We consider the problem of ex-ante forecasting conditional correlation patterns using ultra high frequency data. Flexible semiparametric predictors referring to the class of dynamic panel and dynamic factor models are adopted for daily forecasts. The parsimonious set up of our approach allows to...

We consider the problem of ex-ante forecasting conditional correlation patterns using ultra high frequency data. Flexible semiparametric predictors referring to the class of dynamic panel and dynamic factor models are adopted for daily forecasts. The parsimonious set up of our approach allows to...

The present thesis comprises two rather independent chapters. In general, the diagnosis and quantification of dependence is a major aim of econometric studies. Along these lines, the concept of dependence serves as an encompassing framework to analyze time series with two very different...

In this paper we model the dynamics of 100 years long monthly price series of eight non-ferrous and precious metals. Applying the state space framework we impose and identify two common factors related to non-ferrous and precious metals, respectively, which exhibit quite distinct autoregressive...

In this paper we investigate exponential smoothing (ES) predictors for the weights of high-dimensional realized global minimum variance portfolios (GMVP) which only depend on the realized covariance matrix of risky financial assets. We compare direct ES predictions of realized GMVP proportions...

The popular conditional autoregressive Wishart (CAW) model for dynamics of realized covariance matrices provides a flexible parametrisation. However, the number of parameters grows quadratically with the number of assets, which causes enormous computational difficulties in higher dimensions....