Estimation and regularization for covariance matrix of asset returns. For covariance matrix estimation, three major types of factor models are included: macroeconomic factor model, fundamental factor ...

where nused is the number of non-missing observations and np is the number of estimable parameters. The standard error reported for the parameters is the sqrt of the ...

This chapter introduces several recent developments for estimating large covariance and precision matrices without assuming the covariance matrix to be sparse. It explains two methods for covariance ...

Abstract: Accurate Direction-of-Arrival (DoA) estimation is highly dependent on the correctness of the covariance matrix, which reflects the spatial characteristics of the signal. In underwater ...

Alexandre Antonov, Alexander Lipton and Marcos Lopez de Prado compare and contrast two portfolio allocation methods: the classical Markowitz approach and the hierarchical risk parity (HRP) approach.

We propose a nonparametric method for automatically selecting the number of autocovariances to use in computing a heteroskedasticity and autocorrelation consistent covariance matrix. For a given ...

In this paper, we propose a parametric spectral estimation procedure for constructing heteroskedasticity and autocorrelation consistent (HAC) covariance matrices. We establish the consistency of this ...

This repository contains MATLAB, Python, and R code for the paper E. Raninen and E. Ollila, "Coupled Regularized Sample Covariance Matrix Estimator for Multiple Classes," in IEEE Transactions on ...

This is the GEE equivalent of the inverse of the Fisher information matrix that is often used in generalized linear models as an estimator of the covariance estimate of the maximum likelihood ...