We introduce a novel parametrization of the correlation matrix. The reparametrization
facilitates modeling of correlation and covariance matrices by an unrestricted vector, where …

We consider the well-studied problem of predicting the timevarying covariance matrix of a
vector of financial returns. Popular methods range from simple predictors like rolling window …

We propose new asymmetric multivariate volatility models. The models exploit estimates of
variances and covariances based on the signs of high-frequency returns, measures known …

We propose a novel class of multivariate GARCH models that utilize realized measures of
volatilities and correlations. The central component is an unconstrained vector …

In predicting conditional covariance matrices of financial portfolios, practitioners are required
to choose among several alternative options, facing a number of different sources of …

This chapter provides a survey of various multivariate GARCH specifications that model the
temporal dependence in the second moment of multivariate return series processes. The …

An estimator of the ex-post covariation of log-prices under asynchronicity and microstructure
noise is proposed. It uses the Cholesky factorization of the covariance matrix in order to …

We introduce an approximate dynamic factor model for modeling and forecasting large
panels of realized volatilities. Since the model is estimated by means of principal …

We forecast the realized covariance matrix of asset returns in the US equity market by
exploiting the predictive information of graphs in volatility and correlation. Specifically, we …

We propose a class of score-driven realized covariance models where volatilities and
correlations are separately estimated. We can thus combine univariate realized volatility …