approach is that it allows the investor to blend asset-specific views of each asset's expected return with views that would be consistent with market weights in a market equilibrium model.
Whereas some asset allocators employ advanced techniques such as the Black-Litterman approach to reduce the sensitivity of the weights to the expected risks and returns, a much larger number of asset allocators choose to add additional constraints to the optimization model to circumvent the difficulties and sensitivities of mean-variance optimization. Common additional constraints include the following:
The last constraint, limits on portfolio weights, is the most popular. These constraints can prescribe upper or lower weights, outside of which the asset allocator will not invest. The portfolio optimizer is forced to generate weights within those ranges. In practice, however, many investors use so many constraints that the constraints have more influence on the final asset allocation than does the mean-variance optimization process. While each of the added constraints may help the asset allocator avoid extreme weights, the approach may ultimately lead to having the constraints define the allocation rather than the goal of diversification.
1.9 Conclusion
This chapter has introduced the asset allocation process, with a focus on using the mean-variance approach to create optimal portfolios. The asset allocation process discussed in this chapter consists of five steps, of which four were discussed.
Step 1 focuses on understanding who the asset owners are and their mission in managing assets. Step 2 examines the asset owner's objectives and constraints. Here we discussed the expected utility and its mean-variance version as a flexible way of quantifying an asset owner's objectives. Two types of constraints, internal and external, were explained.
Step 3 deals with preparing the investment policy statement, which will provide a general framework for the actual asset allocation. One of the key features of this statement is to develop a list of asset classes to be considered. Step 4 is implementation, which was covered with a focus on mean-variance optimization and its potential problems.
References
Ang, A. 2014. Asset Management: A Systematic Approach to Factor Investing. Oxford: Oxford University Press.
Brinson, G. P., L. R. Hood, and G. L. Beebower. 1986. “Determinants of Portfolio Performance.” Financial Analysts Journal 42 (4): 39–44.
Cox, J., J. Ingersoll, and S. A. Ross. 1985. “An Intertemporal General Equilibrium Model of Asset Prices.” Econometrica 53 (2): 363–84.
Eychenne, K., S. Martinetti, and T. Roncalli. 2011. “Strategic Asset Allocation.” Lyxor Asset Management, Paris.
Eychenne, K., and T. Roncalli. 2011. “Strategic Asset Allocation – An Update Following the Sovereign Debt Crisis.” Lyxor Asset Management, Paris.
Ibbotson, R. G., and P. D. Kaplan. 2000. “Does Asset Allocation Policy Explain 40, 90, or 100 Percent of Performance?” Financial Analysts Journal 56 (1): 26–33.
Maginn, J. L., D. L. Tuttle, J. E. Pinto, and D. W. McLeavey. 2007. Managing Investment Portfolios. 3rd ed. Hoboken, NJ: John Wiley & Sons.
CHAPTER 2
Tactical Asset Allocation, Mean-Variance Extensions, Risk Budgeting, Risk Parity, and Factor Investing
Chapter 1 discussed the asset allocation process, strategic asset allocation, and the basic mean-variance approach. The role of the investment policy statement was examined as a way of summarizing the objectives and constraints of asset owners, and strategic asset allocation was presented as a long-term optimal allocation. Next, the basic properties of the mean-variance approach were examined as a quantitative method for creating optimal portfolios that are consistent with asset owners' objectives. This chapter begins with a discussion of the methodology behind tactical asset allocation (TAA), and then studies some practical extensions to the mean-variance model that address some of the problems raised at the end of Chapter 1. In particular, it examines how illiquidity, risk factor exposures, and estimation risks can be taken into account when creating optimal portfolios. This chapter then discusses alternatives to the basic mean-variance optimization. It discusses risk budgeting as a way of understanding and controlling the risk exposures of a portfolio; it also examines the risk parity approach, which is closely related to risk budgeting. Finally, it presents a relatively new approach called factor investing, which recommends an optimal risk allocation rather than an optimal asset allocation as the proper way of creating optimal portfolios.
2.1 Tactical Asset Allocation
Tactical asset allocation has a long history and has been used by large and small asset owners.8 However, there is no well-established definition of TAA, and different authors and investment firms use the term to mean different things. One thing that all uses of TAA seem to have in common is that it represents a form of active management of a portfolio. Tactical asset allocation (TAA) is defined as an active strategy that shifts capital to those asset classes that are expected to offer the most attractive risk-return combination over a short- to medium-term time horizon. In this sense, TAA is a dynamic asset allocation strategy that actively adjusts a portfolio's strategic asset allocation (SAA) based on short- to medium-term changes in the economic and financial environment. TAA will add value if it can systematically take advantage of temporary market inefficiencies and departures of asset prices from their fundamental values. As discussed in Chapter 1, over time, SAA allocation is the most important driver of a portfolio's risk-return characteristics. TAA can add value if (1) there are short- to medium-term inefficiencies in some markets, and (2) a systematic approach can be designed to exploit these inefficiencies while overcoming the risks and costs that are associated with active portfolio management.9
2.1.1 TAA and the Fundamental Law of Active Management
Chapter 20 of the CAIA Level I book discussed the Fundamental Law of Active Management (FLOAM). This model expresses the risk-adjusted value added by an active portfolio manager as a function of the manager's skill to forecast asset returns and the number of markets to which the manager's skill can be applied (breadth). In particular, we saw that
where IR is the information ratio and is equal to the ratio of the manager's alpha (i.e., expected outperformance) divided by the volatility of the alpha. IC is the information coefficient of the manager, which is a measure of the manager's skill, and represents the correlation between the manager's forecast of asset returns and the actual returns to those assets. BR is the strategy's breadth, which is defined as the number of independent forecasts that the manager can skillfully make during a given period of time (e.g., one year). Not surprisingly, the value added by active management increases with the ability of the manager to forecast returns and the number of independent markets to which the forecasting skill can be applied.
The FLOAM can be applied to security selection as well as asset class allocation. Clearly, when FLOAM is applied to the process of selecting securities from a universe of 5,000 or more, there should be greater potential for adding value, as the breadth could be large. This insight has been used as an argument against TAA. In other words, to add value through active asset allocation, a portfolio manager will need a much higher level of skill if that skill is to be applied to only a handful of independent asset classes.
APPLICATION 2.1.1
Suppose active manager A has the skill to select stocks from a universe of 2,000 securities