The goal of these simulations is to understand the mathematics of mean-variance optimization and the balance of risk pricing, if all investors use this rule with common information set. A simulation focuses on five to 10 years of monthly sector returns, the first drawn from a known multivariate normal distribution. Mean-variance optimization is designed (ie, the highest Sharpe ratio) produce the highest ratio of excess return to portfolio portfolio standard deviation in this sett … Read more »

The goal of these simulations is to understand the mathematics of mean-variance optimization and the balance of risk pricing, if all investors use this rule with common information set. A simulation focuses on five to 10 years of monthly sector returns, the first drawn from a known multivariate normal distribution. Mean-variance optimization is designed (ie, the highest Sharpe ratio) to produce the highest ratio of excess return to portfolio portfolio standard deviation in this setting. Simulation B changes by setting allowing students to determine expected returns through a simultaneous auction. We will continue to have agreement on the covariance matrix, and implicitly expected payments, but it allows the student to set the market price. The average portfolio weights for the 10 sectors is calculated and used as a vector of market capitalization weights. With these market weights (w) and the given covariance matrix implies the Capital Asset Pricing Model (CAPM), the expected returns for each sector are calculated and compared with the student set expected returns.
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from
Erik Stafford,
Joshua D Coval,
Rodrigo Osmo,
Zack Page,
John Jernigan,
Paulo Passoni
3 pages.
Publication Date: Nov 15, 2007. Prod #: 208086-PDF-ENG
Asset Allocation I HBR case solution