We introduce a blocking and regularization approach to estimate high-dimensional
covariances using highfrequency data. Assets are first grouped according to liquidity. Using the …

This paper addresses the debate about the usefulness of high‐frequency (HF) data in large‐scale
portfolio allocation. We construct global minimum variance portfolios based on the …

Realized covariance estimation for large dimension problems is little explored and poses
challenges in terms of computational burden and estimation error. In a global minimum …

This paper addresses the open debate about the effectiveness and practical relevance of
high-frequency (HF) data in portfolio allocation. Our results demonstrate that when used with …

Mean-variance portfolio optimization requires both invertible and well-conditioned covariance
matrices. This paper compares the performance of covariance conditioning techniques …

We model the dynamics of large-dimensional covariances on the basis of a multi-scale
spectral decomposition of a realized covariance. Volatilities, correlation eigenvalues and …

Assessing the economic value of increasingly precise covariance estimates is of great interest
in finance. We present a realized tick-time covariance estimator that incorporates cross-…

In the midst of the global financial downturn, one area of private equity is growing. The private
equity secondary market—ie, buying and selling existing private investor commitments in …

… Lada Kyj, who received her doctorate in statistics from Rice University, holds undergraduate
degrees in economics and statistics as well as a master's in statistics from the same …

… Lada M. Kyj …