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This short paper demonstrates how a covariance matrix estimated using log returns of multiple assets in their respective base currencies can be converted directly into a covariance matrix in a single ...

The COV= option must be specified to compute an approximate covariance matrix for the parameter estimates under asymptotic theory for least-squares, maximum-likelihood, or Bayesian estimation, with or ...

Procedures for calculating the additive genetic variance-covariance matrix and its inverse are adapted to accommodate the occurrence of mutations in the genome. The inverse matrix can be used in mixed ...

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

There is a growing need for the ability to specify and generate correlated random variables as primitive inputs to stochastic models. Motivated by this need, several authors have explored the ...

Covariance matrix forecasts of financial asset returns are an important component of current practice in financial risk management. A wide variety of models are available for generating such forecasts ...

Covariance is a statistical measure of how two assets move in relation to each other. It provides diversification and reduces the overall volatility of a portfolio. A positive covariance indicates ...

The estimated covariance matrix of the parameter estimates is computed as the inverse Hessian matrix, and for unconstrained problems it should be positive definite. If the final parameter estimates ...