This course offers a practically oriented introduction into regression modeling methods. The basic concepts and some mathematical background are included, with the emphasis lying in learning "good practice" that can be applied in every student's own projects and daily work life. A special focus will be laid in the use of the statistical software package R for regression analysis.

The course starts with an introduction to time series analysis (examples, goal, mathematical notation). In the following, descriptive techniques, modeling and prediction as well as advanced topics will be covered.

Introduction to time series analysis: examples, goals and mathematical notation. Descriptive techniques, modelling and prediction.

More advanced topics in time series analysis like time series regression, time series classification and spectral analysis.

Linear Algebra:linear systems, vector calculus, matrix calculus, linear maps, orthogonal maps, trace & determinant, eigenvalues & eigenvectors, vector spacesstochastics:combinatorics, probability, probability densities, statistics

Distributions for financial data. Volatility models: ARCH- and GARCH models. Value at risk and expected shortfall. Portfolio theory: minimum-variance portfolio, efficient frontier, Sharpe’s ratio. Factor models: capital asset pricing model, macroeconomic factor models, fundamental factor model. Copulas: Basic theory, Gaussian and t-copulas, archimedean copulas, calibration of copulas.