for volatility, correlation and covariance using high frequency financial data. It also implements complementary …

modelling that enables stochastic structural change in model parameters and on model estimation by Bayesian or non …-parametric kernel methods. In the context of the estimation of covariance matrices of large dimensional panels, such data requires … applicable in econometric analysis beyond estimation of large covariance matrices. We discuss the utility of the robust …

into intraday covariance and correlation dynamics. We show that intraday (co-)variations (i) follow underlying periodicity …

into intraday covariance and correlation dynamics. We show that intraday (co-)variations (i) follow underlying periodicity …

The estimation of the covariances of high-frequency asset prices is problematic because of asynchronous trading and …

novel statistical methods have been introduced to address large volatility matrix estimation problems from a high … Huber loss function with a diverging threshold to develop a robust realized volatility estimation. We show that it has the … the proposed estimation methods …

estimation of the covariances if traditional methods for low-frequency data are employed. We propose to model intraday log … microstructure noise is taken into account, (iii) estimation is performed by standard maximum likelihood. Our empirical analysis …

We construct a parsimonious test of constancy of the correlation matrix in the multivariate conditional correlation … deterministically as a function of time. The alternative is a covariance matrix, not a correlation matrix, so the test may be viewed as … a general test of stability of a constant correlation matrix. The size of the test in finite samples is studied by …