We propose a targeted and robust modeling of dependence in multivariate time series via dynamic networks, with time-varying predictors included to improve interpretation and prediction. The model is applied to financial markets, estimating effects of verbal and material cooperations
In this paper, predictions of future price movements of a major American stock index was made by ana...
We propose a new time-varying Generalized Dynamic Factor Model for high-dimensional, locally station...
Cross-correlation and mutual information based complex networks of the day-to-day returns of US S&P...
We propose a targeted and robust modeling of dependence in multivariate time series via dynamic netw...
This paper aims to investigate the dependence structure of global financial markets using an systema...
Many examples exist of multivariate time series where dependencies between variables change over tim...
Research projects in the area of multivariate financial time-series are of a particular interest for...
We discuss the development and application of dynamic graphical models for multivariate financial ti...
The accurate prediction of time-changing covariances is an important problem in the modeling of mult...
In this paper we propose a time-varying parameter framework to estimate the dynamic network of finan...
The dependence structure in multivariate financial time series is of great importance in portfolio m...
In complex systems, statistical dependencies between individual components are often considered one ...
After the 2008 financial crisis, researchers found it’s necessary to understand the financial market...
When modelling multivariate financial data, the problem of structural learning is compounded by the ...
This thesis investigates the modelling and forecasting of multivariate volatility and dependence in ...
In this paper, predictions of future price movements of a major American stock index was made by ana...
We propose a new time-varying Generalized Dynamic Factor Model for high-dimensional, locally station...
Cross-correlation and mutual information based complex networks of the day-to-day returns of US S&P...
We propose a targeted and robust modeling of dependence in multivariate time series via dynamic netw...
This paper aims to investigate the dependence structure of global financial markets using an systema...
Many examples exist of multivariate time series where dependencies between variables change over tim...
Research projects in the area of multivariate financial time-series are of a particular interest for...
We discuss the development and application of dynamic graphical models for multivariate financial ti...
The accurate prediction of time-changing covariances is an important problem in the modeling of mult...
In this paper we propose a time-varying parameter framework to estimate the dynamic network of finan...
The dependence structure in multivariate financial time series is of great importance in portfolio m...
In complex systems, statistical dependencies between individual components are often considered one ...
After the 2008 financial crisis, researchers found it’s necessary to understand the financial market...
When modelling multivariate financial data, the problem of structural learning is compounded by the ...
This thesis investigates the modelling and forecasting of multivariate volatility and dependence in ...
In this paper, predictions of future price movements of a major American stock index was made by ana...
We propose a new time-varying Generalized Dynamic Factor Model for high-dimensional, locally station...
Cross-correlation and mutual information based complex networks of the day-to-day returns of US S&P...
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