We develop a new model for the multivariate covariance matrix dynamics based on daily return observations and daily realized covariance matrix kernels based on intraday data. Both types of data may be fat-tailed. We account for this by assuming a matrix-F distribution for the realized kernels,...

We develop a new model for the multivariate covariance matrix dynamics based on daily return observations and daily realized covariance matrix kernels based on intraday data. Both types of data may be fat-tailed. We account for this by assuming a matrix-F distribution for the realized kernels,...

The purpose of this research paper is to reveal which European countries are most suitable for EU membership using the multivariate method discriminant analysis. Discriminant analysis is useful for building a model for separation of group membership based on observed characteristics of each...

For symmetric random matrices with correlated entries, which are functions of independent random variables, we show that the asymptotic behavior of the empirical eigenvalue distribution can be obtained by analyzing a Gaussian matrix with the same covariance structure. This class contains both...

We consider a type of normalized sample covariance matrix without independence in columns, and derive the limiting spectral distribution when the number of variables p and the sample size n satisfy that p→∞, n→∞, and p/n→0. This result is a supplement to the corresponding result under...