We will discuss three types of problems frequently encountered in financial applications. First, we will deal with classification problems (solvent/insolvent bank client) using such tools as logistic regression, regression trees, neural networks. Second, we will apply different dimension reduction techniques such as factor analysis, cluster analysis, etc. to real estate or financial data. Finally, we will work out several forecasting techniques for large financial datasets aimed to obtain “optimal” asset allocation strategies.
Classification; dimension reduction; forecasting
R, background in regression analysis
Dr. Yarema Okhrin
Professor of Statistics at the University of Augsburg
Affiliation: University of Augsburg, Germany
After my PhD and PostDoc in Statistics and Econometrics at the European University Viadrina, Germany I spent two years as Assitant Professor in Econometrics at the University of Bern, Switzerland. Since 2010 I have been holding the chair of Statistics at the University of Augsburg, Germany.
Fields of interests: financial econometrics, multivariate and high-dimensional statistical analysis, dependence modelling, environmental statistics.