3404372: Asset allocation and performance analysis

Description

Lectures by Prof. Gantenbein:

• Strategic Asset Allocation
• Yield and Correlation Structures in Asset Management
• Dynamic and Tactical Asset Allocation
• Active Portfolio Management, Factor Models and Style Analysis
• Asset Allocation and Investment Horizon
• Asset Class Comprehensive Risk Measurement
• Performance Measurement and Output Evaluation
• Discussion of the Methods of Individual Selected Journal Contributions
• Term Structure and Asset Allocation
• Real Economy and Financial Markets
• Currency Regimes and International Diversification Exchange Rate Risks in Asset Management Protection Management

• Portfolio Performance Evaluation
• > Conventional Risk measures based on CAPM: Sharpe measure, Treynor’s measure, Tracking Error, Jensen’s measure, Information Ratio, Sortino Ratio
• > Multi-factor models (e.g. Fama-French Three Factor model)
• >> Timing analysis (Treynor and Mazuy model) and
• >> Style analysis
• > New risk measures: Value-at-Risk (VaR), Conditional-Value-at-Risk (CVaR)
• Resampling to address parameter uncertainty
• Black – Litterman model for active portfolio management
• Time-horizon effects in portfolio management
• Planning and sequential decision making under uncertainty
• Merton Framework

• Modelling in GAMS
• Scenario representation and scenario optimization
• Modelling the Mean Absolute Deviation model with practical constraints
• Modelling VaR and CVaR
• Implementation of a multistage stochastic program

Learning Outcomes

Students master a wide range of procedures and methods of making asset allocation decisions in practical cases. Thus they are able to critically evaluate the various procedures and apply them on a case-by-case basis in asset allocation. They exhibit context-oriented originality and creativity in the application of their knowledge and they are familiar with the various approaches in asset management discussed in class and make informed judgements with their application in situations with incomplete information.

Students know how to calculate the different performance measures on a real data set. They are familiar with the Bayesian approach in the Black-Litterman model and how to use it for practical asset allocation decisions. They exhibit context-oriented originality and creativity in the application of their knowledge. Candidates understand the fundamental difference between one-period and multi-period decision problems (“hedging demands”) and how to model real-life optimization task (with cash in-and outflows, human capital, restrictions on the asset allocation etc.).

Students who have followed the course will be able to formulate and solve optimization problems in GAMS in particular within the following areas:

• Measuring and managing return and risk trade offs
• Adding practical constraints to financial optimization problems
• Modelling Value at Risk (VaR) and Conditional Value at Risk
• Modelling a multistage stochastic program

Literature

Required reading:

• Bernanke B.S. (2004). Conducting monetary policy at very low short-term interest rates. BIS Review, pp. 1-5
• Bodie, Z. /Kane, A. /Marcus, A.J. (2009). Investments (Eights Edition). Boston: Mc Graw-Hill.
• Elton, E.J./ Gruber, M.J./ Brown, S.J./ Goetzmann, W.N. (2007). Modern Portfolio Theory and Investment Analysis (Seventh edition). New York: John Wiley.
• Michaud R.O./ Michaud R.O. 1998: Efficient Asset Management: A Practical Guide to Stock Portfolio Optimization and Asset Allocation, Oxford University Press.
• Black, F./ Litterman, R. (1992). Global Portfolio Optimization, Financial Analysts Journal, pp. 28–43.
• The GAMS tutorial: http://www.gams.com/dd/docs/gams/Tutorial.pdf

• Zenios, S.A. (2008) Practical Financial Optimization: Decision Making for Financial Engineers.
• Campbell, J.Y./ Viceira, L.M. (2002). Strategic Asset Allocation: Portfolio Choice for Long-term Investors. Oxford University Press.
• Francis J.C./ Ibbotson R. (2002). Investments – A Global Perspective, Pearson.
• Amenc N./ Le Sourd V. (2003). Portfolio Theory and Performance Analysis, John Wiley.
• Spremann K. (2008). Portfoliomanagement, Oldenbourg.

Materials

Lecture slides will be available on Moodle

Comments

The students are required to have installed GAMS (www.gams.com) on their computers and have gone through the GAMS tutorial (www.gams.com/dd/docs/gams/Tutorial.pdf) and implemented and run the example in the tutorial on their own before the start of the course. It will be a clear advantage to have windows machines with Microsoft Excel on them, since we will be writing the output of the models directly to EXCEL from GAMS and analyzing them in EXCEL.