DYNAMIC CONDITIONAL CORRELATION MODELS FOR REALIZED COVARIANCE MATRICES (Preliminary and incomplete version) Luc Bauwens 1 , Giuseppe Storti 2 and Francesco Violante 3 August 24, 2012 Abstract New dynamic models for realized covariance matrices are proposed. The expected value of the realized covariance matrix is specified in two steps: a model for each realized variance, and a model for the realized correlation matrix. The realized correlation model is a dynamic conditional correlation model. Estimation can be done in two steps as well, and a QML interpretation is given to each step, by assuming a Wishart conditional distribution. Moreover, the model is applicable to large matrices since estimation can be done by the composite likelihood method. Keywords: Realized covariance; dynamic conditional correlations; covariance tar- geting; Wishart distribution; composite likelihood. JEL Classification: C32, C58. 1 Universit´ e catholique de Louvain, CORE, B-1348 Louvain-La-Neuve. E-mail: luc.bauwens@uclouvain.be 2 Department of Economics and Statistics, Universiti . Salerno, Fisciano, Italy. E-mail: storti@unisa.it 3 Department of Quantitative Economics, Maastricht University, The Netherlands. E-mail: f.violante@maastrichtuniversity.nl L. Bauwens thanks the University of Sassari for its hospitality during a visit while working on this paper. Research supported by the contract ”Projet d’Actions de Recherche Concert´ ees” 07/12-002 of the ”Com- munaut´ e francaise de Belgique”, granted by the ”Acad´ emie universitaire Louvain”. The scientific respon- sibility is assumed by the authors.