06/07/2019
Approaches for asset allocation
1. The Mean – Variance Optimization (MVO) approach
A significant drawback to generating an efficient frontier through traditional mean-variance optimization methods is the sensitivity of the frontier to changes in the inputs.
The input themselves (e.g., expected return, covariance) are estimates. Reliance on an efficient frontier developed through a traditional, single mean-variance optimization is questionable.
2. Resampled Efficient Frontier (REF)
– Michaud developed a simulation approach utilizing historical mean, variances, and covariances of asset classes, which combined with capital market forecasts, assumes they are fair representations of their expectations. His resampling technique is bases on a Monte Carlo simulation that draws from the distributions to develop a simulated efficient frontier.
– The simulation is run thousands of times, the efficient portfolio at each return level, and hence the resulting efficient frontier is the result of an averaging process.
– Rather than a single, sharp curve, the resampled efficient frontier is a blur. At each level of return is a simulated efficient portfolio at the center with a distribution of portfolios above and below it.
– The asset mix at any point on the resampled efficient frontier is an average of many portfolios thay might have been constructed to meet that return.
– By utilizing this resampled technique, a portfolio manager is able to judge the need for rebalancing.
Advantages:
– It utilizes an averaging process and generates an efficient frontier that is more stable than a traditional mean-variance efficient frontier. Small changes in the inputs variables result in only minor changes in the REF.
– Portfolios generated through this process tend to be better diversified.
– By comparing any asset mix of an existing portfolio to the range of asset mixed across the multiple portfolios on the REF that could have generated the required return, it is possible to see if the current mix is within the boundaries of what is acceptable.
Disadvatages:
Ther is no theoretical reasoning to support the contention that a portfolio constructed through resampling should be superior relative to another constructed through traditional mean-variance analysis.
In addition, like MVO, the inputs are often based on historical data that could lack current relativance.
3. Black – Litteman
With the same motivation as Michaud (resampling), Black Litterman developed two modes for dealing with the problem associated with estimation error, especially expected return:
– The unconstrained Black-Littlerman model (UBL)
– The Black-Litterman model (BL)
The assigned reading focuses primarily on BL (i.e., constrained for no short selling)
1. The Mean – Variance Optimization (MVO) approach A significant drawback to generating an efficient frontier through traditional mean-variance optimization methods is the sensitivity of the frontier to changes in the inputs. The input themselves (e.g., expected return, covariance) are estimates. ...
