1. Basic problems in financial econometrics.
2. Simulation Monte-Carlo – random numbers generators.
3. Estimation methods – capabilities and restrictions.
4. Regression analysis – estimation of empirical arbitrage model.
5. Regression analysis – generalized linear models and applications.
6. Introduction to stochastic optimization – applications and solution techniques.
7. Application of principal component analysis.
8. Controlling of extremal losses – estimation of risk with low probabilities.
9. Introduction to Visual Basic for Application.
10. Application of given mixture probability distributions.
11. Application of stochastic processes.
12. Modelling volatility with asymmetric effect.
13. Modelling dependences, covariance matrices.
14. Introduction to data envelopment analysis (DEA).
2. Simulation Monte-Carlo – random numbers generators.
3. Estimation methods – capabilities and restrictions.
4. Regression analysis – estimation of empirical arbitrage model.
5. Regression analysis – generalized linear models and applications.
6. Introduction to stochastic optimization – applications and solution techniques.
7. Application of principal component analysis.
8. Controlling of extremal losses – estimation of risk with low probabilities.
9. Introduction to Visual Basic for Application.
10. Application of given mixture probability distributions.
11. Application of stochastic processes.
12. Modelling volatility with asymmetric effect.
13. Modelling dependences, covariance matrices.
14. Introduction to data envelopment analysis (DEA).