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Honey, I Shrunk the Sample Covariance Matrix
TLDR
It is shown on actual stock market data that shrinkage reduces tracking error relative to a benchmark index, and substantially increases the realized information ratio of the active portfolio manager.
Gain, Loss, and Asset Pricing
We develop an approach to asset pricing in incomplete markets that bridges the gap between the two fundamental approaches in finance: model‐based pricing and pricing by no arbitrage. We strengthen
Some hypothesis tests for the covariance matrix when the dimension is large compared to the sample size
This paper analyzes whether standard covariance matrix tests work when dimensionality is large, and in particular larger than sample size. In the latter case, the singularity of the sample covariance
Crashes at Critical Points
We study a rational expectation model of bubbles and crashes. The model has two components: (1) our key assumption is that a crash may be caused by local self-reinforcing imitation between noise
Nonlinear shrinkage estimation of large-dimensional covariance matrices
Many statistical applications require an estimate of a covariance matrix and/or its inverse. When the matrix dimension is large compared to the sample size, which happens frequently, the sample
Nonlinear Shrinkage of the Covariance Matrix for Portfolio Selection: Markowitz Meets Goldilocks
Markowitz (1952) portfolio selection requires an estimator of the covariance matrix of returns. To address this problem, we promote a nonlinear shrinkage estimator that is more flexible than previous
Flexible Multivariate GARCH Modeling with an Application to International Stock Markets
This paper offers a new approach to estimating time-varying covariance matrices in the framework of the diagonal-vech version of the multivariate GARCH(1,1) model. Our method is numerically feasible
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