A0918
Title: Dynamic partial correlation models
Authors: Enzo DInnocenzo - VU University Amsterdam (Netherlands) [presenting]
Andre Lucas - VU University Amsterdam (Netherlands)
Abstract: A new nonlinear dynamic model is introduced for dynamic conditional correlation matrices. To generate correlation matrices that satisfy the constraints of positive (semi) definiteness and ones on the diagonal, we parameterize the correlation matrix using a sequence of partial correlations. Each partial correlation is built recursively from previous partial correlations and pairwise correlations using the so-called D-vine copula structure in a static framework for random correlation matrices. The main advantages of this strategy are that (i) it ensures positive definite correlation matrices with a sequence of simple transformations; (ii) the method is easily scalable to higher dimensions without losing computational stability (which off-sets it from other parameterizations); (iii) the recursive structure of the parametrization allows for much a much simplified asymptotic analysis of the process and the maximum likelihood estimator; (iv) the formulation allows us to easily impose (theoretical) restrictions such as zero restrictions on some of the partial correlations during the filtering stage. We provide conditions for stationarity, ergodicity and invertibility of our model and prove strong consistency and asymptotic normality of the maximum likelihood estimator. An extensive Monte Carlo simulation and an empirical in-sample and out-of-sample analysis of stock return data show that the new approach outperforms a range of recent alternatives.
