I don’t quiet understand the idea of this model, how is it helping to generate the efficient frontier and reducing the inputs needed for froecasing from 125750 to simply 1502 inputs for a S&P500 index. Or do we just need to know them cold for the exam and plug and chug come exam day? 1. Expected return for Asset i = [ai] + [bi * E(Rm)] 2. Variance of Asset i = [bi^2 * (var of mkt)] + [(var of error term)] 3. Covariance between asset i and j = Cov ij = bi*bj*(var of mkt) 4. Parameters using market model = 3n + 2 to forecasts n expected return, n variances and n*(n-1)/2 covariances needed for efficient fronter generation Any inputs would be greatly appreciated.
WTF… Are you asking a question?