M
Michael Wolf
Researcher at University of Zurich
Publications - 178
Citations - 15836
Michael Wolf is an academic researcher from University of Zurich. The author has contributed to research in topics: Covariance matrix & Estimator. The author has an hindex of 42, co-authored 160 publications receiving 13822 citations. Previous affiliations of Michael Wolf include University Hospital Bonn & University of California, San Diego.
Papers
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Journal ArticleDOI
A well-conditioned estimator for large-dimensional covariance matrices
Olivier Ledoit,Michael Wolf +1 more
TL;DR: This paper introduces an estimator that is both well-conditioned and more accurate than the sample covariance matrix asymptotically, that is distribution-free and has a simple explicit formula that is easy to compute and interpret.
Journal ArticleDOI
Improved Estimation of the Covariance Matrix of Stock Returns With an Application to Portfolio Selection
TL;DR: In this paper, the covariance matrix of stock returns is estimated by an optimally weighted average of two existing estimators: the sample covariance and single-index covariance matrices.
Proceedings ArticleDOI
A data locality optimizing algorithm
Michael Wolf,Monica S. Lam +1 more
TL;DR: An algorithm that improves the locality of a loop nest by transforming the code via interchange, reversal, skewing and tiling is proposed, and is successful in optimizing codes such as matrix multiplication, successive over-relaxation, LU decomposition without pivoting, and Givens QR factorization.
Journal ArticleDOI
Honey, I shrunk the sample covariance matrix
Olivier Ledoit,Michael Wolf +1 more
TL;DR: Shrinkage as mentioned in this paper is a matrix obtained from the sample covariance matrix through a transformation called shrinkage, which pulls the most extreme coefficients toward more central values, systematically reducing estimation error when it matters most.
Journal ArticleDOI
A loop transformation theory and an algorithm to maximize parallelism
Michael Wolf,Monica S. Lam +1 more
TL;DR: The loop transformation theory is applied to the problem of maximizing the degree of coarse- or fine-grain parallelism in a loop nest and it is shown that the maximum degree of parallelism can be achieved by transforming the loops into a nest of coarsest fullypermutable loop nests and wavefronting the fully permutable nests.