Campuses:

Marcenko-Pastur law

Thursday, February 22, 2018 - 2:10pm - 2:50pm
Alexander Aue (University of California, Davis)
One of the effects of large dimensionality of data is that even for many low-dimensional functions of the underlying population parameters, such as the mean vector and the covariance matrix, the corresponding sample counterparts are highly biased estimators. In this talk, one such problem is addressed, within the context of determining the mean-variance frontier in financial portfolio optimization. This is done in the time series setting when the vector of returns exhibits temporal dependence.
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