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Activity Number:
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485
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Type:
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Invited
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Date/Time:
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Thursday, August 2, 2007 : 8:30 AM to 10:20 AM
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Sponsor:
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IMS
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| Abstract - #307705 |
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Title:
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Large Dimensional Covariance Matrix Estimation for Asset Pricing and Risk Management Using a Factor Model
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Author(s):
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Jianqing Fan*+ and Yingying Fan and Jinchi Lv
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Companies:
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Princeton University and Princeton University and Princeton University
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Address:
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Department of Operations Res and Fin Eng, Princeton, NJ, 08544,
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Keywords:
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Covariance estimation ; Factor Model ; High-dimensionality ; Portifolio Allocation ; Risk Management
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Abstract:
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Large dimensionality comparable to the sample size is a common feature as in modern portfolio allocation and risk management. In this paper we examine the covariance matrix estimation in the asymptotic framework that the dimensionality p grows with sample size. Motivated by financial economics theory, we propose to use a multi-factor model to reduce the dimensionality and to estimate the covariance matrix among those assets. Under some basic assumptions, we have established the rate of convergence and asymptotic normality for the proposed covariance matrix estimator. The performance is compared with the sample covariance matrix. We identify the situations under which the factor approach can gain substantially the performance and the cases where the gains are only marginal.
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