, the winner would most likely be the correlation coefficient with a significant difference from its first competitor. In …. Therefore, we search for robust correlation coefficients to nonnormality and outliers that could be applied to all applications … and detect influenced or hidden correlations not recognized by the most popular correlation coefficients. We introduce a …

approach than the usual ML approach. It is applicable to multiple -equation models with K -dimensional error correlation …

approach than the usual ML approach. It is applicable to multiple-equation models with K-dimensional error correlation matrices …

We develop a Bayesian approach for parsimoniously estimating the correlation structure of the errors in a multivariate … stochastic volatility model. Since the number of parameters in the joint correlation matrix of the return and volatility errors … is potentially very large, we impose a prior that allows the off-diagonal elements of the inverse of the correlation …

-frequency intraday returns. It disentangles covariance estimation into variance and correlation components. This allows to estimate …

The Gaussian rank correlation equals the usual correlation coefficient computed from the normal scores of the data … correlation matrix based on the Gaussian rank correlation is always positive semidefinite, and very easy to compute, also in high … the popular Kendall and Spearman correlation measures. In the empirical application, we show how it can be used for …