Practitioners often have to compute a correlation matrix for financial applications as in value-at-risk, asset … allocation, and option pricing, etc. The computed correlation matrix, however, could be a non-positive semidefinite matrix due to … invalid correlation matrix to be fixed. Algorithm 1 keeps the top left submatrix unchanged, whereas Algorithm 2, under a …

ensemble of random matrices that models the truly existing set of measured correlation matrices. As a most welcome side effect …

model for random correlation matrices to model non-stationary effects. We then use the Merton model in which default events … ensemble of random matrices that models the truly existing set of measured correlation matrices. As a most welcome side effect …

paper, we exploit clustering techniques derived from Random Matrix Theory (RMT) to study a third, intermediate (mesoscopic … portfolios by filtering out both random and systemic comovements from the correlation matrix. Second, we redefine the portfolio …

Statistical inferences for sample correlation matrices are important in high dimensional data analysis. Motivated by … sample correlation matrices for the case where the dimension <I>p</I> and the sample size <I>n</I> are comparable. This … result is of independent interest in large dimensional random matrix theory. Meanwhile, we apply the linear spectral …

We propose a Kronecker product structure for large covariance or correlation matrices. One feature of this model is … asymptotic distributions of the estimators of the parameters of the spectral distribution of the Kronecker product correlation …