Addressing Criticisms of and Using MVO

Despite its limitations, MVO is commonly used as a foundation for other methods in practice. The following approaches aim to address and improve upon some of the common criticisms associated with MVO. 

Reverse Optimization

Reverse optimization involves a different approach compared to traditional MVO. Instead of starting with expected asset metrics to determine asset allocation, we begin with the optimal asset allocation based on the global market portfolio. From there, we calculate the expected return and risk of the portfolio.

The global market portfolio is a theoretical concept. It consists of all investable assets worldwide, weighted according to their market values. While it provides a reference for a diversified asset allocation, it is not practically achievable for individual investors. Nevertheless, individual investors can use the global market portfolio as a starting point and subsequently adjust the asset mix to suit their specific needs and preferences, taking factors such as risk tolerance and investment objectives into account. This customization allows the portfolio to align with individual clients’ requirements, even if it deviates from the optimal global market portfolio.

One of the notable advantages of this method is its ability to prevent highly concentrated positions, which can occasionally occur with MVO. For instance, although the return and risk metrics may align with the investor’s requirements, an MVO allocation could result in an allocation where the majority of the portfolio (90% or more) is invested in real estate, with minimal exposure to other asset classes. This allocation clearly falls short of meeting the principles of common-sense diversification.

The Black-Litterman Model is an extension of reverse optimization but with the added feature of including investor expectations for asset class returns, volatility, and correlations. As resampled allocations are derived, the process is repeated, and the allocations are again updated to include forecasts, allowing for some degree of active management in the portfolio allocation process.

Adding Constraints Beyond Budget Constraints

Traditional mean-variance optimization (MVO) techniques may result in concentrated positions that fail to meet investor preferences, despite aligning with modern portfolio theory. In preventing these undesirable outcomes, investors can add additional constraints to their portfolios and further guide the MVO process down the path to an optimal and acceptable portfolio.

Additional constraints in portfolio management can be applied to control various aspects of asset allocation. These constraints may include:

• Specifying a fixed weight for a particular asset or asset class.
• Managing the size of an allocation relative to another allocation within the portfolio.
• Setting boundaries or corridors for the weights of different assets in the portfolio.
• Imposing upper limits on allocations to address liquidity considerations.
• Incorporating hedging characteristics to align with specific liability-related requirements.

Resampled MVO

Like reverse optimization, resampled MVO starts with a best-guess estimate of returns, risk, and correlations between assets to generate an efficient frontier. This first output is then tested via Monte Carlo simulations to determine the acceptability of outcomes. Analysts will typically pick the average outcome from the simulations to derive the efficient frontier and ultimately select an optimal asset allocation.

Question

An analyst is developing an asset allocation strategy for a high-net-worth individual. Initially, the analyst considers the global market portfolio as a starting point. After calculating the initial allocation, the analyst decides to decrease the allocation to equity by 5%. Additionally, observing an inverted yield curve, the analyst anticipates a potential underperformance of equity in the coming months. To address this, the analyst increases the allocation to short-term fixed income by an additional 5%.

The above explanation most likely describes:

1. Resampling.
2. Reverse optimization.
3. Black-Litterman approach.

Solution

The correct answer is C:

The passage correctly describes the Black-Litterman framework. The Black-Litterman model is an extension of reverse optimization but with the added feature of including investor expectations for asset class returns, volatility, and correlations. As resampled allocations are derived, the process is repeated, and the allocations are again updated to include forecasts, allowing for some degree of active management in the portfolio allocation process.

A is incorrect. Resampling is similar to the Black-Litterman framework but uses best guess estimates rather than the global market portfolio and explicitly uses Monte Carlo analysis to test its assumptions.

Reading 6: Principles of Asset Allocation

Los 6 (i) Recommend and justify an asset allocation based on the global market portfolio