This preview shows page 1. Sign up to view the full content.
Unformatted text preview: ected returns and covariance matrix are
given. Obviously, in practice this is not the case and these need to be
estimated
o How do we estimate these?
o Can we use the historical sample returns to calculate the past average
returns and covariance matrix? What complicates the matter is that MV optimization is sensitive to the
inputs:
o That is, small changes in the expected returns and covariance matrix
may result in large changes in the resulting portfolio weights
o Any estimate is subject to estimation error (sampling error). If the
average estimation error is large the output from MV could be garbage
at best In this handout we will talk about how to estimate the expected returns
using the BlackLitterman model; we also briefly discuss robust optimization RISK AND PORTFOLIO MANAGEMENT WITH ECONOMETRICS, VER. 11/11/2012. © P. KOLM. 5 Impact of Estimation Error in MV Optimization Simple portfolio rules (e.g. equal weighting) often outperforms MV (Jobson
and Korkie (1981), Jorion (1985)) Portfolio optimizers are often “error maximizers” (Michaud (1998)) Optimal portfolios are not necessarily well diversified and result in “corner
solutions”(Green and Hollifield (1992)) Estimation errors in returns are one order of magnitude more important
than the estimation errors in the covariance matrix (Kallberg and Ziemba
(1984), Best and Grauer (1991; (1991), Chopra and Ziemba (1993)) RIS...
View Full
Document
 Fall '14

Click to edit the document details