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High dimensional covariance matrix estimation using a factor model. (English) Zbl 1429.62185
Summary: 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 \(\infty\) as the sample size \(n\) 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 thorough simulation study.

MSC:
62H12 Estimation in multivariate analysis
62F12 Asymptotic properties of parametric estimators
62H25 Factor analysis and principal components; correspondence analysis
15A18 Eigenvalues, singular values, and eigenvectors
62P05 Applications of statistics to actuarial sciences and financial mathematics
91G10 Portfolio theory
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