This paper proposes a novel shrinkage estimator for high-dimensional covariance matrices by extending the Oracle Approximating Shrinkage (OAS) of Chen et al. (2009) to target the diagonal elements of ...

Graphical models provide a robust framework for representing the conditional independence structure between variables through networks, enabling nuanced insight into complex high-dimensional data.

We consider a p-dimensional time series where the dimension p increases with the sample size n. The resulting data matrix X follows a stochastic volatility model: each entry consists of a positive ...

The estimation of portfolio value-at-risk (VaR) requires a good estimate of the covariance matrix. As it is well known that a sample covariance matrix based on some historical rolling window is noisy ...

The Canadian Journal of Statistics / La Revue Canadienne de Statistique Necessary and sufficient conditions are given for the covariance structure of all the observations in a multivariate factorial ...