• Students understand the essentials of mathematical decision theory as well as the psychological aspects of market participant behaviour.
• They critically reflect the concepts for performance and risk measurement which are used to support decision taking in financial markets.
• The module provides a necessary basis for the elective Module “Advanced Risk Management”.
• Students are able to implement financial models for the valuation and analysis of fixed-income instruments (net present value, yield-to-maturity, duration, modified duration, convexity) in Excel and R. They know how to interpret the results of their calculations.
• Students are able to simulate the results of CAPM (with given model parameters) in R. They are able to interpret the results for the minimum variance portfolio and the optimal market portfolio. They are aware of the fact that the major challenge in real life is the dynamic estimation of model parameters, which they will learn to implement in R (based upon real market data) in the elective module M6.2 “Advanced Risk Management”
• This module M1 provides (in connection with M4 Financial Economics, Financial Institutions and Monetary Policy) a necessary basis for “Advanced Risk Management”
• It can be used as stand-alone-module within any program with an advanced focus on financial markets

• Introduction to (Mathematical) Decision Theory
• Modelling Risk and Uncertainty
• Term Structure of Interest Rates, Forward Contracts and Futures
• Fixed-Income Securities I: Duration
• Fixed-Income Securities II: Convexity
• Mean-Variance Portfolio Theory
• Market Equilibrium I: CAPM
• Market Equilibrium II: Arbitrage Pricing Theory
• Modelling Equity, Debt, Currency and Commodity Markets
• Introduction to Option Pricing
• Structured Products
• Markets and Psychology: Brief Introduction to Behavioural Finance

• Hansson, S., Decision Theory – A Brief Introduction, http://www.infra.kth.se/~soh/decisiontheory.pdf
• Hansson, S., Fallacies of Risk, http://www.infra.kth.se/~soh/fallaciesofrisk.pdf
• Hansson, S., Philosophical Perspectives on Risk, http://www.infra.kth.se/~soh/PhilPerspRisk-text.pdf
• Copeland, T., Weston, J., Shastri, K., Financial Theory and Corporate Policy, 4th ed., Amsterdam 2013
• Hull, J., Options, Futures and Other Derivatives, 8th edition, Toronto 2011
• Additional learning material (scientific papers, newspaper articles, corporate publications) will be provided in Moodle
• Clifford S. Ang, Analyzing Financial Data and Implementing Financial Models Using R, Springer 2015 (available as e-Book in HSA’s library)

6 ECTS x 30 hours = 180 hours,  combined out of the following:

• Course attendance: 6 weeks * 4.5 hours =27 hours
• Preparation / homework / self-study : 12 weeks * 3 hours = 36 hours + 20 hours for DataCamp Courses
• Time for exercises and group work: 12 weeks * 2 hours = 24 hours
• Semester project / presentation: 40 hours for scientific work and 9 hours for presentations
• Exam preparation:24 hours
• Exam time: 120 minutes

• „Seminaristischer Unterricht“ (Lecture with integrated practical problems)
• The lecture is supplemented by
  • questions for discussion
  • practical problems
  • case studies which are either solved as teamwork in class or assigned as homework problems using statistical tools such as MS Excel, MATLAB or R
• It is expected that students make use of the online learning tracks offered free of charge on https://www.datacamp.com. Assignments are the online courses (including online exercises)
          o Introduction to R for Finance
          o Intermediate R for Finance
          o Bond Valuation and Analysis in R
  Completion of DataCamp courses is compensated with bonus points for the exam. Students are encouraged to complete further DataCamp courses for the DataCamp Study tracks “Finance Basics in R” and “Applied Finance in R”.
• Every student has to work on a semester project covering a historic case study on “Misbehavior of Markets versus Misbehavior of Market Participants”. The topic is assigned in the first classroom session. Depending on group size, topics may be assigned to groups of two. The semester project consists of a scientific paper and a professional presentation.