This review covers recent results concerning the estimation of large covariance matrices
using tools from Random Matrix Theory (RMT). We introduce several RMT methods and …

We develop a recursive L 1-regularized least squares (SPARLS) algorithm for the estimation
of a sparse tap-weight vector in the adaptive filtering setting. The SPARLS algorithm exploits …

In many practical applications, correlation matrices might be affected by the “curse of
dimensionality” and by an excessive sensitiveness to outliers and remote observations …

In portfolio selection, there is often the need for procedures to generate “realistic” covariance
matrices for security returns, for example to test and benchmark optimization algorithms. For …

This paper deals with the construction of the covariance matrix for portfolio optimization. We
show that in terms of the ex-post standard deviation of the global minimum variance …

The literature provides strong evidence that stock price values can be predicted from past
price data. Principal component analysis (PCA) identifies a small number of principle …

Greedy algorithms form an essential tool for compressed sensing. However, their inherent
batch mode discourages their use in time-varying environments due to significant complexity …

We present a framework for modeling asset and portfolio dynamics, incorporating this
information into portfolio optimization. We define drivers for asset and portfolio dynamics and …

Random matrix theory (RMT) filters, applied to covariance matrices of financial returns, have
recently been shown to offer improvements to the optimisation of stock portfolios. This paper …

This paper introduces a Modified Hierarchical Risk Parity (" MHRP") approach that extends
the HRP approach by incorporating three intuitive elements commonly used by practitioners …