Dynamic Factor Models. Improvements and applications

High-dimensional financial data are characterised by panels of heterogeneous time series, in order to deal with such a complex panels I adopted infinite dimensional Dynamic Factor Models (DFM) to extract volatilities and Bayesian non-parametrics techniques to estimate the parameters of a Stochastic Volatility model. The non-parametric estimation is realised ad an infinite mixture of normals and the combination of such specification with DFM seems to be an original element of this work. The applied exercises worked imply the use of S&P500 daily data spanning over 12 years, the approach returned results showing good adherence of the forecasted volatility and forecasted returns with actual realisations, overcoming existent approaches and offering an effective tool to deal with large, high-frequency datasets. A second application of this approach is in the field of macroeconomics, in which Structural DFM was applied to opena market sector in order to validate the theoretical model