This cumulative dissertation studies various approaches to improve stock market volatility forecasts based on nonlinearity and asymmetric dependence modeling as well as new innovative data sources. Studying multivariate dependence patterns using a vine copula approach and incorporating Google...

This paper proposes a dynamic framework for modeling and forecasting of realized covariance matrices using vine copulas to allow for more flexible dependencies between assets. Our model automatically guarantees positive definiteness of the forecast through the use of a Cholesky decomposition of...

In this paper we adapt the empirical similarity (ES) concept for the purpose of combining forecasts originating from different models. Our ES approach is suitable for situations where a decision maker refrains from evaluating success probabilities of forecasting models but prefers to think by...