The primary goal of a Strategic Asset Allocation (SAA) is to create an asset mix which will provide an optimal balance between expected risk and return for a long-term investment horizon. SAA is often thought as a reference portfolio which will be tactically adjusted based on short-term market forecasts, following a process often called Tactical Asset Allocation (TAA). Many empirical studies support that SAA is the most important determinant of the total return and risk of a broadly diversified portfolio. As a matter of fact, long-term allocation needs long-term assumptions on assets risk/return characteristics as a key input. With 30 years in mind as a typical horizon, the issue of determining expected returns, volatilities and correlations for equity, bond, commodity and alternative asset classes is a complex task, faced by most institutions. Two main routes can be explored to address this issue.

The first one basically consists in stating that past history will repeat itself similarly and historical figures can serve as a reliable guide for the future. This method, sometimes referred as unconditional, determines expected returns based on historical returns, disregarding any world shocks or structural economic changes that could arise. As a result, this approach appears unsatisfactory and not adapted to the SAA problem.

A second route consists in relating long-run financial expected returns to long-run macroeconomic scenarios. The assumption here is that market prices do not differ, on the long term, from their so called fundamental value which is determined by the returns of physical assets. Hence, this fair value methodology consists in first, establishing a link between financial prices and economic fundamentals and second, determining the long-run value of those fundamentals.

This sixth white paper explores the second route. First, the long-run short rate is defined based upon macro-economic quantities such as the long-run inflation and real potential output growth. Using this long-run short rate as a reference numeraire, risk-premium of bonds, equities and some alternative asset classes are then successively derived. Eventually, using a typical mean-variance framework, numerical results are obtained.

Overall, the approach described herein provides an original line of thought to address allocation issues in a consistent set-up. The traditional segmentation between SAA and TAA, which could appear artificial, finds a clear justification. In the SAA step, allocation is built to adapt to the expected long-term stationary state of the economy. In turn, the TAA step allows for local fluctuations (business cycle) around this steady-state to be taken into account. We hope you will find this article both interesting and useful in practice.

Nicolas Gaussel
Publishing Director
PhD, Global Head of Quantitative Asset Management