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 ...

Covariance Matrix 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 ...

The major outputs you need to be concerned about for simple linear regression are the R-squared, the intercept (constant) and the GDP's beta (b) coefficient. The R-squared number in this example ...

For example, suppose a risk manager wants to calculate the value at risk using the parametric method for a one-day time horizon. The weight of the first asset is 40%, and the weight of the second ...

More subtle, but significant changes of correlation can also be observed between single stocks and/or between sectors in the stock market. For example, a downward move of the S&P 500 leads to an ...

Within a VaR framework, the relative performance of covariance matrix forecasts depends greatly on the VaR models' distributional assumptions. Of the forecasts examined, simple specifications, such as ...

Covariance Matrix 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 ...