This module provides a thorough introduction into Quantitative Finance and Risk Management. Topics covered include:
- Market Risk including concepts such as VaR, Expected Shortfall and Stress Testing
- Credit Risk including Logistic Regression (Credit Scoring Models) and portfolio approaches to credit risk that include rating migration risk (CreditMetrics Model)
- Valuation theory as it relates to interest rate, equity and credit derivatives. Models covered include the Black-Scholes, Binomial as well as more advanced models such as Hull-White.
- Machine Learning methods and their applications in Finance including k-Means, KNN, Neural Networks and Kalman Filters.
Grading: Students will work together in groups on a subject of their choice from any of the sections outlined above. Grading will be based on a term paper that the group will submit by August 15th (50%) and a group presentation that will take place in the first week of August.
- Dozent/in: Thomas Zellerer