Course offered within International Triple Degree
The Master of Science in Management - International Triple Degree (ITD) is a two-year management degree program and is offered jointly by Loyola University (New Orleans, USA), SKEMA Business School (Paris, France) and LMU Munich School of Management. INRIVER, together with KMF, offers the Financial Risk Management course for ITD students.
Financial Risk Management
- Lecturer (Lecture)
- Prof. Dr. Andreas Richter; Prof. Markus Glaser
- Language
- English
- Target group
- Master Management International Triple Degree (MBA, M.Sc.)
- Link
- Moodle
- Cycle
- Summer semester, May - July
- ECTS
- 6 ECTS
- Course format
- 5 x 4 hours lecture and 5 x 4 hours tutorial
- Textbook
- The class is based on the book "Corporate Finance“, Fifth Edition, Global Edition by Jonathan Berk and Peter DeMarzo (2020), which students can access free of charge via theUniversity library
- Grading
- Term paper + oral exam
Description
The course introduces the theoretical and empirical concepts of modern finance relevant to risk management (RM). The course aims to deepen the understanding of why risk management is beneficial by applying classical decision theory to investment and risk management problems. The course also looks at market risk, covering various risk and return measures as well as portfolio theory and common asset valuation techniques. In addition, concepts and instruments such as derivatives, hedging and insurance are discussed.
Aim and Objectives
After completing the course, students should be able to calculate and interpret different measures of risk and return. Furthermore, students will enhance their understanding of portfolio theory and asset pricing techniques. Besides, students should be able to evaluate different risk management techniques in terms of pricing, relevance and usage, which will also include being able to assess the insurability of certain risks. Finally, students will improve their skills in Excel and learn the fundamentals of how to analyze and visualize financial data.