367 research outputs found
A partial correlation vine based approach for modeling and forecasting multivariate volatility time-series
A novel approach for dynamic modeling and forecasting of realized covariance
matrices is proposed. Realized variances and realized correlation matrices are
jointly estimated. The one-to-one relationship between a positive definite
correlation matrix and its associated set of partial correlations corresponding
to any vine specification is used for data transformation. The model components
therefore are realized variances as well as realized standard and partial
correlations corresponding to a daily log-return series. As such, they have a
clear practical interpretation. A method to select a regular vine structure,
which allows for parsimonious time-series and dependence modeling of the model
components, is introduced. Being algebraically independent the latter do not
underlie any algebraic constraint. The proposed model approach is outlined in
detail and motivated along with a real data example on six highly liquid
stocks. The forecasting performance is evaluated both with respect to
statistical precision and in the context of portfolio optimization. Comparisons
with Cholesky decomposition based benchmark models support the excellent
prediction ability of the proposed model approach
- …