B0663
Title: Spectral analysis of high-dimensional time series with applications to the mean-variance frontier
Authors: Alexander Aue - UC Davis (United States) [presenting]
Haoyang Liu - UC Berkeley (United States)
Debashis Paul - University of California, Davis (United States)
Abstract: The limiting spectral behavior of the covariance and symmetrized autocovariance matrices of a class of high-dimensional linear time series is discussed, where the asymptotic regime is such that dimensionality and sample size grow proportionally. The results extend the classical Marcenko-Pastur law to the time series case. The form of the limiting spectral distribution is exploited to estimate the mean-variance frontier, an important measure of the minimum risk required for a fixed expected return in a portfolio of financial assets. The results may help alleviate the risk underestimation of the mean-variance frontier well documented in the finance and econometrics literature.
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