle

Harvard

Guo, S, Box, JL & Zhang, W 2017, 'A Dynamic Structure for High Dimensional Covariance Matrices and its Application in Portfolio Allocation', Journal of the American Statistical Association, vol. 112, no. 517, pp. 235-253. https://doi.org/10.1080/01621459.2015.1129969

APA

Guo, S., Box, J. L., & Zhang, W. (2017). A Dynamic Structure for High Dimensional Covariance Matrices and its Application in Portfolio Allocation. Journal of the American Statistical Association, 112(517), 235-253. https://doi.org/10.1080/01621459.2015.1129969

Vancouver

Guo S, Box JL, Zhang W. A Dynamic Structure for High Dimensional Covariance Matrices and its Application in Portfolio Allocation. Journal of the American Statistical Association. 2017 Jan 2;112(517):235-253. https://doi.org/10.1080/01621459.2015.1129969

Author

Guo, Shaojun ; Box, John Leigh ; Zhang, Wenyang. / A Dynamic Structure for High Dimensional Covariance Matrices and its Application in Portfolio Allocation. In: Journal of the American Statistical Association. 2017 ; Vol. 112, No. 517. pp. 235-253.

Bibtex - Download

@article{f5b87300c9744be38b321e904661a2ef,
title = "A Dynamic Structure for High Dimensional Covariance Matrices and its Application in Portfolio Allocation",
abstract = "Estimation of high-dimensional covariance matrices is an interesting and important research topic. In this article, we propose a dynamic structure and develop an estimation procedure for high-dimensional covariance matrices. Asymptotic properties are derived to justify the estimation procedure and simulation studies are conducted to demonstrate its performance when the sample size is finite. By exploring a financial application, an empirical study shows that portfolio allocation based on dynamic high-dimensional covariance matrices can significantly outperform the market from 1995 to 2014. Our proposed method also outperforms portfolio allocation based on the sample covariance matrix, the covariance matrix based on factor models, and the shrinkage estimator of covariance matrix. Supplementary materials for this article are available online.",
keywords = "Dynamic structure, Factor models, High-dimensional covariance matrices, Iterative algorithm, Kernel smoothing, Portfolio allocation, Single-index models",
author = "Shaojun Guo and Box, {John Leigh} and Wenyang Zhang",
year = "2017",
month = jan,
day = "2",
doi = "10.1080/01621459.2015.1129969",
language = "English",
volume = "112",
pages = "235--253",
journal = "Journal of the American Statistical Association",
issn = "0162-1459",
publisher = "Taylor and Francis Ltd.",
number = "517",

}

RIS (suitable for import to EndNote) - Download

TY - JOUR

T1 - A Dynamic Structure for High Dimensional Covariance Matrices and its Application in Portfolio Allocation

AU - Guo, Shaojun

AU - Box, John Leigh

AU - Zhang, Wenyang

PY - 2017/1/2

Y1 - 2017/1/2

N2 - Estimation of high-dimensional covariance matrices is an interesting and important research topic. In this article, we propose a dynamic structure and develop an estimation procedure for high-dimensional covariance matrices. Asymptotic properties are derived to justify the estimation procedure and simulation studies are conducted to demonstrate its performance when the sample size is finite. By exploring a financial application, an empirical study shows that portfolio allocation based on dynamic high-dimensional covariance matrices can significantly outperform the market from 1995 to 2014. Our proposed method also outperforms portfolio allocation based on the sample covariance matrix, the covariance matrix based on factor models, and the shrinkage estimator of covariance matrix. Supplementary materials for this article are available online.

AB - Estimation of high-dimensional covariance matrices is an interesting and important research topic. In this article, we propose a dynamic structure and develop an estimation procedure for high-dimensional covariance matrices. Asymptotic properties are derived to justify the estimation procedure and simulation studies are conducted to demonstrate its performance when the sample size is finite. By exploring a financial application, an empirical study shows that portfolio allocation based on dynamic high-dimensional covariance matrices can significantly outperform the market from 1995 to 2014. Our proposed method also outperforms portfolio allocation based on the sample covariance matrix, the covariance matrix based on factor models, and the shrinkage estimator of covariance matrix. Supplementary materials for this article are available online.

KW - Dynamic structure

KW - Factor models

KW - High-dimensional covariance matrices

KW - Iterative algorithm

KW - Kernel smoothing

KW - Portfolio allocation

KW - Single-index models

UR - http://www.scopus.com/inward/record.url?scp=85019019298&partnerID=8YFLogxK

U2 - 10.1080/01621459.2015.1129969

DO - 10.1080/01621459.2015.1129969

M3 - Article

VL - 112

SP - 235

EP - 253

JO - Journal of the American Statistical Association

JF - Journal of the American Statistical Association

SN - 0162-1459

IS - 517

ER -