.png){kind=icon}
Return
High dimensional covariance matrix estimation using a factor model
.png){kind=icon}
OA
AI
DOI：10.1016/j.jeconom.2008.09.017.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
High dimensionality comparable to sample size is common in many statistical problems. We examine covariance matrix estimation in the asymptotic framework that the dimensionality p tends to infinity as the sample size it increases. Motivated by the Arbitrage Pricing Theory in finance, a multi-factor model is employed to reduce dimensionality and to estimate the covariance Matrix. The factors are observable and the number of factors K is allowed to grow With P. We investigate the impact of p and K on the performance of the model-based covariance matrix estimator. Under mild assumptions, we have established convergence rates and asymptotic normality of the model-based estimator. Its performance is compared with that of the sample covariance matrix. We identify situations under which the factor approach increases performance substantially or marginally. The impacts of covariance matrix estimation on optimal portfolio allocation and portfolio risk assessment are Studied. The asymptotic results are supported by a through stimulation study. (C) 2008 Elsevier B.V. All rights reserved.
Keywords:
Factor model
Diverging dimensionality
Covariance matrix estimation
Asymptotic properties
Portfolio management
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
4
Papers:
5.3K
Citations:
3.0W
Organization
Cited Papers
ShareSaveIncidence of Hepatitis C Infection among Prisoners by Routine Laboratory Values during a 20-Year Period.png)
PLoS ONEOAAI
PLoS ONEIF0
OAAIShareSaveShareSaveShareSaveShareSaveShareSaveShareSaveShareSave
View more