HMC defined their Policy Portfolio to correspond to their benchmark, according to the modern portfolio theory (Markowitz, 1952), whose goal is to minimize the variance for a given return. The main advantage of the optimal portfolio allocation lies in its ability to provide weights on how to invest a given amount of money based on a few inputs. Optimal portfolio allocation is easy to implement, yet it faces some issues and limitations. As discussed in class, the model assumes normality in the returns, since the optimization only depends on the mean and the variance. HMC team should however take into account that the distribution of returns is not normal and that there might be outliers issues. HMC partly takes these into account by controlling the risk for the aforementioned outliers using stress test (Exhibit 7). Another important matter to point out is that the model uses historical data as input, and these data might very well not be constant or accurate. Correlation may indeed change both over time and between classes of assets. However HMC examined short-term and long-term historical records and talked with investment management firms specialized in this type of analysis in order to get the most accurate data. Finally, HMC is doing well using the optimizer as a proxy for the investment decision. Optimizers may lead to completely different investment strategies if the inputs (mean, variance, correlation) are to be changed by a small amount. In a first step optimization, Meyer and his team found out they had to take substantial position in non-traditional asset classes. They therefore constrained the optimization in a second step, which led to a more realistic and implementable Policy Portfolio. How does HMC develop its capital market assumptions? Why does HMC focus on real returns? What do HMC’s capital market assumptions imply about the U.S. equity premium and foreign...

HMC defined their Policy Portfolio to correspond to their benchmark, according to the modern portfolio theory (Markowitz, 1952), whose goal is to minimize the variance for a given return. The main advantage of the optimal portfolio allocation lies in its ability to provide weights on how to invest a given amount of money based on a few inputs. Optimal portfolio allocation is easy to implement, yet it faces some issues and limitations. As discussed in class, the model assumes normality in the returns, since the optimization only depends on the mean and the variance. HMC team should however take into account that the distribution of returns is not normal and that there might be outliers issues. HMC partly takes these into account by controlling the risk for the aforementioned outliers using stress test (Exhibit 7). Another important matter to point out is that the model uses historical data as input, and these data might very well not be constant or accurate. Correlation may indeed change both over time and between classes of assets. However HMC examined short-term and long-term historical records and talked with investment management firms specialized in this type of analysis in order to get the most accurate data. Finally, HMC is doing well using the optimizer as a proxy for the investment decision. Optimizers may lead to completely different investment strategies if the inputs (mean, variance, correlation) are to be changed by a small amount. In a first step optimization, Meyer and his team found out they had to take substantial position in non-traditional asset classes. They therefore constrained the optimization in a second step, which led to a more realistic and implementable Policy Portfolio. How does HMC develop its capital market assumptions? Why does HMC focus on real returns? What do HMC’s capital market assumptions imply about the U.S. equity premium and foreign...