3804372: Asset allocation and performance analysis

Description

Lectures by Prof. Weissensteiner:

• Portfolio Performance Evaluation

• Multi-factor models (e.g. Fama-French Three Factor model)

• 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

• Exchange Rate Risks in Asset Management (International Diversification, FX Risks of Stocks and Bonds and Management Approaches, Role of the Reference Currency)
• Special Topics in Fund Management (Performance Attribution, Performance Persistence, Value of Analyst Recommendations, Volatility as an Asset Class, Sukuk Investments)
• Fund Markets and Regulation (Relationship between Performance and Regulation, Role of Domiciles, UCITS & AIFM)

• Carry Trades

• Debate on active vs. passive management

• Cointegration Analysis

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.

• 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