Effective Portfolio Optimization Techniques

For this highly interactive workshop, all delegates are strongly recommended to attend the workshop with a laptop computer loaded with Microsoft Excel with Visual Basic and Excel Solver Add-ins. There will be several interactive group sessions to work on real-life cases.

Overview of portfolio optimization

• What is portfolio optimization

• Asset allocation considerations and objectives

• Overview of approaches to portfolio optimization

• Data issues and considerations

• Learning Outcome: Attendees will gain a detailed understanding of portfolio optimization technology, its role as an asset allocation tool, and the data issues associated with its implementation

Introduction to mean-variance optimization

• What is mean-variance optimization

• Defining the inputs to MV optimization

• Determining an investor’s risk aversion

• Solving a MV optimization problem

• Constructing the efficient frontier and analyzing the output of MV

• Key features and criticisms of MV optimization

• Learning Outcome: Attendees will gain a detailed understanding of Markowitz’s mean variance optimization; the general framework, interpretation, analysis of results, strengths and weaknesses

INTERACTIVE: Getting started with matrix arithmetic and optimization in Excel

• Review of Excel matrix arithmetic functions

• Review of the Excel Solver for optimization problems

• Learning Outcome: By demonstrating how to use Excel matrix arithmetic and optimization functions, delegates will gain an understanding the basic tools needed to implement and extend the MV optimization framework

INTERACTIVE: Mean-variance optimization in Excel + CASE STUDIES

• Delegates will be provided with Excel files with macros to perform MV optimization and analyze output

• Case studies will be considered to demonstrate features and extremes of MV optimization

• Learning Outcome: By working with an Excel implementation of the MV framework and stepping through several case studies, delegates will deepen their understanding of the methodology and have a tool to apply their knowledge after returning from the course

Adaptations and extensions to MV optimization

• Accounting for skewness and kurtosis of asset return distributions

• Michaud’s criticism of MV optimization and the resampled frontier

• Black-Litterman approach

• Dynamic rebalancing/accounting for transaction costs

• Learning Outcome: Attendees will learn about important, practical extensions of the mean-variance optimization and how they address shortcomings

INTERACTIVE: Extensions to MV optimization in Excel + CASE STUDIES

• Delegates will be provided with Excel files to perform MV extensions

• Case studies will be considered to demonstrate features of variations of MV optimization

• Case studies to compare asset allocations produced by different approaches

• Learning Outcome: By working with an Excel implementation of the MV framework and stepping through several case studies, candidates will deepen their understanding of resampling and the Black-Litterman approach and have a tool to apply their knowledge after returning from the course

Portfolio optimization beyond mean-variance

• Dynamic investment problems

• Transaction costs

• Accounting for skewness and kurtosis of asset return distributions

• Asset liability management problems

• Learning Outcome: Attendees will examine the latest techniques in using stochastic programming to address non-normal return distributions and transaction costs and to generate dynamic asset allocation strategies

 

Dr. Christopher Donohue is Managing Director of the GARP Research Center and the GARP Digital Library and is a member of the FRM committee. He is responsible for managing the GARP Digital Library, developing new resources for GARP, including risk management workshops, and conducting and supporting research in financial risk management areas.

Previously, Chris was a Partner at a hedge fund where he was responsible for the development of risk management and asset allocation tools for pension funds. Prior to that, Chris was a Director in the Global Research Center at Deutsche Bank where he led product research and development in the areas of risk management, asset liability management, and asset allocation. He also worked as Director of Optimization Technology at Alphatech, a leading edge technology and research defense contractor, where he led algorithm development for intelligence aircraft path planning and sensor scheduling systems.

Chris has published several articles on portfolio management and optimization. He has a BA in Mathematics from Hamilton College and a Ph.D. in Operations Research from the University of Michigan.

New York (Jersey City): October 28, 2008

 

GARP Individual, Student & Fellow Member Rates

Register before September 18	Register after September 18
$900	$950

 

Affiliate & Non-Member Rates

Register before September 18	Register after September 18
$950	$1,000

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London: October 30, 2008

 

GARP Individual, Student & Fellow Member Rates

Register before September 18	Register after September 18
£630	£665

 

Affiliate & Non-Member Rates

Register before September 18	Register after September 18
£665	£700