Délka:

2 dny

2 dny

Místo:

Praha, hotel NH Prague

Praha, hotel NH Prague

- Traditional Asset Allocation Models and their Shortcomings
- Introducing a Bayesian Approach to Asset Allocation
- The Black-Litterman Model and the Intuitions Behind It
- Using Reverse Optimization to Calculate Equilibrium Returns
- Incorporating Absolute and Relative Market Views
- Controlling Tracking Error and Market Exposure
- Maximizing Expected Returns under Risk Constraints
- Using the B-L Model in a Real Life Investment Setting

The objectives of this workshop are to give you an in-depth introduction to the Black-Litterman asset allocation model and a practical, hands-on understanding of how you can implement and use this model to determine optimal portfolio allocations for specific classes of assets in a manner consistent with investors' market views.

We start with a general introduction to the model and explain how this model overcomes some of the severe weaknesses of the traditional, Markowitz mean-variance optimization approach. Further, we introduce the Black-Litterman formula and explain the intuition behind it and discus the advantages and disadvantages of using this model.

We then proceed with a number of workshops, giving step-by-step instructions for the practitioner to combine market equilibrium expected returns with "investor views" to generate new vectors of expected returns.

Following an overview of the investment process, participants will learn how to set the key parameters in the Black-Litterman framework. This involves a discussion of how the model is used to observe the equilibrium returns in global capital markets and then blend the equilibrium returns with "our" own views to provide a set of expected returns. We explain and demonstrate how we determine the weight and confidence levels on our own views relative to the equilibrium.

We next turn to looking at risk control and optimization. We describe the process of assessing and controlling tracking error risk and Market Exposure (a statistical measure of a portfolio's sensitivity to market moves), and we explain and demonstrate how optimal portfolios can be constructed under risk (budget) constraints.

We conclude with a complete, "real life" asset allocation exercise. We assess and discuss how the allocation has performed when applied to historical data and how it will be expected to perform in the current, low-yield, low-returns environment.

We start with a general introduction to the model and explain how this model overcomes some of the severe weaknesses of the traditional, Markowitz mean-variance optimization approach. Further, we introduce the Black-Litterman formula and explain the intuition behind it and discus the advantages and disadvantages of using this model.

We then proceed with a number of workshops, giving step-by-step instructions for the practitioner to combine market equilibrium expected returns with "investor views" to generate new vectors of expected returns.

Following an overview of the investment process, participants will learn how to set the key parameters in the Black-Litterman framework. This involves a discussion of how the model is used to observe the equilibrium returns in global capital markets and then blend the equilibrium returns with "our" own views to provide a set of expected returns. We explain and demonstrate how we determine the weight and confidence levels on our own views relative to the equilibrium.

We next turn to looking at risk control and optimization. We describe the process of assessing and controlling tracking error risk and Market Exposure (a statistical measure of a portfolio's sensitivity to market moves), and we explain and demonstrate how optimal portfolios can be constructed under risk (budget) constraints.

We conclude with a complete, "real life" asset allocation exercise. We assess and discuss how the allocation has performed when applied to historical data and how it will be expected to perform in the current, low-yield, low-returns environment.

Microsoft®, Excel®, Visual Basic® a VBA® jsou registrovanými ochrannými známkami společnosti MICROSOFT.

- The Problems with the "Traditional" Markowitz Models
- Concentrated portfolios
- Input-sensitivity
- Estimation error maximization

- Introducing a Baysian Approach to Asset Allocation
- The Intuition behind the Model
- Presenting and Explaining The Black-Litterman Formula

- Equilibrium Returns as Neutral Reference Point
- The Risk Aversion Coefficient
- Using "Reverse Optimization" to Extract Vector of Implied Excess Returns
- An Example Using Real-Life Data
- Hands-On Workshop

- What Is a "View"
- Types of Views in the B-L Model
- Absolute views
- Relative views

- The View Vector and the Asset Matrix
- Moving from the Stated Views to the Inputs Used in the B-L Model
- Calculating the Views Covariance Matrix
- Determining the Weightings of the Views
- Interpreting and Choosing the Scaling Scalar Tau
- Hands-On Workshop

- Important Risk Measures in the B-L Model
- Tracking error
- Market exposure

- Setting Target Risk Levels
- Target risk levels when views on market direction are neutral
- Target risk levels with strong views on market directions

- Hands-on Workshop

- Finding Portfolio that Maximize Expected Returns under Risk Constraints
- Securing that risk exposures correspond to positions where risk-taking is desired
- Imposing upper bounds on the risk from any position
- Ensuring that tracking error is evenly balanced

- Hands-on Workshop

- Measuring the Efficacy of the Model by Comparing to Unconstrained Optimization
- Determining whether Risk Control Parameters Work Effectively
- Hands-on Workshop

- Client Profiling and Determination of Client's Risk-Return Objectives
- Constructing an Optimal Portfolio under a Risk Budget Constraint
- Communication of Model Outputs to the Clients

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